.github/workflows/build-everything-tgw.yml

@@ -41,13 +41,6 @@ jobs:
       version: ${{ inputs.version }}
       config: 'os:ubuntu-22.04'
 
-  build_release_rocm_windows:
-    name: ROCm Windows
-    uses: ./.github/workflows/build-portable-release-rocm.yml
-    with:
-      version: ${{ inputs.version }}
-      config: 'os:windows-2022'
-
   build_release_rocm_linux:
     name: ROCm Linux
     uses: ./.github/workflows/build-portable-release-rocm.yml
@@ -74,32 +67,4 @@ jobs:
     uses: ./.github/workflows/build-portable-release.yml
     with:
       version: ${{ inputs.version }}
-      config: 'os:macos-15-intel,macos-14'
-
-  build_release_ik_cuda_windows:
-    name: ik CUDA Windows
-    uses: ./.github/workflows/build-portable-release-ik-cuda.yml
-    with:
-      version: ${{ inputs.version }}
-      config: 'os:windows-2022'
-
-  build_release_ik_cuda_linux:
-    name: ik CUDA Linux
-    uses: ./.github/workflows/build-portable-release-ik-cuda.yml
-    with:
-      version: ${{ inputs.version }}
-      config: 'os:ubuntu-22.04'
-
-  build_release_ik_cpu_windows:
-    name: ik CPU Windows
-    uses: ./.github/workflows/build-portable-release-ik.yml
-    with:
-      version: ${{ inputs.version }}
-      config: 'os:windows-2022'
-
-  build_release_ik_cpu_linux:
-    name: ik CPU Linux
-    uses: ./.github/workflows/build-portable-release-ik.yml
-    with:
-      version: ${{ inputs.version }}
-      config: 'os:ubuntu-22.04'
+      config: 'os:macos-13,macos-14'

.github/workflows/build-portable-release-cuda.yml

@@ -106,7 +106,7 @@ jobs:
             cd "text-generation-webui-${VERSION_CLEAN}"
 
             # Remove extensions that need additional requirements
-            allowed=("character_bias" "gallery" "sd_api_pictures")
+            allowed=("character_bias" "gallery" "openai" "sd_api_pictures")
             find extensions/ -mindepth 1 -maxdepth 1 -type d | grep -v -E "$(printf '%s|' "${allowed[@]}" | sed 's/|$//')" | xargs rm -rf
 
             # Define common variables
@@ -116,13 +116,13 @@ jobs:
             # 1. Set platform-specific variables
             if [[ "$RUNNER_OS" == "Windows" ]]; then
                 PLATFORM="windows"
-                PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-pc-windows-msvc-install_only_stripped.tar.gz"
+                PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-pc-windows-msvc-install_only.tar.gz"
                 PIP_PATH="portable_env/python.exe -m pip"
                 PACKAGES_PATH="portable_env/Lib/site-packages"
                 rm start_linux.sh start_macos.sh
             else
                 PLATFORM="linux"
-                PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-unknown-linux-gnu-install_only_stripped.tar.gz"
+                PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-unknown-linux-gnu-install_only.tar.gz"
                 PIP_PATH="portable_env/bin/python -m pip"
                 PACKAGES_PATH="portable_env/lib/python3.13/site-packages"
                 rm start_macos.sh start_windows.bat
@@ -150,16 +150,15 @@ jobs:
             # 5. Clean up
             rm -rf .git cmd* update_wizard* Colab-TextGen-GPU.ipynb docker setup.cfg .github .gitignore requirements/ one_click.py
 
-            # 6. Create archive
+            # 6. Create ZIP file
             cd ..
+            ZIP_NAME="textgen-portable-${VERSION_CLEAN}-${PLATFORM}-cuda${CUDA_VERSION}.zip"
+            echo "Creating archive: $ZIP_NAME"
+
             if [[ "$RUNNER_OS" == "Windows" ]]; then
-                ARCHIVE_NAME="textgen-portable-${VERSION_CLEAN}-${PLATFORM}-cuda${CUDA_VERSION}.zip"
-                echo "Creating archive: $ARCHIVE_NAME"
-                powershell -Command "Compress-Archive -Path text-generation-webui-${VERSION_CLEAN} -DestinationPath $ARCHIVE_NAME"
+                powershell -Command "Compress-Archive -Path text-generation-webui-${VERSION_CLEAN} -DestinationPath $ZIP_NAME"
             else
-                ARCHIVE_NAME="textgen-portable-${VERSION_CLEAN}-${PLATFORM}-cuda${CUDA_VERSION}.tar.gz"
-                echo "Creating archive: $ARCHIVE_NAME"
-                tar czf "$ARCHIVE_NAME" "text-generation-webui-${VERSION_CLEAN}"
+                zip -r "$ZIP_NAME" "text-generation-webui-${VERSION_CLEAN}"
             fi
 
       - name: Upload files to a GitHub release
@@ -168,7 +167,7 @@ jobs:
         continue-on-error: true
         with:
           repo_token: ${{ secrets.GITHUB_TOKEN }}
-          file: ../textgen-portable-*
+          file: ../textgen-portable-*.zip
           tag: ${{ inputs.version }}
           file_glob: true
           make_latest: false

.github/workflows/build-portable-release-ik-cuda.yml

@@ -1,178 +0,0 @@
-name: Build ik CUDA
-
-on:
-  workflow_dispatch:
-    inputs:
-      version:
-        description: 'Version tag of text-generation-webui to build: v3.0'
-        default: 'v3.0'
-        required: true
-        type: string
-      config:
-        description: 'Override configurations to build: key1:item1-1,item1-2;key2:item2-1,item2-2'
-        default: 'Default'
-        required: false
-        type: string
-      exclude:
-        description: 'Exclude build configurations: key1-1:item1-1,key1-2:item1-2;key2-1:item2-1,key2-2:item2-2'
-        default: 'None'
-        required: false
-        type: string
-  workflow_call:
-    inputs:
-      version:
-        description: 'Version tag of text-generation-webui to build: v3.0'
-        default: 'v3.0'
-        required: true
-        type: string
-      config:
-        description: 'Configurations to build: key1:item1-1,item1-2;key2:item2-1,item2-2'
-        default: 'Default'
-        required: false
-        type: string
-      exclude:
-        description: 'Exclude build configurations: key1-1:item1-1,key1-2:item1-2;key2-1:item2-1,key2-2:item2-2'
-        default: 'None'
-        required: false
-        type: string
-
-permissions:
-  contents: write
-
-jobs:
-  define_matrix:
-    name: Define Build Matrix
-    runs-on: ubuntu-latest
-    outputs:
-      matrix: ${{ steps.set-matrix.outputs.matrix }}
-    defaults:
-      run:
-        shell: pwsh
-    env:
-      CONFIGIN: ${{ inputs.config }}
-      EXCLUDEIN: ${{ inputs.exclude }}
-
-    steps:
-      - name: Define Job Output
-        id: set-matrix
-        run: |
-          $matrix = @{
-              'os' = @('ubuntu-22.04', 'windows-2022')
-              'pyver' = @("3.13")
-              'cuda' = @("12.4", "13.1")
-          }
-
-          if ($env:CONFIGIN -ne 'Default') {$env:CONFIGIN.split(';').foreach({$matrix[$_.split(':')[0]] = $_.split(':')[1].split(',')})}
-
-          if ($env:EXCLUDEIN -ne 'None') {
-              $exclusions = @()
-              $exclusions += $env:EXCLUDEIN.split(';').replace(':','=').replace(',',"`n") | ConvertFrom-StringData
-              $matrix['exclude'] = $exclusions
-          }
-
-          $matrixOut = ConvertTo-Json $matrix -Compress
-          Write-Output ('matrix=' + $matrixOut) >> $env:GITHUB_OUTPUT
-
-  build_wheels:
-    name: ${{ matrix.os }} ${{ matrix.pyver }} CUDA ${{ matrix.cuda }}
-    needs: define_matrix
-    runs-on: ${{ matrix.os }}
-    strategy:
-      matrix: ${{ fromJSON(needs.define_matrix.outputs.matrix) }}
-    defaults:
-      run:
-        shell: pwsh
-    env:
-      PCKGVER: ${{ inputs.version }}
-
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          repository: 'oobabooga/text-generation-webui'
-          ref: ${{ inputs.version }}
-          submodules: 'recursive'
-
-      - uses: actions/setup-python@v6
-        with:
-          python-version: ${{ matrix.pyver }}
-
-      - name: Build Package
-        shell: bash
-        run: |
-            VERSION_CLEAN="${{ inputs.version }}"
-            VERSION_CLEAN="${VERSION_CLEAN#v}"
-            cd ..
-            cp -r text-generation-webui "text-generation-webui-ik-${VERSION_CLEAN}"
-            cd "text-generation-webui-ik-${VERSION_CLEAN}"
-
-            # Remove extensions that need additional requirements
-            allowed=("character_bias" "gallery" "sd_api_pictures")
-            find extensions/ -mindepth 1 -maxdepth 1 -type d | grep -v -E "$(printf '%s|' "${allowed[@]}" | sed 's/|$//')" | xargs rm -rf
-
-            # Define common variables
-            CUDA_VERSION="${{ matrix.cuda }}"
-            VERSION="${{ inputs.version }}"
-
-            # 1. Set platform-specific variables
-            if [[ "$RUNNER_OS" == "Windows" ]]; then
-                PLATFORM="windows"
-                PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-pc-windows-msvc-install_only_stripped.tar.gz"
-                PIP_PATH="portable_env/python.exe -m pip"
-                PACKAGES_PATH="portable_env/Lib/site-packages"
-                rm start_linux.sh start_macos.sh
-            else
-                PLATFORM="linux"
-                PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-unknown-linux-gnu-install_only_stripped.tar.gz"
-                PIP_PATH="portable_env/bin/python -m pip"
-                PACKAGES_PATH="portable_env/lib/python3.13/site-packages"
-                rm start_macos.sh start_windows.bat
-            fi
-
-            # 2. Download and extract Python
-            cd ..
-            echo "Downloading Python for $PLATFORM..."
-            curl -L -o python-build.tar.gz "$PYTHON_URL"
-            tar -xzf python-build.tar.gz
-            mv python "text-generation-webui-ik-${VERSION_CLEAN}/portable_env"
-
-            # 3. Prepare requirements file based on CUDA version
-            cd "text-generation-webui-ik-${VERSION_CLEAN}"
-            if [[ "$CUDA_VERSION" == "13.1" ]]; then
-                REQ_FILE="requirements/portable/requirements_ik_cuda131.txt"
-            else
-                REQ_FILE="requirements/portable/requirements_ik.txt"
-            fi
-
-            # 4. Inject --ik into start scripts
-            sed -i 's/--portable/--portable --ik/g' start_linux.sh start_windows.bat 2>/dev/null || true
-
-            # 5. Install packages
-            echo "Installing Python packages from $REQ_FILE..."
-            $PIP_PATH install --target="./$PACKAGES_PATH" -r "$REQ_FILE"
-
-            # 6. Clean up
-            rm -rf .git cmd* update_wizard* Colab-TextGen-GPU.ipynb docker setup.cfg .github .gitignore requirements/ one_click.py
-
-            # 7. Create archive
-            cd ..
-            if [[ "$RUNNER_OS" == "Windows" ]]; then
-                ARCHIVE_NAME="textgen-portable-ik-${VERSION_CLEAN}-${PLATFORM}-cuda${CUDA_VERSION}.zip"
-                echo "Creating archive: $ARCHIVE_NAME"
-                powershell -Command "Compress-Archive -Path text-generation-webui-ik-${VERSION_CLEAN} -DestinationPath $ARCHIVE_NAME"
-            else
-                ARCHIVE_NAME="textgen-portable-ik-${VERSION_CLEAN}-${PLATFORM}-cuda${CUDA_VERSION}.tar.gz"
-                echo "Creating archive: $ARCHIVE_NAME"
-                tar czf "$ARCHIVE_NAME" "text-generation-webui-ik-${VERSION_CLEAN}"
-            fi
-
-      - name: Upload files to a GitHub release
-        id: upload-release
-        uses: svenstaro/upload-release-action@2.7.0
-        continue-on-error: true
-        with:
-          repo_token: ${{ secrets.GITHUB_TOKEN }}
-          file: ../textgen-portable-ik-*
-          tag: ${{ inputs.version }}
-          file_glob: true
-          make_latest: false
-          overwrite: true

.github/workflows/build-portable-release-ik.yml

@@ -1,173 +0,0 @@
-name: Build ik CPU
-
-on:
-  workflow_dispatch:
-    inputs:
-      version:
-        description: 'Version tag of text-generation-webui to build: v3.0'
-        default: 'v3.0'
-        required: true
-        type: string
-      config:
-        description: 'Override configurations to build: key1:item1-1,item1-2;key2:item2-1,item2-2'
-        default: 'Default'
-        required: false
-        type: string
-      exclude:
-        description: 'Exclude build configurations: key1-1:item1-1,key1-2:item1-2;key2-1:item2-1,key2-2:item2-2'
-        default: 'None'
-        required: false
-        type: string
-  workflow_call:
-    inputs:
-      version:
-        description: 'Version tag of text-generation-webui to build: v3.0'
-        default: 'v3.0'
-        required: true
-        type: string
-      config:
-        description: 'Configurations to build: key1:item1-1,item1-2;key2:item2-1,item2-2'
-        default: 'Default'
-        required: false
-        type: string
-      exclude:
-        description: 'Exclude build configurations: key1-1:item1-1,key1-2:item1-2;key2-1:item2-1,key2-2:item2-2'
-        default: 'None'
-        required: false
-        type: string
-
-permissions:
-  contents: write
-
-jobs:
-  define_matrix:
-    name: Define Build Matrix
-    runs-on: ubuntu-latest
-    outputs:
-      matrix: ${{ steps.set-matrix.outputs.matrix }}
-    defaults:
-      run:
-        shell: pwsh
-    env:
-      CONFIGIN: ${{ inputs.config }}
-      EXCLUDEIN: ${{ inputs.exclude }}
-
-    steps:
-      - name: Define Job Output
-        id: set-matrix
-        run: |
-          $matrix = @{
-              'os' = @('ubuntu-22.04', 'windows-2022')
-              'pyver' = @("3.13")
-          }
-
-          if ($env:CONFIGIN -ne 'Default') {$env:CONFIGIN.split(';').foreach({$matrix[$_.split(':')[0]] = $_.split(':')[1].split(',')})}
-
-          if ($env:EXCLUDEIN -ne 'None') {
-              $exclusions = @()
-              $exclusions += $env:EXCLUDEIN.split(';').replace(':','=').replace(',',"`n") | ConvertFrom-StringData
-              $matrix['exclude'] = $exclusions
-          }
-
-          $matrixOut = ConvertTo-Json $matrix -Compress
-          Write-Output ('matrix=' + $matrixOut) >> $env:GITHUB_OUTPUT
-
-  build_wheels:
-    name: ${{ matrix.os }} ${{ matrix.pyver }}
-    needs: define_matrix
-    runs-on: ${{ matrix.os }}
-    strategy:
-      matrix: ${{ fromJSON(needs.define_matrix.outputs.matrix) }}
-    defaults:
-      run:
-        shell: pwsh
-    env:
-      PCKGVER: ${{ inputs.version }}
-
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          repository: 'oobabooga/text-generation-webui'
-          ref: ${{ inputs.version }}
-          submodules: 'recursive'
-
-      - uses: actions/setup-python@v6
-        with:
-          python-version: ${{ matrix.pyver }}
-
-      - name: Build Package
-        shell: bash
-        run: |
-            VERSION_CLEAN="${{ inputs.version }}"
-            VERSION_CLEAN="${VERSION_CLEAN#v}"
-            cd ..
-            cp -r text-generation-webui "text-generation-webui-ik-${VERSION_CLEAN}"
-            cd "text-generation-webui-ik-${VERSION_CLEAN}"
-
-            # Remove extensions that need additional requirements
-            allowed=("character_bias" "gallery" "sd_api_pictures")
-            find extensions/ -mindepth 1 -maxdepth 1 -type d | grep -v -E "$(printf '%s|' "${allowed[@]}" | sed 's/|$//')" | xargs rm -rf
-
-            # Define common variables
-            VERSION="${{ inputs.version }}"
-
-            # 1. Set platform-specific variables
-            if [[ "$RUNNER_OS" == "Windows" ]]; then
-                PLATFORM="windows-cpu"
-                PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-pc-windows-msvc-install_only_stripped.tar.gz"
-                PIP_PATH="portable_env/python.exe -m pip"
-                PACKAGES_PATH="portable_env/Lib/site-packages"
-                rm start_linux.sh start_macos.sh
-            else
-                PLATFORM="linux-cpu"
-                PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-unknown-linux-gnu-install_only_stripped.tar.gz"
-                PIP_PATH="portable_env/bin/python -m pip"
-                PACKAGES_PATH="portable_env/lib/python3.13/site-packages"
-                rm start_macos.sh start_windows.bat
-            fi
-
-            # 2. Download and extract Python
-            echo "Downloading Python for $PLATFORM..."
-            cd ..
-            curl -L -o python-build.tar.gz "$PYTHON_URL"
-            tar -xzf python-build.tar.gz
-            mv python "text-generation-webui-ik-${VERSION_CLEAN}/portable_env"
-
-            # 3. Prepare requirements file
-            cd "text-generation-webui-ik-${VERSION_CLEAN}"
-            REQ_FILE="requirements/portable/requirements_ik_cpu_only.txt"
-            echo "Using requirements file: $REQ_FILE"
-
-            # 4. Inject --ik into start scripts
-            sed -i 's/--portable/--portable --ik/g' start_linux.sh start_windows.bat 2>/dev/null || true
-
-            # 5. Install packages
-            echo "Installing Python packages from $REQ_FILE..."
-            $PIP_PATH install --target="./$PACKAGES_PATH" -r "$REQ_FILE"
-
-            # 6. Clean up
-            rm -rf .git cmd* update_wizard* Colab-TextGen-GPU.ipynb docker setup.cfg .github .gitignore requirements/ one_click.py
-
-            # 7. Create archive
-            cd ..
-            if [[ "$RUNNER_OS" == "Windows" ]]; then
-                ARCHIVE_NAME="textgen-portable-ik-${VERSION_CLEAN}-${PLATFORM}.zip"
-                echo "Creating archive: $ARCHIVE_NAME"
-                powershell -Command "Compress-Archive -Path text-generation-webui-ik-${VERSION_CLEAN} -DestinationPath $ARCHIVE_NAME"
-            else
-                ARCHIVE_NAME="textgen-portable-ik-${VERSION_CLEAN}-${PLATFORM}.tar.gz"
-                echo "Creating archive: $ARCHIVE_NAME"
-                tar czf "$ARCHIVE_NAME" "text-generation-webui-ik-${VERSION_CLEAN}"
-            fi
-
-      - name: Upload files to a GitHub release
-        id: upload-release
-        uses: svenstaro/upload-release-action@2.7.0
-        continue-on-error: true
-        with:
-          repo_token: ${{ secrets.GITHUB_TOKEN }}
-          file: ../textgen-portable-ik-*
-          tag: ${{ inputs.version }}
-          file_glob: true
-          make_latest: false
-          overwrite: true

.github/workflows/build-portable-release-rocm.yml

@@ -105,7 +105,7 @@ jobs:
             cd "text-generation-webui-${VERSION_CLEAN}"
 
             # Remove extensions that need additional requirements
-            allowed=("character_bias" "gallery" "sd_api_pictures")
+            allowed=("character_bias" "gallery" "openai" "sd_api_pictures")
             find extensions/ -mindepth 1 -maxdepth 1 -type d | grep -v -E "$(printf '%s|' "${allowed[@]}" | sed 's/|$//')" | xargs rm -rf
 
             # Define common variables
@@ -114,13 +114,13 @@ jobs:
             # 1. Set platform-specific variables
             if [[ "$RUNNER_OS" == "Windows" ]]; then
                 PLATFORM="windows"
-                PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-pc-windows-msvc-install_only_stripped.tar.gz"
+                PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-pc-windows-msvc-install_only.tar.gz"
                 PIP_PATH="portable_env/python.exe -m pip"
                 PACKAGES_PATH="portable_env/Lib/site-packages"
                 rm start_linux.sh start_macos.sh
             else
                 PLATFORM="linux"
-                PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-unknown-linux-gnu-install_only_stripped.tar.gz"
+                PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-unknown-linux-gnu-install_only.tar.gz"
                 PIP_PATH="portable_env/bin/python -m pip"
                 PACKAGES_PATH="portable_env/lib/python3.13/site-packages"
                 rm start_macos.sh start_windows.bat
@@ -145,16 +145,15 @@ jobs:
             # 5. Clean up
             rm -rf .git cmd* update_wizard* Colab-TextGen-GPU.ipynb docker setup.cfg .github .gitignore requirements/ one_click.py
 
-            # 6. Create archive
+            # 6. Create ZIP file
             cd ..
+            ZIP_NAME="textgen-portable-${VERSION_CLEAN}-${PLATFORM}-rocm.zip"
+            echo "Creating archive: $ZIP_NAME"
+
             if [[ "$RUNNER_OS" == "Windows" ]]; then
-                ARCHIVE_NAME="textgen-portable-${VERSION_CLEAN}-${PLATFORM}-rocm7.2.zip"
-                echo "Creating archive: $ARCHIVE_NAME"
-                powershell -Command "Compress-Archive -Path text-generation-webui-${VERSION_CLEAN} -DestinationPath $ARCHIVE_NAME"
+                powershell -Command "Compress-Archive -Path text-generation-webui-${VERSION_CLEAN} -DestinationPath $ZIP_NAME"
             else
-                ARCHIVE_NAME="textgen-portable-${VERSION_CLEAN}-${PLATFORM}-rocm7.2.tar.gz"
-                echo "Creating archive: $ARCHIVE_NAME"
-                tar czf "$ARCHIVE_NAME" "text-generation-webui-${VERSION_CLEAN}"
+                zip -r "$ZIP_NAME" "text-generation-webui-${VERSION_CLEAN}"
             fi
 
       - name: Upload files to a GitHub release
@@ -163,7 +162,7 @@ jobs:
         continue-on-error: true
         with:
           repo_token: ${{ secrets.GITHUB_TOKEN }}
-          file: ../textgen-portable-*
+          file: ../textgen-portable-*.zip
           tag: ${{ inputs.version }}
           file_glob: true
           make_latest: false

.github/workflows/build-portable-release-vulkan.yml

@@ -105,7 +105,7 @@ jobs:
             cd "text-generation-webui-${VERSION_CLEAN}"
 
             # Remove extensions that need additional requirements
-            allowed=("character_bias" "gallery" "sd_api_pictures")
+            allowed=("character_bias" "gallery" "openai" "sd_api_pictures")
             find extensions/ -mindepth 1 -maxdepth 1 -type d | grep -v -E "$(printf '%s|' "${allowed[@]}" | sed 's/|$//')" | xargs rm -rf
 
             # Define common variables
@@ -114,13 +114,13 @@ jobs:
             # 1. Set platform-specific variables
             if [[ "$RUNNER_OS" == "Windows" ]]; then
                 PLATFORM="windows"
-                PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-pc-windows-msvc-install_only_stripped.tar.gz"
+                PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-pc-windows-msvc-install_only.tar.gz"
                 PIP_PATH="portable_env/python.exe -m pip"
                 PACKAGES_PATH="portable_env/Lib/site-packages"
                 rm start_linux.sh start_macos.sh
             else
                 PLATFORM="linux"
-                PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-unknown-linux-gnu-install_only_stripped.tar.gz"
+                PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-unknown-linux-gnu-install_only.tar.gz"
                 PIP_PATH="portable_env/bin/python -m pip"
                 PACKAGES_PATH="portable_env/lib/python3.13/site-packages"
                 rm start_macos.sh start_windows.bat
@@ -145,16 +145,15 @@ jobs:
             # 5. Clean up
             rm -rf .git cmd* update_wizard* Colab-TextGen-GPU.ipynb docker setup.cfg .github .gitignore requirements/ one_click.py
 
-            # 6. Create archive
+            # 6. Create ZIP file
             cd ..
+            ZIP_NAME="textgen-portable-${VERSION_CLEAN}-${PLATFORM}-vulkan.zip"
+            echo "Creating archive: $ZIP_NAME"
+
             if [[ "$RUNNER_OS" == "Windows" ]]; then
-                ARCHIVE_NAME="textgen-portable-${VERSION_CLEAN}-${PLATFORM}-vulkan.zip"
-                echo "Creating archive: $ARCHIVE_NAME"
-                powershell -Command "Compress-Archive -Path text-generation-webui-${VERSION_CLEAN} -DestinationPath $ARCHIVE_NAME"
+                powershell -Command "Compress-Archive -Path text-generation-webui-${VERSION_CLEAN} -DestinationPath $ZIP_NAME"
             else
-                ARCHIVE_NAME="textgen-portable-${VERSION_CLEAN}-${PLATFORM}-vulkan.tar.gz"
-                echo "Creating archive: $ARCHIVE_NAME"
-                tar czf "$ARCHIVE_NAME" "text-generation-webui-${VERSION_CLEAN}"
+                zip -r "$ZIP_NAME" "text-generation-webui-${VERSION_CLEAN}"
             fi
 
       - name: Upload files to a GitHub release
@@ -163,7 +162,7 @@ jobs:
         continue-on-error: true
         with:
           repo_token: ${{ secrets.GITHUB_TOKEN }}
-          file: ../textgen-portable-*
+          file: ../textgen-portable-*.zip
           tag: ${{ inputs.version }}
           file_glob: true
           make_latest: false

.github/workflows/build-portable-release.yml

@@ -105,7 +105,7 @@ jobs:
             cd "text-generation-webui-${VERSION_CLEAN}"
 
             # Remove extensions that need additional requirements
-            allowed=("character_bias" "gallery" "sd_api_pictures")
+            allowed=("character_bias" "gallery" "openai" "sd_api_pictures")
             find extensions/ -mindepth 1 -maxdepth 1 -type d | grep -v -E "$(printf '%s|' "${allowed[@]}" | sed 's/|$//')" | xargs rm -rf
 
             # Define common variables
@@ -115,18 +115,18 @@ jobs:
             # 1. Set platform-specific variables
             if [[ "$RUNNER_OS" == "Windows" ]]; then
                 PLATFORM="windows-cpu"
-                PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-pc-windows-msvc-install_only_stripped.tar.gz"
+                PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-pc-windows-msvc-install_only.tar.gz"
                 PIP_PATH="portable_env/python.exe -m pip"
                 PACKAGES_PATH="portable_env/Lib/site-packages"
                 rm start_linux.sh start_macos.sh
             elif [[ "$RUNNER_OS" == "macOS" ]]; then
-                if [[ "$OS_TYPE" == "macos-15-intel" ]]; then
+                if [[ "$OS_TYPE" == "macos-13" ]]; then
                     PLATFORM="macos-x86_64"
-                    PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-apple-darwin-install_only_stripped.tar.gz"
+                    PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-apple-darwin-install_only.tar.gz"
                     REQ_TYPE="apple_intel"
                 else
                     PLATFORM="macos-arm64"
-                    PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-aarch64-apple-darwin-install_only_stripped.tar.gz"
+                    PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-aarch64-apple-darwin-install_only.tar.gz"
                     REQ_TYPE="apple_silicon"
                 fi
                 PIP_PATH="portable_env/bin/python -m pip"
@@ -135,7 +135,7 @@ jobs:
             else
                 # Linux case
                 PLATFORM="linux-cpu"
-                PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-unknown-linux-gnu-install_only_stripped.tar.gz"
+                PYTHON_URL="https://github.com/astral-sh/python-build-standalone/releases/download/20260303/cpython-3.13.12+20260303-x86_64-unknown-linux-gnu-install_only.tar.gz"
                 PIP_PATH="portable_env/bin/python -m pip"
                 PACKAGES_PATH="portable_env/lib/python3.13/site-packages"
                 rm start_macos.sh start_windows.bat
@@ -153,7 +153,7 @@ jobs:
 
             # Select requirements file based on platform
             if [[ "$RUNNER_OS" == "macOS" ]]; then
-                if [[ "$OS_TYPE" == "macos-15-intel" ]]; then
+                if [[ "$OS_TYPE" == "macos-13" ]]; then
                     REQ_FILE="requirements/portable/requirements_apple_intel.txt"
                 else
                     REQ_FILE="requirements/portable/requirements_apple_silicon.txt"
@@ -171,16 +171,15 @@ jobs:
             # 5. Clean up
             rm -rf .git cmd* update_wizard* Colab-TextGen-GPU.ipynb docker setup.cfg .github .gitignore requirements/ one_click.py
 
-            # 6. Create archive
+            # 6. Create ZIP file
             cd ..
+            ZIP_NAME="textgen-portable-${VERSION_CLEAN}-${PLATFORM}.zip"
+            echo "Creating archive: $ZIP_NAME"
+
             if [[ "$RUNNER_OS" == "Windows" ]]; then
-                ARCHIVE_NAME="textgen-portable-${VERSION_CLEAN}-${PLATFORM}.zip"
-                echo "Creating archive: $ARCHIVE_NAME"
-                powershell -Command "Compress-Archive -Path text-generation-webui-${VERSION_CLEAN} -DestinationPath $ARCHIVE_NAME"
+                powershell -Command "Compress-Archive -Path text-generation-webui-${VERSION_CLEAN} -DestinationPath $ZIP_NAME"
             else
-                ARCHIVE_NAME="textgen-portable-${VERSION_CLEAN}-${PLATFORM}.tar.gz"
-                echo "Creating archive: $ARCHIVE_NAME"
-                tar czf "$ARCHIVE_NAME" "text-generation-webui-${VERSION_CLEAN}"
+                zip -r "$ZIP_NAME" "text-generation-webui-${VERSION_CLEAN}"
             fi
 
       - name: Upload files to a GitHub release
@@ -189,7 +188,7 @@ jobs:
         continue-on-error: true
         with:
           repo_token: ${{ secrets.GITHUB_TOKEN }}
-          file: ../textgen-portable-*
+          file: ../textgen-portable-*.zip
           tag: ${{ inputs.version }}
           file_glob: true
           make_latest: false

README.md

@@ -13,7 +13,7 @@
 
 # Text Generation Web UI
 
-A Gradio web UI for running Large Language Models locally. 100% private and offline. Supports text generation, vision, tool-calling, training, image generation, and more.
+A Gradio web UI for running Large Language Models locally. 100% private, offline, and free.
 
 [Try the Deep Reason extension](https://oobabooga.gumroad.com/l/deep_reason)
 
@@ -23,20 +23,22 @@ A Gradio web UI for running Large Language Models locally. 100% private and offl
 
 ## Features
 
-- **Easy setup**: [Portable builds](https://github.com/oobabooga/text-generation-webui/releases) (zero setup, just unzip and run) for GGUF models on Windows/Linux/macOS, or a one-click installer for the full feature set.
-- **Multiple backends**: [llama.cpp](https://github.com/ggerganov/llama.cpp), [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp), [Transformers](https://github.com/huggingface/transformers), [ExLlamaV3](https://github.com/turboderp-org/exllamav3), and [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM). Switch between backends and models without restarting.
-- **OpenAI/Anthropic-compatible API**: Chat, Completions, and Messages endpoints with tool-calling support. Use as a local drop-in replacement for the OpenAI/Anthropic APIs ([examples](https://github.com/oobabooga/text-generation-webui/wiki/12-%E2%80%90-OpenAI-API#examples)).
-- **Tool-calling**: Models can call custom functions during chat — web search, page fetching, math, and more. Each tool is a single `.py` file. MCP servers are also supported ([tutorial](https://github.com/oobabooga/text-generation-webui/wiki/Tool-Calling-Tutorial)).
-- **Vision (multimodal)**: Attach images to messages for visual understanding ([tutorial](https://github.com/oobabooga/text-generation-webui/wiki/Multimodal-Tutorial)).
-- **File attachments**: Upload text files, PDF documents, and .docx documents to talk about their contents.
-- **Training**: Fine-tune LoRAs on multi-turn chat or raw text datasets. Supports resuming interrupted runs ([tutorial](https://github.com/oobabooga/text-generation-webui/wiki/05-%E2%80%90-Training-Tab)).
-- **Image generation**: A dedicated tab for `diffusers` models like **Z-Image-Turbo**. Features 4-bit/8-bit quantization and a persistent gallery with metadata ([tutorial](https://github.com/oobabooga/text-generation-webui/wiki/Image-Generation-Tutorial)).
+- Supports multiple local text generation backends, including [llama.cpp](https://github.com/ggerganov/llama.cpp), [Transformers](https://github.com/huggingface/transformers), [ExLlamaV3](https://github.com/turboderp-org/exllamav3), and [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) (the latter via its own [Dockerfile](https://github.com/oobabooga/text-generation-webui/blob/main/docker/TensorRT-LLM/Dockerfile)).
+- Easy setup: Choose between **portable builds** (zero setup, just unzip and run) for GGUF models on Windows/Linux/macOS, or the one-click installer that creates a self-contained `installer_files` directory.
 - 100% offline and private, with zero telemetry, external resources, or remote update requests.
 - `instruct` mode for instruction-following (like ChatGPT), and `chat-instruct`/`chat` modes for talking to custom characters. Prompts are automatically formatted with Jinja2 templates.
+- **File attachments**: Upload text files, PDF documents, and .docx documents to talk about their contents.
+- **Vision (multimodal models)**: Attach images to messages for visual understanding ([tutorial](https://github.com/oobabooga/text-generation-webui/wiki/Multimodal-Tutorial)).
+- **Image generation**: A dedicated tab for `diffusers` models like **Z-Image-Turbo**. Features 4-bit/8-bit quantization and a persistent gallery with metadata ([tutorial](https://github.com/oobabooga/text-generation-webui/wiki/Image-Generation-Tutorial)).
+- **Web search**: Optionally search the internet with LLM-generated queries to add context to the conversation.
+- **Training**: Fine-tune LoRAs on multi-turn chat or raw text datasets. Easy to use, good defaults, and supports resuming interrupted runs ([tutorial](https://github.com/oobabooga/text-generation-webui/wiki/05-%E2%80%90-Training-Tab)).
 - Edit messages, navigate between message versions, and branch conversations at any point.
+- Switch between different models in the UI without restarting.
 - Free-form text generation in the Notebook tab without being limited to chat turns.
 - Multiple sampling parameters and generation options for sophisticated text generation control.
-- Dark/light themes, syntax highlighting for code blocks, and LaTeX rendering for mathematical expressions.
+- Aesthetic UI with dark and light themes.
+- Syntax highlighting for code blocks and LaTeX rendering for mathematical expressions.
+- OpenAI-compatible API with Chat and Completions endpoints, including tool-calling support – see [examples](https://github.com/oobabooga/text-generation-webui/wiki/12-%E2%80%90-OpenAI-API#examples).
 - Extension support, with numerous built-in and user-contributed extensions available. See the [wiki](https://github.com/oobabooga/text-generation-webui/wiki/07-%E2%80%90-Extensions) and [extensions directory](https://github.com/oobabooga/text-generation-webui-extensions) for details.
 
 ## How to install
@@ -45,10 +47,9 @@ A Gradio web UI for running Large Language Models locally. 100% private and offl
 
 No installation needed – just download, unzip and run. All dependencies included.
 
-Download from here: **https://github.com/oobabooga/text-generation-webui/releases**
+Compatible with GGUF (llama.cpp) models on Windows, Linux, and macOS. [Check what models fit your hardware](https://huggingface.co/spaces/oobabooga/accurate-gguf-vram-calculator).
 
-- Builds are provided for Linux, Windows, and macOS, with options for CUDA, Vulkan, ROCm, and CPU-only.
-- Compatible with GGUF (llama.cpp) models.
+Download from here: **https://github.com/oobabooga/text-generation-webui/releases**
 
 #### Option 2: Manual portable install with venv
 
@@ -80,7 +81,7 @@ deactivate
 
 #### Option 3: One-click installer
 
-For users who need additional backends (ExLlamaV3, Transformers), training, image generation, or extensions (TTS, voice input, translation, etc). Requires ~10GB disk space and downloads PyTorch.
+For users who need additional backends (ExLlamaV3, Transformers) or extensions (TTS, voice input, translation, etc). Requires ~10GB disk space and downloads PyTorch.
 
 1. Clone the repository, or [download its source code](https://github.com/oobabooga/text-generation-webui/archive/refs/heads/main.zip) and extract it.
 2. Run the startup script for your OS: `start_windows.bat`, `start_linux.sh`, or `start_macos.sh`.
@@ -145,7 +146,7 @@ conda activate textgen
 |--------|---------|---------|
 | Linux/WSL | NVIDIA | `pip3 install torch==2.9.1 --index-url https://download.pytorch.org/whl/cu128` |
 | Linux/WSL | CPU only | `pip3 install torch==2.9.1 --index-url https://download.pytorch.org/whl/cpu` |
-| Linux | AMD | `pip3 install https://repo.radeon.com/rocm/manylinux/rocm-rel-7.2/torch-2.9.1%2Brocm7.2.0.lw.git7e1940d4-cp313-cp313-linux_x86_64.whl` |
+| Linux | AMD | `pip3 install torch==2.9.1 --index-url https://download.pytorch.org/whl/rocm6.4` |
 | MacOS + MPS | Any | `pip3 install torch==2.9.1` |
 | Windows | NVIDIA | `pip3 install torch==2.9.1 --index-url https://download.pytorch.org/whl/cu128` |
 | Windows | CPU only | `pip3 install torch==2.9.1` |
@@ -200,7 +201,7 @@ ln -s docker/{nvidia/Dockerfile,nvidia/docker-compose.yml,.dockerignore} .
 For AMD GPU:
 ln -s docker/{amd/Dockerfile,amd/docker-compose.yml,.dockerignore} .
 For Intel GPU:
-ln -s docker/{intel/Dockerfile,intel/docker-compose.yml,.dockerignore} .
+ln -s docker/{intel/Dockerfile,amd/docker-compose.yml,.dockerignore} .
 For CPU only
 ln -s docker/{cpu/Dockerfile,cpu/docker-compose.yml,.dockerignore} .
 cp docker/.env.example .env
@@ -235,24 +236,20 @@ List of command-line flags
 </summary>
 
 ```txt
-usage: server.py [-h] [--user-data-dir USER_DATA_DIR] [--multi-user] [--model MODEL] [--lora LORA [LORA ...]] [--model-dir MODEL_DIR] [--lora-dir LORA_DIR] [--model-menu] [--settings SETTINGS]
+usage: server.py [-h] [--multi-user] [--model MODEL] [--lora LORA [LORA ...]] [--model-dir MODEL_DIR] [--lora-dir LORA_DIR] [--model-menu] [--settings SETTINGS]
                  [--extensions EXTENSIONS [EXTENSIONS ...]] [--verbose] [--idle-timeout IDLE_TIMEOUT] [--image-model IMAGE_MODEL] [--image-model-dir IMAGE_MODEL_DIR] [--image-dtype {bfloat16,float16}]
                  [--image-attn-backend {flash_attention_2,sdpa}] [--image-cpu-offload] [--image-compile] [--image-quant {none,bnb-8bit,bnb-4bit,torchao-int8wo,torchao-fp4,torchao-float8wo}]
                  [--loader LOADER] [--ctx-size N] [--cache-type N] [--model-draft MODEL_DRAFT] [--draft-max DRAFT_MAX] [--gpu-layers-draft GPU_LAYERS_DRAFT] [--device-draft DEVICE_DRAFT]
                  [--ctx-size-draft CTX_SIZE_DRAFT] [--spec-type {none,ngram-mod,ngram-simple,ngram-map-k,ngram-map-k4v,ngram-cache}] [--spec-ngram-size-n SPEC_NGRAM_SIZE_N]
                  [--spec-ngram-size-m SPEC_NGRAM_SIZE_M] [--spec-ngram-min-hits SPEC_NGRAM_MIN_HITS] [--gpu-layers N] [--cpu-moe] [--mmproj MMPROJ] [--streaming-llm] [--tensor-split TENSOR_SPLIT]
                  [--row-split] [--no-mmap] [--mlock] [--no-kv-offload] [--batch-size BATCH_SIZE] [--ubatch-size UBATCH_SIZE] [--threads THREADS] [--threads-batch THREADS_BATCH] [--numa]
-                 [--parallel PARALLEL] [--fit-target FIT_TARGET] [--extra-flags EXTRA_FLAGS] [--cpu] [--cpu-memory CPU_MEMORY] [--disk] [--disk-cache-dir DISK_CACHE_DIR] [--load-in-8bit] [--bf16]
-                 [--no-cache] [--trust-remote-code] [--force-safetensors] [--no_use_fast] [--attn-implementation IMPLEMENTATION] [--load-in-4bit] [--use_double_quant] [--compute_dtype COMPUTE_DTYPE]
-                 [--quant_type QUANT_TYPE] [--gpu-split GPU_SPLIT] [--enable-tp] [--tp-backend TP_BACKEND] [--cfg-cache] [--listen] [--listen-port LISTEN_PORT] [--listen-host LISTEN_HOST] [--share]
+                 [--extra-flags EXTRA_FLAGS] [--cpu] [--cpu-memory CPU_MEMORY] [--disk] [--disk-cache-dir DISK_CACHE_DIR] [--load-in-8bit] [--bf16] [--no-cache] [--trust-remote-code]
+                 [--force-safetensors] [--no_use_fast] [--attn-implementation IMPLEMENTATION] [--load-in-4bit] [--use_double_quant] [--compute_dtype COMPUTE_DTYPE] [--quant_type QUANT_TYPE]
+                 [--gpu-split GPU_SPLIT] [--enable-tp] [--tp-backend TP_BACKEND] [--cfg-cache] [--cpp-runner]
+                 [--alpha_value ALPHA_VALUE] [--rope_freq_base ROPE_FREQ_BASE] [--compress_pos_emb COMPRESS_POS_EMB] [--listen] [--listen-port LISTEN_PORT] [--listen-host LISTEN_HOST] [--share]
                  [--auto-launch] [--gradio-auth GRADIO_AUTH] [--gradio-auth-path GRADIO_AUTH_PATH] [--ssl-keyfile SSL_KEYFILE] [--ssl-certfile SSL_CERTFILE] [--subpath SUBPATH] [--old-colors]
                  [--portable] [--api] [--public-api] [--public-api-id PUBLIC_API_ID] [--api-port API_PORT] [--api-key API_KEY] [--admin-key ADMIN_KEY] [--api-enable-ipv6] [--api-disable-ipv4]
-                 [--nowebui] [--temperature N] [--dynatemp-low N] [--dynatemp-high N] [--dynatemp-exponent N] [--smoothing-factor N] [--smoothing-curve N] [--min-p N] [--top-p N] [--top-k N]
-                 [--typical-p N] [--xtc-threshold N] [--xtc-probability N] [--epsilon-cutoff N] [--eta-cutoff N] [--tfs N] [--top-a N] [--top-n-sigma N] [--adaptive-target N] [--adaptive-decay N]
-                 [--dry-multiplier N] [--dry-allowed-length N] [--dry-base N] [--repetition-penalty N] [--frequency-penalty N] [--presence-penalty N] [--encoder-repetition-penalty N]
-                 [--no-repeat-ngram-size N] [--repetition-penalty-range N] [--penalty-alpha N] [--guidance-scale N] [--mirostat-mode N] [--mirostat-tau N] [--mirostat-eta N]
-                 [--do-sample | --no-do-sample] [--dynamic-temperature | --no-dynamic-temperature] [--temperature-last | --no-temperature-last] [--sampler-priority N] [--dry-sequence-breakers N]
-                 [--enable-thinking | --no-enable-thinking] [--reasoning-effort N] [--chat-template-file CHAT_TEMPLATE_FILE]
+                 [--nowebui]
 
 Text Generation Web UI
 
@@ -260,8 +257,7 @@ options:
   -h, --help                                           show this help message and exit
 
 Basic settings:
-  --user-data-dir USER_DATA_DIR                        Path to the user data directory. Default: auto-detected.
-  --multi-user                                         Multi-user mode. Chat histories are not saved or automatically loaded. Best suited for small trusted teams.
+  --multi-user                                         Multi-user mode. Chat histories are not saved or automatically loaded. Warning: this is likely not safe for sharing publicly.
   --model MODEL                                        Name of the model to load by default.
   --lora LORA [LORA ...]                               The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces.
   --model-dir MODEL_DIR                                Path to directory with all the models.
@@ -284,12 +280,12 @@ Image model:
                                                        Quantization method for image model.
 
 Model loader:
-  --loader LOADER                                      Choose the model loader manually, otherwise, it will get autodetected. Valid options: Transformers, llama.cpp, ExLlamav3_HF, ExLlamav3, TensorRT-
-                                                       LLM.
+  --loader LOADER                                      Choose the model loader manually, otherwise, it will get autodetected. Valid options: Transformers, llama.cpp, ExLlamav3_HF, ExLlamav3,
+                                                       TensorRT-LLM.
 
 Context and cache:
-  --ctx-size, --n_ctx, --max_seq_len N                 Context size in tokens. 0 = auto for llama.cpp (requires gpu-layers=-1), 8192 for other loaders.
-  --cache-type, --cache_type N                         KV cache type; valid options: llama.cpp - fp16, q8_0, q4_0; ExLlamaV3 - fp16, q2 to q8 (can specify k_bits and v_bits separately, e.g. q4_q8).
+  --ctx-size N, --n_ctx N, --max_seq_len N             Context size in tokens. llama.cpp: 0 = auto if gpu-layers is also -1.
+  --cache-type N, --cache_type N                       KV cache type; valid options: llama.cpp - fp16, q8_0, q4_0; ExLlamaV3 - fp16, q2 to q8 (can specify k_bits and v_bits separately, e.g. q4_q8).
 
 Speculative decoding:
   --model-draft MODEL_DRAFT                            Path to the draft model for speculative decoding.
@@ -304,7 +300,7 @@ Speculative decoding:
   --spec-ngram-min-hits SPEC_NGRAM_MIN_HITS            Minimum n-gram hits for ngram-map speculative decoding.
 
 llama.cpp:
-  --gpu-layers, --n-gpu-layers N                       Number of layers to offload to the GPU. -1 = auto.
+  --gpu-layers N, --n-gpu-layers N                     Number of layers to offload to the GPU. -1 = auto.
   --cpu-moe                                            Move the experts to the CPU (for MoE models).
   --mmproj MMPROJ                                      Path to the mmproj file for vision models.
   --streaming-llm                                      Activate StreamingLLM to avoid re-evaluating the entire prompt when old messages are removed.
@@ -312,23 +308,19 @@ llama.cpp:
   --row-split                                          Split the model by rows across GPUs. This may improve multi-gpu performance.
   --no-mmap                                            Prevent mmap from being used.
   --mlock                                              Force the system to keep the model in RAM.
-  --no-kv-offload                                      Do not offload the K, Q, V to the GPU. This saves VRAM but reduces performance.
+  --no-kv-offload                                      Do not offload the K, Q, V to the GPU. This saves VRAM but reduces the performance.
   --batch-size BATCH_SIZE                              Maximum number of prompt tokens to batch together when calling llama-server. This is the application level batch size.
   --ubatch-size UBATCH_SIZE                            Maximum number of prompt tokens to batch together when calling llama-server. This is the max physical batch size for computation (device level).
   --threads THREADS                                    Number of threads to use.
   --threads-batch THREADS_BATCH                        Number of threads to use for batches/prompt processing.
   --numa                                               Activate NUMA task allocation for llama.cpp.
-  --parallel PARALLEL                                  Number of parallel request slots. The context size is divided equally among slots. For example, to have 4 slots with 8192 context each, set
-                                                       ctx_size to 32768.
-  --fit-target FIT_TARGET                              Target VRAM margin per device for auto GPU layers, comma-separated list of values in MiB. A single value is broadcast across all devices.
-                                                       Default: 1024.
   --extra-flags EXTRA_FLAGS                            Extra flags to pass to llama-server. Format: "flag1=value1,flag2,flag3=value3". Example: "override-tensor=exps=CPU"
 
 Transformers/Accelerate:
   --cpu                                                Use the CPU to generate text. Warning: Training on CPU is extremely slow.
   --cpu-memory CPU_MEMORY                              Maximum CPU memory in GiB. Use this for CPU offloading.
   --disk                                               If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.
-  --disk-cache-dir DISK_CACHE_DIR                      Directory to save the disk cache to.
+  --disk-cache-dir DISK_CACHE_DIR                      Directory to save the disk cache to. Defaults to "user_data/cache".
   --load-in-8bit                                       Load the model with 8-bit precision (using bitsandbytes).
   --bf16                                               Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.
   --no-cache                                           Set use_cache to False while generating text. This reduces VRAM usage slightly, but it comes at a performance cost.
@@ -349,6 +341,14 @@ ExLlamaV3:
   --tp-backend TP_BACKEND                              The backend for tensor parallelism. Valid options: native, nccl. Default: native.
   --cfg-cache                                          Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader.
 
+TensorRT-LLM:
+  --cpp-runner                                         Use the ModelRunnerCpp runner, which is faster than the default ModelRunner.
+
+RoPE:
+  --alpha_value ALPHA_VALUE                            Positional embeddings alpha factor for NTK RoPE scaling. Use either this or compress_pos_emb, not both.
+  --rope_freq_base ROPE_FREQ_BASE                      If greater than 0, will be used instead of alpha_value. Those two are related by rope_freq_base = 10000 * alpha_value ^ (64 / 63).
+  --compress_pos_emb COMPRESS_POS_EMB                  Positional embeddings compression factor. Should be set to (context length) / (model's original context length). Equal to 1/rope_freq_scale.
+
 Gradio:
   --listen                                             Make the web UI reachable from your local network.
   --listen-port LISTEN_PORT                            The listening port that the server will use.
@@ -365,7 +365,7 @@ Gradio:
 
 API:
   --api                                                Enable the API extension.
-  --public-api                                         Create a public URL for the API using Cloudflare.
+  --public-api                                         Create a public URL for the API using Cloudfare.
   --public-api-id PUBLIC_API_ID                        Tunnel ID for named Cloudflare Tunnel. Use together with public-api option.
   --api-port API_PORT                                  The listening port for the API.
   --api-key API_KEY                                    API authentication key.
@@ -373,67 +373,28 @@ API:
   --api-enable-ipv6                                    Enable IPv6 for the API
   --api-disable-ipv4                                   Disable IPv4 for the API
   --nowebui                                            Do not launch the Gradio UI. Useful for launching the API in standalone mode.
-
-API generation defaults:
-  --temperature N                                      Temperature
-  --dynatemp-low N                                     Dynamic temperature low
-  --dynatemp-high N                                    Dynamic temperature high
-  --dynatemp-exponent N                                Dynamic temperature exponent
-  --smoothing-factor N                                 Smoothing factor
-  --smoothing-curve N                                  Smoothing curve
-  --min-p N                                            Min P
-  --top-p N                                            Top P
-  --top-k N                                            Top K
-  --typical-p N                                        Typical P
-  --xtc-threshold N                                    XTC threshold
-  --xtc-probability N                                  XTC probability
-  --epsilon-cutoff N                                   Epsilon cutoff
-  --eta-cutoff N                                       Eta cutoff
-  --tfs N                                              TFS
-  --top-a N                                            Top A
-  --top-n-sigma N                                      Top N Sigma
-  --adaptive-target N                                  Adaptive target
-  --adaptive-decay N                                   Adaptive decay
-  --dry-multiplier N                                   DRY multiplier
-  --dry-allowed-length N                               DRY allowed length
-  --dry-base N                                         DRY base
-  --repetition-penalty N                               Repetition penalty
-  --frequency-penalty N                                Frequency penalty
-  --presence-penalty N                                 Presence penalty
-  --encoder-repetition-penalty N                       Encoder repetition penalty
-  --no-repeat-ngram-size N                             No repeat ngram size
-  --repetition-penalty-range N                         Repetition penalty range
-  --penalty-alpha N                                    Penalty alpha
-  --guidance-scale N                                   Guidance scale
-  --mirostat-mode N                                    Mirostat mode
-  --mirostat-tau N                                     Mirostat tau
-  --mirostat-eta N                                     Mirostat eta
-  --do-sample, --no-do-sample                          Do sample
-  --dynamic-temperature, --no-dynamic-temperature      Dynamic temperature
-  --temperature-last, --no-temperature-last            Temperature last
-  --sampler-priority N                                 Sampler priority
-  --dry-sequence-breakers N                            DRY sequence breakers
-  --enable-thinking, --no-enable-thinking              Enable thinking
-  --reasoning-effort N                                 Reasoning effort
-  --chat-template-file CHAT_TEMPLATE_FILE              Path to a chat template file (.jinja, .jinja2, or .yaml) to use as the default instruction template for API requests. Overrides the model's
-                                                       built-in template.
 ```
 
 </details>
 
 ## Downloading models
 
-1. Download a GGUF model file from [Hugging Face](https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads&search=gguf).
-2. Place it in the `user_data/models` folder.
+Models should be placed in the folder `text-generation-webui/user_data/models`. They are usually downloaded from [Hugging Face](https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads&search=gguf).
 
-That's it. The UI will detect it automatically.
+To check if a GGUF model will fit in your hardware before downloading it, you can use this tool I created:
 
-To estimate how much memory a model will use, you can use the [GGUF Memory Calculator](https://huggingface.co/spaces/oobabooga/accurate-gguf-vram-calculator).
+[Accurate GGUF VRAM Calculator](https://huggingface.co/spaces/oobabooga/accurate-gguf-vram-calculator)
 
-<details>
-<summary>Other model types (Transformers, EXL3)</summary>
+* GGUF models are a single file and should be placed directly into `user_data/models`. Example:
 
-Models that consist of multiple files (like 16-bit Transformers models and EXL3 models) should be placed in a subfolder inside `user_data/models`:
+```
+text-generation-webui
+└── user_data
+    └── models
+        └── llama-2-13b-chat.Q4_K_M.gguf
+```
+
+* The remaining model types (like 16-bit Transformers models and EXL3 models) are made of several files and must be placed in a subfolder. Example:
 
 ```
 text-generation-webui
@@ -443,18 +404,31 @@ text-generation-webui
             ├── config.json
             ├── generation_config.json
             ├── model-00001-of-00004.safetensors
-            ├── ...
+            ├── model-00002-of-00004.safetensors
+            ├── model-00003-of-00004.safetensors
+            ├── model-00004-of-00004.safetensors
+            ├── model.safetensors.index.json
+            ├── special_tokens_map.json
             ├── tokenizer_config.json
             └── tokenizer.json
 ```
 
-These formats require the one-click installer (not the portable build).
-</details>
+In both cases, you can use the "Model" tab of the UI to download the model from Hugging Face automatically. It is also possible to download it via the command-line with:
+
+```
+python download-model.py organization/model
+```
+
+Run `python download-model.py --help` to see all the options.
 
 ## Documentation
 
 https://github.com/oobabooga/text-generation-webui/wiki
 
+## Google Colab notebook
+
+https://colab.research.google.com/github/oobabooga/text-generation-webui/blob/main/Colab-TextGen-GPU.ipynb
+
 ## Community
 
 https://www.reddit.com/r/Oobabooga/

cmd_windows.bat

@@ -21,7 +21,6 @@ set INSTALL_ENV_DIR=%cd%\installer_files\env
 set PYTHONNOUSERSITE=1
 set PYTHONPATH=
 set PYTHONHOME=
-set PYTHONUTF8=1
 set "CUDA_PATH=%INSTALL_ENV_DIR%"
 set "CUDA_HOME=%CUDA_PATH%"
 

css/chat_style-Dark.css

@@ -2,7 +2,6 @@
     display: grid;
     align-items: start;
     grid-template-columns: 60px minmax(0, 1fr);
-    width: min(100%, calc(724px + 60px));
     padding-bottom: 22px;
     padding-top: 6px;
     font-size: 18px;
@@ -92,6 +91,9 @@
 }
 
 .message-body p {
+    margin-bottom: 0 !important;
+    font-size: 16px !important;
+    line-height: 1.5 !important;
     color: #e0e0e0 !important; /* Light color for text */
 }
 
@@ -120,7 +122,7 @@
     }
 
     .message-body p {
-        font-size: 14px !important;
+        font-size: 14px !important; /* Smaller text for mobile */
     }
 
     .username {

css/chat_style-TheEncrypted777.css

@@ -4,7 +4,6 @@
     display: grid;
     align-items: start;
     grid-template-columns: 60px minmax(0, 1fr);
-    width: min(100%, calc(724px + 60px + 90px));
     padding-bottom: 21px;
     padding-top: 7px;
     font-size: 18px;
@@ -87,8 +86,10 @@
     border-radius: 20px;
 }
 
-.message-body p, .message-body li {
+.message-body p {
+    margin-bottom: 0 !important;
     font-size: 18px !important;
+    line-height: 1.428571429 !important;
     color: rgb(243 244 246) !important;
     text-shadow: 2px 2px 2px rgb(0 0 0);
     font-weight: 500;
@@ -126,7 +127,7 @@
         padding-left: 0;
     }
 
-    .message-body p, .message-body li {
+    .message-body p {
         font-size: 16px !important;
     }
 

css/chat_style-cai-chat-square.css

@@ -19,5 +19,4 @@
     padding-bottom: 1.5em;
     padding-top: 0.5em;
     grid-template-columns: 70px minmax(0, 1fr);
-    width: min(100%, calc(724px + 70px));
 }

css/chat_style-cai-chat.css

@@ -2,7 +2,6 @@
     display: grid;
     align-items: start;
     grid-template-columns: 60px minmax(0, 1fr);
-    width: min(100%, calc(724px + 60px));
     padding-bottom: 1.5em;
     padding-top: 0.5em;
     font-size: 15px;
@@ -47,10 +46,16 @@
     border-radius: 20px;
 }
 
-.message-body p, .message-body li {
+.message-body p {
+    font-size: 15px !important;
+    line-height: 22.5px !important;
     font-weight: 500;
 }
 
+.message-body p, .chat .message-body ul, .chat .message-body ol {
+    margin-bottom: 10px !important;
+}
+
 .dark .message-body p em {
     color: rgb(138 138 138) !important;
 }

css/chat_style-messenger.css

@@ -1,5 +1,4 @@
 .message {
-    width: min(100%, calc(724px + 60px));
     padding-bottom: 22px;
     padding-top: 3px;
     font-size: 15px;
@@ -61,10 +60,8 @@
     text-align: right;
 }
 
-.dark .circle-bot + .text div, .dark .circle-bot + .text *,
-.dark .chat .message .circle-bot + .text .message-body :is(h1, h2, h3, h4, h5, h6),
-.dark .chat .message .circle-bot + .text .message-body a {
-    color: #000 !important;
+.dark .circle-bot + .text div, .dark .circle-bot + .text * {
+    color: #000;
 }
 
 .text {
@@ -79,14 +76,19 @@
     font-weight: bold;
 }
 
+.message-body {
+}
+
 .message-body img {
     max-width: 300px;
     max-height: 300px;
     border-radius: 20px;
 }
 
-.message-body p, .message-body li {
+.message-body p {
+    margin-bottom: 0 !important;
     font-size: 15px !important;
+    line-height: 1.428571429 !important;
     font-weight: 500;
 }
 

css/chat_style-wpp.css

@@ -1,6 +1,5 @@
 .message {
     display: block;
-    width: min(100%, 724px);
     padding-top: 0;
     padding-bottom: 21px;
     font-size: 15px;
@@ -78,8 +77,14 @@
     border-radius: 12px;
 }
 
-.message-body p, .message-body li {
+.message-body p {
     font-size: 15px !important;
+    line-height: 1.4 !important;
+    font-weight: 400;
+}
+
+.message-body p:first-child {
+    margin-top: 0 !important;
 }
 
 .dark .message-body p em {
@@ -95,3 +100,6 @@
     margin-top: 8px;
 }
 
+.message-body p, .chat .message-body ul, .chat .message-body ol {
+    margin-bottom: 10px !important;
+}

css/html_instruct_style.css

@@ -13,12 +13,21 @@
     line-height: 28px !important;
 }
 
-.dark .chat .message-body :is(p,li),
+.dark .chat .message-body :is(p,li,h1,h2,h3,h4,h5,h6),
 .dark .chat .message-body em:not(:is(h1,h2,h3,h4,h5,h6,b,strong) em),
 .dark .chat .message-body q:not(:is(h1,h2,h3,h4,h5,h6,b,strong) q) {
     color: #d1d5db !important;
 }
 
+.chat .message-body :is(th, td),
+.prose hr {
+    border-color: #40404096 !important;
+}
+
+.dark .chat .message-body :is(th, td),
+.dark .prose hr {
+    border-color: rgb(255 255 255 / 30%) !important;
+}
 
 .chat .message-body :is(p, ul, ol) {
     margin: 1.25em 0 !important;
@@ -69,7 +78,7 @@
 
 .chat .user-message .text,
 .chat .assistant-message .text {
-    max-width: 724px;
+    max-width: 700px;
     margin-left: auto;
     margin-right: auto;
 }

css/main.css

@@ -2,8 +2,8 @@
     --darker-gray: #1C1C1D;
     --dark-gray: #212125;
     --light-gray: #2C2E34;
-    --light-theme-gray: #f0f3fb;
-    --border-color-dark: rgba(255, 255, 255, 0.15);
+    --light-theme-gray: #f9fbff;
+    --border-color-dark: #525252;
     --header-width: 112px;
     --selected-item-color-dark: #282930;
 }
@@ -22,17 +22,6 @@
     font-style: italic;
 }
 
-/* Hide spin buttons on number inputs (look bad on Windows) */
-input[type="number"]::-webkit-outer-spin-button,
-input[type="number"]::-webkit-inner-spin-button {
-    -webkit-appearance: none;
-    margin: 0;
-}
-
-input[type="number"] {
-    -moz-appearance: textfield;
-}
-
 .padded.svelte-12cmxck {
     padding: 3px 0;
 }
@@ -65,7 +54,7 @@ div.svelte-iyf88w {
     height: 39.594px;
     align-self: end;
     line-height: 1em;
-    border-radius: 0.75rem;
+    border-radius: 0.375rem;
     flex: none;
 }
 
@@ -138,7 +127,7 @@ gradio-app > :first-child {
 }
 
 .header_bar {
-    border-right: none;
+    border-right: var(--input-border-width) solid var(--input-border-color);
     margin-bottom: 0;
     overflow-x: scroll;
     text-wrap: nowrap;
@@ -161,7 +150,7 @@ gradio-app > :first-child {
 
 .dark .header_bar {
     border: none !important;
-    box-shadow: none;
+    box-shadow: 0 3px 4px rgba(20 20 20 / 60%);
     background-color: #8080802b;
 }
 
@@ -257,8 +246,8 @@ button {
 
 .pretty_scrollbar::-webkit-scrollbar,
 #image-history-gallery > :nth-child(2)::-webkit-scrollbar {
-    width: 7px;
-    height: 7px;
+    width: 8px;
+    height: 8px;
 }
 
 .pretty_scrollbar::-webkit-scrollbar-track,
@@ -271,7 +260,7 @@ button {
 #image-history-gallery > :nth-child(2)::-webkit-scrollbar-thumb,
 #image-history-gallery > :nth-child(2)::-webkit-scrollbar-thumb:hover {
     background: var(--neutral-300);
-    border-radius: 9999px;
+    border-radius: 30px;
 }
 
 .dark .pretty_scrollbar::-webkit-scrollbar-thumb,
@@ -279,17 +268,18 @@ button {
 .dark #image-history-gallery > :nth-child(2)::-webkit-scrollbar-thumb,
 .dark #image-history-gallery > :nth-child(2)::-webkit-scrollbar-thumb:hover {
     background: rgb(255 255 255 / 6.25%);
-    border-radius: 9999px;
+    border-radius: 10px;
 }
 
 .pretty_scrollbar::-webkit-resizer,
 #image-history-gallery > :nth-child(2)::-webkit-resizer {
-    background: transparent;
+    background: #c5c5d2;
 }
 
 .dark .pretty_scrollbar::-webkit-resizer,
 .dark #image-history-gallery > :nth-child(2)::-webkit-resizer {
-    background: transparent;
+    background: #ccc;
+    border-radius: 10px;
 }
 
 .pretty_scrollbar::-webkit-scrollbar-corner,
@@ -410,6 +400,7 @@ audio {
 }
 
 .chat .message {
+    width: min(100%, 48rem);
     margin-left: auto;
     margin-right: auto;
     text-align: start;
@@ -440,31 +431,12 @@ audio {
     font-size: 16px;
 }
 
-.dark .message-body h1,
-.dark .message-body h2,
-.dark .message-body h3,
-.dark .message-body h4,
-.dark .message-body h5,
-.dark .message-body h6 {
-    color: #e8e8e8 !important;
-}
-
-.message-body blockquote {
-    border-left-width: 4px;
-    border-left-color: var(--border-color-primary);
-}
-
-.message-body h1,
-.message-body h2,
-.message-body h3,
-.message-body h4,
-.message-body h5,
-.message-body h6 {
-    color: #1a1a1a;
+.dark .message-body :is(h1, h2, h3, h4, h5, h6) {
+    color: white !important;
 }
 
 .message-body h1 {
-    font-weight: 700;
+    font-weight: 800;
     font-size: 2.25em;
     margin-top: 0;
     margin-bottom: 0.8888889em;
@@ -496,13 +468,13 @@ audio {
 }
 
 .message-body h5 {
-    font-weight: 600;
+    font-weight: normal;
     font-size: 1em;
     margin: 0;
 }
 
 .message-body h6 {
-    font-weight: 600;
+    font-weight: normal;
     font-size: 1em;
     margin: 0;
 }
@@ -602,28 +574,10 @@ audio {
 
 #chat-input textarea {
     background: #f3f4f6;
-    padding: 0.675rem 2.5rem 0.6rem;
-    margin-top: 0.15rem;
-    border: 1px solid #d2d2d8;
-    border-radius: 1.5rem;
-    overflow-y: auto !important;
-}
-
-#chat-input textarea::-webkit-scrollbar {
-    width: 7px;
-}
-
-#chat-input textarea::-webkit-scrollbar-track {
-    background: transparent;
-}
-
-#chat-input textarea::-webkit-scrollbar-thumb {
-    background: var(--neutral-300);
-    border-radius: 9999px;
-}
-
-.dark #chat-input textarea::-webkit-scrollbar-thumb {
-    background: rgb(255 255 255 / 6.25%);
+    padding: 0.65rem 2.5rem;
+    border: 0;
+    box-shadow: 0;
+    border-radius: 8px;
 }
 
 #chat-input textarea::placeholder {
@@ -653,10 +607,6 @@ audio {
     background: transparent;
 }
 
-#chat-input .thumbnails {
-    padding-top: 3px;
-}
-
 .chat-input-positioned {
     max-width: 54rem;
     left: 50%;
@@ -759,30 +709,7 @@ audio {
 
 .hover-element {
     position: relative;
-    padding-top: 4px;
-}
-
-#hover-element-button {
-    display: flex;
-    align-items: center;
-    justify-content: center;
-    width: 32px;
-    height: 32px;
-    border-radius: 0.5rem;
-    cursor: pointer;
-    color: gray;
-}
-
-#hover-element-button:hover {
-    background-color: var(--background-fill-secondary);
-}
-
-#hover-element-button svg {
-    color: inherit;
-}
-
-.dark #hover-element-button:hover {
-    background-color: var(--selected-item-color-dark);
+    font-size: 24px;
 }
 
 .hover-menu {
@@ -790,40 +717,24 @@ audio {
     position: absolute;
     bottom: 100%;
     left: 0;
-    background: white;
-    border: 1px solid rgba(0, 0, 0, 0.1);
-    box-shadow: 0 4px 16px rgb(0 0 0 / 12%), 0 1px 3px rgb(0 0 0 / 8%);
-    border-radius: 0.75rem;
+    box-shadow: 0 0 5px rgb(0 0 0 / 25%);
     z-index: 10000;
     min-width: 330px;
     flex-direction: column;
-    padding: 4px;
-}
-
-.hover-menu::before {
-    content: '';
-    position: absolute;
-    top: 100%;
-    left: 0;
-    width: 100%;
-    height: 8px;
-}
-
-.hover-menu > * {
-    border: none !important;
-    box-shadow: none !important;
 }
 
 .hover-menu button {
     width: 100%;
-    background: transparent !important;
-    border: none !important;
-    border-radius: 0.5rem !important;
+    background: white !important;
+    border-radius: 0 !important;
     justify-content: space-between;
     margin: 0 !important;
     height: 36px;
-    font-weight: 500;
-    box-shadow: none !important;
+    border-color: transparent !important;
+}
+
+.hover-menu button:not(#clear-history-confirm) {
+    border-bottom: 0 !important;
 }
 
 .hover-menu button:hover {
@@ -835,26 +746,19 @@ audio {
 }
 
 #show-controls {
-    background-color: transparent;
-    border: none !important;
+    background-color: white;
+    border-color: transparent !important;
     height: 36px;
-    border-radius: 0.5rem;
+    border-radius: 0;
+    border-bottom: 0 !important;
     padding-top: 3px;
     padding-left: 4px;
     display: flex;
     font-weight: normal;
 }
 
-#show-controls:hover {
-    background-color: #dbeafe;
-}
-
 .dark #show-controls {
-    background-color: transparent;
-}
-
-.dark #show-controls:hover {
-    background-color: var(--selected-item-color-dark);
+    background-color: var(--darker-gray);
 }
 
 #show-controls label {
@@ -864,12 +768,12 @@ audio {
     width: 100%;
     padding-right: 12px;
     gap: 10px;
-    font-weight: 500;
+    font-weight: 600;
     color: var(--button-secondary-text-color);
 }
 
 #show-controls label input {
-    margin-top: 5px;
+    margin-top: 4px;
 }
 
 .transparent-substring {
@@ -909,7 +813,7 @@ audio {
 }
 
 #chat-input-row {
-    padding: 0.5rem 1rem 1rem;
+    padding: 1rem;
 }
 
 #chat-col {
@@ -927,20 +831,9 @@ audio {
     }
 }
 
-.message-body p, .message-body li {
-    line-height: 1.75 !important;
-}
-
-.message-body p, .message-body ul, .message-body ol {
-    margin: 1.25em 0 !important;
-}
-
-.message-body :is(p, ul, ol):first-child {
+.message-body ol, .message-body ul {
     margin-top: 0 !important;
-}
-
-.message-body :is(p, ul, ol):last-child {
-    margin-bottom: 0 !important;
+    margin-bottom: 1.25em !important;
 }
 
 /* ----------------------------------------------
@@ -1002,7 +895,7 @@ audio {
 .options {
     z-index: 100 !important;
     border: 1px solid var(--input-border-color);
-    border-radius: 0.5rem;
+    border-radius: 0;
 }
 
 /* ----------------------------------------------
@@ -1096,13 +989,9 @@ audio {
     cursor: pointer;
 }
 
-#past-chats label {
-    transition: background-color 0.15s ease;
-}
-
 #past-chats .selected,
 #past-chats label:hover {
-    background-color: #c8d8f5 !important;
+    background-color: #dbeafe !important;
 }
 
 #past-chats-buttons,
@@ -1114,49 +1003,6 @@ audio {
     padding-right: 0.5rem;
 }
 
-#new-chat-wrapper {
-    display: contents;
-}
-
-.new-chat-arrow {
-    cursor: pointer;
-    position: relative;
-    padding: 0;
-    margin-right: -15px;
-    height: 39.594px;
-    display: flex;
-    align-items: center;
-}
-
-.new-chat-menu {
-    display: none;
-    position: absolute;
-    top: 0;
-    left: 0;
-    padding-top: 1.2em;
-    z-index: var(--layer-top);
-    white-space: nowrap;
-}
-
-.new-chat-arrow:hover .new-chat-menu {
-    display: block;
-}
-
-.new-chat-menu-item {
-    cursor: pointer;
-    padding: var(--size-2);
-    background: var(--background-fill-primary);
-    box-shadow: var(--shadow-drop-lg);
-    border-radius: var(--container-radius);
-    color: var(--body-text-color);
-    font-size: var(--text-md);
-    font-weight: var(--button-large-text-weight);
-}
-
-.new-chat-menu-item:hover {
-    background: var(--background-fill-secondary);
-}
-
 #past-chats-row,
 #chat-controls {
     width: 260px;
@@ -1258,7 +1104,7 @@ audio {
   Dark theme
 ---------------------------------------------- */
 .dark .header_bar {
-    background-color: #1a1a1a !important;
+    background-color: var(--darker-gray) !important;
 }
 
 .dark .header_bar button.selected {
@@ -1268,28 +1114,22 @@ audio {
 .dark #chat-input textarea {
     background: var(--light-gray);
     color: white !important;
-    border-color: rgba(255, 255, 255, 0.06);
+    border-color: #292c3b;
 }
 
 .dark #chat-input textarea::placeholder {
     color: #9ca3af;
 }
 
-.dark .hover-menu {
-    background: var(--darker-gray);
-    border-color: transparent;
-    box-shadow: 0 4px 16px rgb(0 0 0 / 40%);
-}
-
 .dark .hover-menu button {
-    background-color: transparent !important;
+    border-color: var(--border-color-primary);
+    background-color: var(--darker-gray) !important;
 }
 
 .dark #chat-controls,
 .dark #past-chats-row {
     background-color: var(--darker-gray);
     border: 0 !important;
-    box-shadow: none;
 }
 
 .dark gradio-app .gradio-container.gradio-container-4-37-2 .contain #past-chats .selected,
@@ -1326,11 +1166,11 @@ audio {
   Light theme
 ---------------------------------------------- */
 .header_bar {
-    background-color: #e4e8f0 !important;
+    background-color: var(--light-theme-gray) !important;
 }
 
 .header_bar button.selected {
-    background: #c8d8f5;
+    background: #dbeafe;
 }
 
 #chat-controls,
@@ -1339,11 +1179,11 @@ audio {
 }
 
 .dark #chat-controls {
-    border-left: 1px solid rgba(255, 255, 255, 0.06);
+    border-left: 1px solid #d9d9d0;
 }
 
 .dark #past-chats-row {
-    border-right: 1px solid rgba(255, 255, 255, 0.06);
+    border-right: 1px solid #d9d9d0;
 }
 
 #past-chats-toggle,
@@ -1444,7 +1284,8 @@ audio {
 }
 
 .footer-button svg {
-    stroke: rgb(140 140 148);
+    stroke: rgb(156 163 175);
+    transition: stroke 0.2s;
 }
 
 .footer-button:hover svg {
@@ -1459,12 +1300,11 @@ audio {
     stroke: rgb(209 213 219);
 }
 
-.block:has(> .label-wrap) {
+.tgw-accordion {
     padding: 10px 12px !important;
-    border: 1px solid #d2d2d8;
 }
 
-.dark .block:has(> .label-wrap) {
+.dark .tgw-accordion {
     border: 1px solid var(--border-color-dark);
 }
 
@@ -1533,6 +1373,7 @@ audio {
     overflow-wrap: break-word;
     max-height: 250px;
     overflow-y: scroll;
+    contain: layout;
 }
 
 .chat .message-body .thinking-content p,
@@ -1629,7 +1470,7 @@ strong {
     min-height: 200px;
     max-height: 65vh;
     padding: 10px;
-    border-radius: 0.5rem;
+    border-radius: 5px;
     border: 1px solid #ccc;
     background-color: var(--light-theme-gray);
     font-family: inherit;
@@ -1657,7 +1498,7 @@ strong {
 .edit-control-button {
     padding: 6px 12px;
     border: 1px solid #ccc;
-    border-radius: 0.75rem;
+    border-radius: 4px;
     cursor: pointer;
     background-color: #f8f9fa;
     color: #212529;
@@ -1821,7 +1662,7 @@ button:focus {
 .chat-parent {
     /* Optimize for scrolling performance */
     will-change: scroll-position;
-    contain: style paint;
+    contain: layout style paint;
 
     /* Ensure GPU acceleration */
     transform: translateZ(0);
@@ -1840,7 +1681,7 @@ button:focus {
 }
 
 .dark .sidebar-vertical-separator {
-    border-bottom: 1px solid rgba(255, 255, 255, 0.06);
+    border-bottom: 1px solid rgb(255 255 255 / 10%);
 }
 
 button#swap-height-width {
@@ -1961,117 +1802,15 @@ table {
     border-collapse: collapse;
 }
 
-.table-wrapper {
-    overflow-x: auto;
-}
-
-.message-body :is(td, th) {
-    word-break: normal;
-    overflow-wrap: normal;
-}
-
 table, tr, td, th, thead {
     border: 0;
 }
 
-.prose hr {
-    border-color: var(--border-color-primary);
-}
-
 td + td,
-th + th {
-    border-left: 1px solid var(--border-color-primary) !important;
-}
+th + th { border-left: 1px solid; }
 
 tr + tr td,
-tr + tr th {
-    border-top: 1px solid var(--border-color-primary) !important;
-}
+tr + tr th { border-top: 1px solid; }
 
 thead + tbody tr:first-child td,
-thead + tbody tr:first-child th {
-    border-top: 1px solid var(--border-color-primary) !important;
-}
-
-/* ------------------------------------------------
-   Tools CheckboxGroup - vertical DragDrop-like style
-   ------------------------------------------------ */
-
-/* "Refresh list" link in the Tools label */
-.tools-refresh-link {
-    cursor: pointer;
-}
-
-/* Checkbox list container */
-#tools-group {
-    padding: 0 !important;
-    border-width: 0 !important;
-    background: transparent !important;
-    min-height: 0 !important;
-}
-
-#tools-group .wrap {
-    display: flex;
-    flex-direction: column;
-    flex-wrap: nowrap;
-    gap: 4px;
-    padding: 0;
-    margin-top: var(--spacing-lg);
-    max-height: 350px;
-    overflow-y: auto;
-}
-
-/* Pretty scrollbar for the tools list */
-#tools-group .wrap::-webkit-scrollbar {
-    width: 7px;
-    height: 7px;
-}
-
-#tools-group .wrap::-webkit-scrollbar-track {
-    background: transparent;
-}
-
-#tools-group .wrap::-webkit-scrollbar-thumb,
-#tools-group .wrap::-webkit-scrollbar-thumb:hover {
-    background: var(--neutral-300);
-    border-radius: 9999px;
-}
-
-.dark #tools-group .wrap::-webkit-scrollbar-thumb,
-.dark #tools-group .wrap::-webkit-scrollbar-thumb:hover {
-    background: rgb(255 255 255 / 6.25%);
-    border-radius: 9999px;
-}
-
-#tools-group .wrap::-webkit-scrollbar-corner {
-    background: transparent;
-}
-
-/* Each checkbox item */
-#tools-group label {
-    display: flex;
-    align-items: center;
-    gap: 8px;
-    padding: 5px 8px;
-    border-radius: var(--radius-sm, 4px);
-    background: var(--block-background-fill);
-    border: 1px solid var(--border-color-primary);
-    color: var(--body-text-color);
-    font-size: var(--input-text-size);
-    font-weight: var(--input-text-weight);
-    cursor: pointer;
-    user-select: none;
-    transition: border-color 0.15s ease, background 0.15s ease;
-    box-shadow: none;
-}
-
-#tools-group label:hover {
-    border-color: var(--input-border-color-focus);
-}
-
-#tools-group label span {
-    flex: 1;
-    overflow: hidden;
-    text-overflow: ellipsis;
-    white-space: nowrap;
-}
+thead + tbody tr:first-child th { border-top: 1px solid; }

docs/01 - Chat Tab.md

@@ -112,7 +112,7 @@ Used for talking to an instruction-following model using the prompt format defin
 
 The prompt format is defined by the **Instruction template** parameter in "Parameters" > "Instruction template", which represents a Jinja2 template.
 
-Note that when you load a model in the "Model" tab, the web UI will try to automatically detect its instruction template (if any) from the model metadata (e.g. `tokenizer_config.json` or GGUF metadata), and will update the values under "Parameters" > "Instruction template" accordingly. You should check the model card on Hugging Face to see if you are using the correct prompt format.
+Note that when you load a model in the "Model" tab, the web UI will try to automatically detect its instruction template (if any), and will update the values under "Parameters" > "Instruction template" accordingly. This is done using a set of regular expressions defined in `user_data/models/config.yaml`. This detection is not guaranteed to be accurate. You should check the model card on Hugging Face to see if you are using the correct prompt format.
 
 ### Chat-instruct
 

docs/04 - Model Tab.md

@@ -41,6 +41,9 @@ Options:
 * **cpu_memory**: Maximum CPU memory in GiB to use for CPU offloading via the accelerate library. Whatever doesn't fit in the GPU or CPU will go to a disk cache if the "disk" checkbox is enabled.
 * **compute_dtype**: Used when "load_in_4bit" is checked. I recommend leaving the default value.
 * **quant_type**: Used when "load_in_4bit" is checked. I recommend leaving the default value.
+* **alpha_value**: Used to extend the context length of a model with a minor loss in quality. I have measured 1.75 to be optimal for 1.5x context, and 2.5 for 2x context. That is, with alpha = 2.5 you can make a model with 4096 context length go to 8192 context length.
+* **rope_freq_base**: Originally another way to write "alpha_value", it ended up becoming a necessary parameter for some models like CodeLlama, which was fine-tuned with this set to 1000000 and hence needs to be loaded with it set to 1000000 as well.
+* **compress_pos_emb**: The first and original context-length extension method, discovered by [kaiokendev](https://kaiokendev.github.io/til). When set to 2, the context length is doubled, 3 and it's tripled, etc. It should only be used for models that have been fine-tuned with this parameter set to different than 1. For models that have not been tuned to have greater context length, alpha_value will lead to a smaller accuracy loss.
 * **attn_implementation**: Choose the attention implementation. Valid options: `sdpa`, `eager`, `flash_attention_2`. The default (`sdpa`) works well in most cases; `flash_attention_2` may be useful for training.
 * **cpu**: Loads the model in CPU mode using Pytorch. The model will be loaded in 32-bit precision, so a lot of RAM will be used. CPU inference with transformers is older than llama.cpp and it works, but it's a lot slower. Note: this parameter has a different interpretation in the llama.cpp loader (see above).
 * **load_in_8bit**: Load the model in 8-bit precision using bitsandbytes. The 8-bit kernel in that library has been optimized for training and not inference, so load_in_8bit is slower than load_in_4bit (but more accurate).

docs/05 - Training Tab.md

@@ -4,7 +4,7 @@ A LoRA is tied to a specific model architecture — a LoRA trained on Llama 3 8B
 
 ### Quick Start
 
-1. Load your base model with the **Transformers** loader (no LoRAs loaded).
+1. Load your base model (no LoRAs loaded).
 2. Open the **Training** tab > **Train LoRA**.
 3. Pick a dataset and configure parameters (see [below](#parameters)).
 4. Click **Start LoRA Training** and monitor the [loss](#loss).
@@ -100,8 +100,6 @@ Each parameter has a description in the UI. Below is guidance on the most import
 
 VRAM usage during training is roughly similar to inference with ~1000 tokens of context. If you can run the model, you can probably train LoRAs with the default settings. If you run out of VRAM, reduce `Micro Batch Size` or `Cutoff Length`. Training 4-bit quantized models uses more VRAM — set `Micro Batch Size` to `1` to compensate.
 
-**Gradient checkpointing** is enabled by default. It reduces VRAM usage by recomputing activations during the backward pass instead of storing them in memory. The tradeoff is ~20-30% slower training. There is no impact on accuracy — the results are mathematically identical. The savings are most noticeable with longer sequences and larger batch sizes. You can disable it if you have VRAM to spare and want faster training.
-
 ### Rank
 
 Higher rank = more learning capacity = larger adapter = more VRAM. Use 4–8 for style/format, 128–256 to teach factual knowledge.

docs/07 - Extensions.md

@@ -20,6 +20,7 @@ If you create an extension, you are welcome to host it in a GitHub repository an
 
 |Extension|Description|
 |---------|-----------|
+|[openai](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/openai)| Creates an API that mimics the OpenAI API and can be used as a drop-in replacement. |
 |[superboogav2](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/superboogav2)| Enhanced RAG extension with support for PDF, DOCX, and PPTX files. |
 |[send_pictures](https://github.com/oobabooga/text-generation-webui/blob/main/extensions/send_pictures/)| Creates an image upload field that can be used to send images to the bot in chat mode. Captions are automatically generated using BLIP. |
 |[coqui_tts](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/coqui_tts)| Text-to-speech extension using Coqui XTTS v2. |

docs/12 - OpenAI API.md

@@ -1,6 +1,6 @@
-## OpenAI/Anthropic-compatible API
+## OpenAI compatible API
 
-The main API for this project is meant to be a drop-in replacement for the OpenAI and Anthropic APIs, including Chat, Completions, and Messages endpoints.
+The main API for this project is meant to be a drop-in replacement to the OpenAI API, including Chat and Completions endpoints.
 
 * It is 100% offline and private.
 * It doesn't create any logs.
@@ -19,7 +19,7 @@ Add `--api` to your command-line flags.
 
 ### Examples
 
-For the documentation with all the endpoints, parameters and their types, consult `http://127.0.0.1:5000/docs` or the [typing.py](https://github.com/oobabooga/text-generation-webui/blob/main/modules/api/typing.py) file.
+For the documentation with all the endpoints, parameters and their types, consult `http://127.0.0.1:5000/docs` or the [typing.py](https://github.com/oobabooga/text-generation-webui/blob/main/extensions/openai/typing.py) file.
 
 The official examples in the [OpenAI documentation](https://platform.openai.com/docs/api-reference) should also work, and the same parameters apply (although the API here has more optional parameters).
 
@@ -39,7 +39,7 @@ curl http://127.0.0.1:5000/v1/completions \
 
 #### Chat completions
 
-Works best with instruction-following models. If the "instruction_template" variable is not provided, it will be detected automatically from the model metadata.
+Works best with instruction-following models. If the "instruction_template" variable is not provided, it will be guessed automatically based on the model name using the regex patterns in `user_data/models/config.yaml`.
 
 ```shell
 curl http://127.0.0.1:5000/v1/chat/completions \
@@ -232,17 +232,6 @@ curl -k http://127.0.0.1:5000/v1/internal/model/load \
   }'
 ```
 
-You can also set a default instruction template for all subsequent API requests by passing `instruction_template` (a template name from `user_data/instruction-templates/`) or `instruction_template_str` (a raw Jinja2 string):
-
-```shell
-curl -k http://127.0.0.1:5000/v1/internal/model/load \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model_name": "Qwen_Qwen3-0.6B-Q4_K_M.gguf",
-    "instruction_template": "Alpaca"
-  }'
-```
-
 #### Python chat example
 
 ```python
@@ -501,6 +490,16 @@ The following environment variables can be used (they take precedence over every
 | `OPENEDAI_EMBEDDING_MODEL` | Embedding model (if applicable) |          sentence-transformers/all-mpnet-base-v2                  |
 | `OPENEDAI_EMBEDDING_DEVICE` | Embedding device (if applicable) |           cuda                 |
 
+#### Persistent settings with `settings.yaml`
+
+You can also set the following variables in your `settings.yaml` file:
+
+```
+openai-embedding_device: cuda
+openai-embedding_model: "sentence-transformers/all-mpnet-base-v2"
+openai-debug: 1
+```
+
 ### Third-party application setup
 
 You can usually force an application that uses the OpenAI API to connect to the local API by using the following environment variables:

docs/Tool Calling Tutorial.md

@@ -1,172 +0,0 @@
-## Tool calling in the UI
-
-### 1. Load a model with tool-calling support
-
-Load a model with tool-calling support from the Model tab.
-
-### 2. Select tools
-
-In the chat sidebar, check the tools you want the model to use:
-
-- `web_search`: Search the web using DuckDuckGo.
-- `fetch_webpage`: Fetch the content of a URL.
-- `calculate`: Evaluate math expressions.
-- `get_datetime`: Get the current date and time.
-- `roll_dice`: Roll dice.
-
-### 3. Chat
-
-Send a message as usual. When the model decides it needs a tool, it will call it automatically. You will see each tool call and its result in a collapsible accordion inside the chat message.
-
-The model may call multiple tools in sequence before giving its final answer.
-
-## Writing custom tools
-
-Each tool is a single `.py` file in `user_data/tools/`. It needs two things:
-
-1. A `tool` dictionary that describes the function (name, description, parameters).
-2. An `execute(arguments)` function that runs it and returns the result.
-
-Here is a minimal example (`user_data/tools/get_datetime.py`):
-
-```python
-from datetime import datetime
-
-tool = {
-    "type": "function",
-    "function": {
-        "name": "get_datetime",
-        "description": "Get the current date and time.",
-        "parameters": {
-            "type": "object",
-            "properties": {},
-        }
-    }
-}
-
-
-def execute(arguments):
-    now = datetime.now()
-    return {"date": now.strftime("%Y-%m-%d"), "time": now.strftime("%I:%M %p")}
-```
-
-An example with parameters (`user_data/tools/roll_dice.py`):
-
-```python
-import random
-
-tool = {
-    "type": "function",
-    "function": {
-        "name": "roll_dice",
-        "description": "Roll one or more dice with the specified number of sides.",
-        "parameters": {
-            "type": "object",
-            "properties": {
-                "count": {"type": "integer", "description": "Number of dice to roll.", "default": 1},
-                "sides": {"type": "integer", "description": "Number of sides per die.", "default": 20},
-            },
-        }
-    }
-}
-
-
-def execute(arguments):
-    count = max(1, min(arguments.get("count", 1), 1000))
-    sides = max(2, min(arguments.get("sides", 20), 1000))
-    rolls = [random.randint(1, sides) for _ in range(count)]
-    return {"rolls": rolls, "total": sum(rolls)}
-```
-
-You can open the built-in tools in `user_data/tools/` for more examples.
-
-## MCP servers
-
-You can connect to remote [MCP (Model Context Protocol)](https://modelcontextprotocol.io/) servers to use their tools alongside local ones.
-
-In the chat sidebar, open the **MCP servers** accordion and enter one server URL per line. For servers that require authentication, append headers after the URL separated by commas:
-
-```
-https://example.com/mcp
-https://other.com/mcp,Authorization: Bearer sk-xxx
-```
-
-All tools from the configured servers are automatically discovered and made available to the model during generation. If an MCP tool has the same name as a selected local tool, the local tool takes priority.
-
-## Tool calling over the API
-
-Tool calling over the API follows the [OpenAI API](https://platform.openai.com/docs/guides/function-calling) convention. Define your tools, send them with your messages, and handle tool calls in a loop until the model gives a final answer.
-
-```python
-import json
-import requests
-
-url = "http://127.0.0.1:5000/v1/chat/completions"
-
-tools = [
-    {
-        "type": "function",
-        "function": {
-            "name": "get_weather",
-            "description": "Get the current weather for a given location.",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "location": {"type": "string", "description": "City name"},
-                },
-                "required": ["location"]
-            }
-        }
-    }
-]
-
-
-def execute_tool(name, arguments):
-    if name == "get_weather":
-        return {"temperature": "14°C", "condition": "partly cloudy"}
-    return {"error": f"Unknown tool: {name}"}
-
-
-messages = [{"role": "user", "content": "What's the weather like in Paris?"}]
-
-for _ in range(10):
-    response = requests.post(url, json={"messages": messages, "tools": tools}).json()
-    choice = response["choices"][0]
-
-    if choice["finish_reason"] == "tool_calls":
-        messages.append({
-            "role": "assistant",
-            "content": choice["message"]["content"],
-            "tool_calls": choice["message"]["tool_calls"],
-        })
-
-        for tool_call in choice["message"]["tool_calls"]:
-            name = tool_call["function"]["name"]
-            arguments = json.loads(tool_call["function"]["arguments"])
-            result = execute_tool(name, arguments)
-            print(f"Tool call: {name}({arguments}) => {result}")
-
-            messages.append({
-                "role": "tool",
-                "tool_call_id": tool_call["id"],
-                "content": json.dumps(result),
-            })
-    else:
-        print(f"\nAssistant: {choice['message']['content']}")
-        break
-```
-
-## Supported models
-
-The following models are supported:
-
-- Qwen 3.5
-- GPT-OSS
-- Mistral Small / Devstral
-- DeepSeek V3
-- Kimi-K2
-- MiniMax-M2.5
-- GLM-5
-- Llama 4
-
-Other models that output tool calls as JSON (inside XML tags, code blocks, or plain JSON) are also supported through a generic fallback parser.

download-model.py

@@ -158,21 +158,28 @@ class ModelDownloader:
         # Also if GGUF and safetensors are available, download only safetensors
         if (has_pytorch or has_pt or has_gguf) and has_safetensors:
             has_gguf = False
-            keep = [i for i, c in enumerate(classifications) if c not in ['pytorch', 'pt', 'gguf']]
-            links = [links[i] for i in keep]
-            file_sizes = [file_sizes[i] for i in keep]
+            for i in range(len(classifications) - 1, -1, -1):
+                if classifications[i] in ['pytorch', 'pt', 'gguf']:
+                    links.pop(i)
+                    file_sizes.pop(i)
 
         # For GGUF, try to download only the Q4_K_M if no specific file is specified.
         if has_gguf and specific_file is None:
-            has_q4km = any('q4_k_m' in link.lower() for link in links)
+            has_q4km = False
+            for i in range(len(classifications) - 1, -1, -1):
+                if 'q4_k_m' in links[i].lower():
+                    has_q4km = True
 
             if has_q4km:
-                keep = [i for i, link in enumerate(links) if 'q4_k_m' in link.lower()]
+                for i in range(len(classifications) - 1, -1, -1):
+                    if 'q4_k_m' not in links[i].lower():
+                        links.pop(i)
+                        file_sizes.pop(i)
             else:
-                keep = [i for i, link in enumerate(links) if not link.lower().endswith('.gguf')]
-
-            links = [links[i] for i in keep]
-            file_sizes = [file_sizes[i] for i in keep]
+                for i in range(len(classifications) - 1, -1, -1):
+                    if links[i].lower().endswith('.gguf'):
+                        links.pop(i)
+                        file_sizes.pop(i)
 
         is_llamacpp = has_gguf and specific_file is not None
         return links, sha256, is_lora, is_llamacpp, file_sizes

extensions/openai/completions.py

@@ -0,0 +1,635 @@
+import copy
+import functools
+import json
+import time
+from collections import deque
+from pathlib import Path
+
+import tiktoken
+import yaml
+from pydantic import ValidationError
+
+from extensions.openai.errors import InvalidRequestError
+from extensions.openai.typing import ToolDefinition
+from extensions.openai.utils import debug_msg, getToolCallId, parseToolCall
+from modules import shared
+from modules.chat import (
+    generate_chat_prompt,
+    generate_chat_reply,
+    load_character_memoized,
+    load_instruction_template_memoized
+)
+from modules.image_utils import convert_openai_messages_to_images
+from modules.logging_colors import logger
+from modules.presets import load_preset_memoized
+from modules.text_generation import decode, encode, generate_reply
+
+
+@functools.cache
+def load_chat_template_file(filepath):
+    """Load a chat template from a file path (.jinja, .jinja2, or .yaml/.yml)."""
+    filepath = Path(filepath)
+    ext = filepath.suffix.lower()
+    text = filepath.read_text(encoding='utf-8')
+    if ext in ['.yaml', '.yml']:
+        data = yaml.safe_load(text)
+        return data.get('instruction_template', '')
+    return text
+
+
+def convert_logprobs_to_tiktoken(model, logprobs):
+    # more problems than it's worth.
+    # try:
+    #     encoder = tiktoken.encoding_for_model(model)
+    #     # just pick the first one if it encodes to multiple tokens... 99.9% not required and maybe worse overall.
+    #     return dict([(encoder.decode([encoder.encode(token)[0]]), prob) for token, prob in logprobs.items()])
+    # except KeyError:
+    #     # assume native tokens if we can't find the tokenizer
+    #     return logprobs
+
+    return logprobs
+
+
+def process_parameters(body, is_legacy=False):
+    generate_params = body
+    max_tokens_str = 'length' if is_legacy else 'max_tokens'
+    generate_params['max_new_tokens'] = body.pop(max_tokens_str)
+    if generate_params['truncation_length'] == 0:
+        generate_params['truncation_length'] = shared.settings['truncation_length']
+
+    if generate_params['temperature'] == 0:
+        generate_params['do_sample'] = False
+        generate_params['top_k'] = 1
+
+    if body['preset'] is not None:
+        preset = load_preset_memoized(body['preset'])
+        generate_params.update(preset)
+
+    generate_params['custom_stopping_strings'] = []
+    if 'stop' in body:  # str or array, max len 4 (ignored)
+        if isinstance(body['stop'], str):
+            generate_params['custom_stopping_strings'] = [body['stop']]
+        elif isinstance(body['stop'], list):
+            generate_params['custom_stopping_strings'] = body['stop']
+
+    if shared.args.loader != 'llama.cpp':
+        from transformers import LogitsProcessorList
+
+        from modules.transformers_loader import (
+            LogitsBiasProcessor,
+            LogprobProcessor
+        )
+
+        logits_processor = []
+        logit_bias = body.get('logit_bias', None)
+        if logit_bias:  # {str: float, ...}
+            logits_processor = [LogitsBiasProcessor(logit_bias)]
+
+        logprobs = None  # coming to chat eventually
+        if 'logprobs' in body:
+            logprobs = body.get('logprobs', 0)  # maybe cap at topk? don't clamp 0-5.
+            generate_params['logprob_proc'] = LogprobProcessor(logprobs)
+            logits_processor.extend([generate_params['logprob_proc']])
+        else:
+            logprobs = None
+
+        if logits_processor:  # requires logits_processor support
+            generate_params['logits_processor'] = LogitsProcessorList(logits_processor)
+
+    return generate_params
+
+
+def process_multimodal_content(content):
+    """Extract text and add image placeholders from OpenAI multimodal format"""
+    if isinstance(content, str):
+        return content
+
+    if isinstance(content, list):
+        text_parts = []
+        image_placeholders = ""
+        for item in content:
+            if not isinstance(item, dict):
+                continue
+
+            item_type = item.get('type', '')
+            if item_type == 'text':
+                text_parts.append(item.get('text', ''))
+            elif item_type == 'image_url':
+                image_placeholders += "<__media__>"
+
+        final_text = ' '.join(text_parts)
+        if image_placeholders:
+            return f"{image_placeholders}\n\n{final_text}"
+        else:
+            return final_text
+
+    return str(content)
+
+
+def convert_history(history):
+    '''
+    Chat histories in this program are in the format [message, reply].
+    This function converts OpenAI histories to that format.
+    '''
+    chat_dialogue = []
+    current_message = ""
+    current_reply = ""
+    user_input = ""
+    user_input_last = True
+    system_message = ""
+
+    for entry in history:
+        content = entry["content"]
+        role = entry["role"]
+
+        if role == "user":
+            # Extract text content (images handled by model-specific code)
+            content = process_multimodal_content(content)
+            user_input = content
+            user_input_last = True
+
+            if current_message:
+                chat_dialogue.append([current_message, '', '', {}])
+                current_message = ""
+
+            current_message = content
+        elif role == "assistant":
+            meta = {}
+            tool_calls = entry.get("tool_calls")
+            if tool_calls and isinstance(tool_calls, list) and len(tool_calls) > 0:
+                meta["tool_calls"] = tool_calls
+                if content.strip() == "":
+                    content = ""  # keep empty content, don't skip
+
+            current_reply = content
+            user_input_last = False
+            if current_message:
+                chat_dialogue.append([current_message, current_reply, '', meta])
+                current_message = ""
+                current_reply = ""
+            else:
+                chat_dialogue.append(['', current_reply, '', meta])
+        elif role == "tool":
+            user_input_last = False
+            meta = {}
+            if "tool_call_id" in entry:
+                meta["tool_call_id"] = entry["tool_call_id"]
+            chat_dialogue.append(['', '', content, meta])
+        elif role == "system":
+            system_message += f"\n{content}" if system_message else content
+
+    if not user_input_last:
+        user_input = ""
+
+    return user_input, system_message, {
+        'internal': chat_dialogue,
+        'visible': copy.deepcopy(chat_dialogue),
+        'messages': history  # Store original messages for multimodal models
+    }
+
+
+def chat_completions_common(body: dict, is_legacy: bool = False, stream=False, prompt_only=False, stop_event=None) -> dict:
+    if body.get('functions', []):
+        raise InvalidRequestError(message="functions is not supported.", param='functions')
+
+    if body.get('function_call', ''):
+        raise InvalidRequestError(message="function_call is not supported.", param='function_call')
+
+    if 'messages' not in body:
+        raise InvalidRequestError(message="messages is required", param='messages')
+
+    tools = None
+    if 'tools' in body and body['tools'] is not None and isinstance(body['tools'], list) and len(body['tools']) > 0:
+        tools = validateTools(body['tools'])  # raises InvalidRequestError if validation fails
+
+    messages = body['messages']
+    for m in messages:
+        if 'role' not in m:
+            raise InvalidRequestError(message="messages: missing role", param='messages')
+        elif m['role'] == 'function':
+            raise InvalidRequestError(message="role: function is not supported.", param='messages')
+
+        # Handle multimodal content validation
+        content = m.get('content')
+        if content is None:
+            # OpenAI allows content: null on assistant messages when tool_calls is present
+            if m['role'] == 'assistant' and m.get('tool_calls'):
+                m['content'] = ''
+            else:
+                raise InvalidRequestError(message="messages: missing content", param='messages')
+
+        # Validate multimodal content structure
+        if isinstance(content, list):
+            for item in content:
+                if not isinstance(item, dict) or 'type' not in item:
+                    raise InvalidRequestError(message="messages: invalid content item format", param='messages')
+                if item['type'] not in ['text', 'image_url']:
+                    raise InvalidRequestError(message="messages: unsupported content type", param='messages')
+                if item['type'] == 'text' and 'text' not in item:
+                    raise InvalidRequestError(message="messages: missing text in content item", param='messages')
+                if item['type'] == 'image_url' and ('image_url' not in item or 'url' not in item['image_url']):
+                    raise InvalidRequestError(message="messages: missing image_url in content item", param='messages')
+
+    # Chat Completions
+    object_type = 'chat.completion' if not stream else 'chat.completion.chunk'
+    created_time = int(time.time())
+    cmpl_id = "chatcmpl-%d" % (int(time.time() * 1000000000))
+    resp_list = 'data' if is_legacy else 'choices'
+
+    # generation parameters
+    generate_params = process_parameters(body, is_legacy=is_legacy)
+    if stop_event is not None:
+        generate_params['stop_event'] = stop_event
+    continue_ = body['continue_']
+
+    # Instruction template
+    if body['instruction_template_str']:
+        instruction_template_str = body['instruction_template_str']
+    elif body['instruction_template']:
+        instruction_template = body['instruction_template']
+        instruction_template = "Alpaca" if instruction_template == "None" else instruction_template
+        instruction_template_str = load_instruction_template_memoized(instruction_template)
+    elif shared.args.chat_template_file:
+        instruction_template_str = load_chat_template_file(shared.args.chat_template_file)
+    else:
+        instruction_template_str = shared.settings['instruction_template_str']
+
+    chat_template_str = body['chat_template_str'] or shared.default_settings['chat_template_str']
+    chat_instruct_command = body['chat_instruct_command'] or shared.default_settings['chat-instruct_command']
+
+    # Chat character
+    character = body['character'] or shared.default_settings['character']
+    character = "Assistant" if character == "None" else character
+    name1 = body['user_name'] or shared.default_settings['name1']
+    name1, name2, _, greeting, context = load_character_memoized(character, name1, '')
+    name2 = body['bot_name'] or name2
+    context = body['context'] or context
+    greeting = body['greeting'] or greeting
+    user_bio = body['user_bio'] or ''
+
+    # History
+    user_input, custom_system_message, history = convert_history(messages)
+
+    generate_params.update({
+        'mode': body['mode'],
+        'name1': name1,
+        'name2': name2,
+        'context': context,
+        'greeting': greeting,
+        'user_bio': user_bio,
+        'instruction_template_str': instruction_template_str,
+        'custom_system_message': custom_system_message,
+        'chat_template_str': chat_template_str,
+        'chat-instruct_command': chat_instruct_command,
+        'tools': tools,
+        'history': history,
+        'stream': stream
+    })
+
+    max_tokens = generate_params['max_new_tokens']
+    if max_tokens in [None, 0]:
+        generate_params['max_new_tokens'] = 512
+        generate_params['auto_max_new_tokens'] = True
+
+    requested_model = generate_params.pop('model')
+    logprob_proc = generate_params.pop('logprob_proc', None)
+
+    def chat_streaming_chunk(content, chunk_tool_calls=None):
+        # begin streaming
+        chunk = {
+            "id": cmpl_id,
+            "object": object_type,
+            "created": created_time,
+            "model": shared.model_name,
+            resp_list: [{
+                "index": 0,
+                "finish_reason": None,
+                "delta": {'role': 'assistant', 'content': content, 'tool_calls': chunk_tool_calls},
+            }],
+        }
+
+        if logprob_proc:  # not official for chat yet
+            top_logprobs = convert_logprobs_to_tiktoken(model=requested_model, logprobs=logprob_proc.token_alternatives)
+            chunk[resp_list][0]["logprobs"] = {'top_logprobs': [top_logprobs]}
+        # else:
+        #    chunk[resp_list][0]["logprobs"] = None
+
+        return chunk
+
+    # generate reply #######################################
+    if prompt_only:
+        prompt = generate_chat_prompt(user_input, generate_params, _continue=continue_)
+        yield {'prompt': prompt}
+        return
+
+    if stream:
+        yield chat_streaming_chunk('')
+
+    generator = generate_chat_reply(
+        user_input, generate_params, regenerate=False, _continue=continue_, loading_message=False)
+
+    answer = ''
+    seen_content = ''
+
+    tool_calls = []
+    end_last_tool_call = 0
+    supported_tools = [x["function"]["name"] for x in tools] if tools is not None else None
+
+    for a in generator:
+        answer = a['internal'][-1][1]
+
+        if supported_tools is not None:
+            tool_call = parseToolCall(answer[end_last_tool_call:], supported_tools) if len(answer) > 0 else []
+            if len(tool_call) > 0:
+                for tc in tool_call:
+                    tc["id"] = getToolCallId()
+                    tc["index"] = len(tool_calls)
+                    tc["function"]["arguments"] = json.dumps(tc["function"]["arguments"])
+                    tool_calls.append(tc)
+                end_last_tool_call = len(answer)
+
+        if stream:
+            len_seen = len(seen_content)
+            new_content = answer[len_seen:]
+
+            if not new_content or chr(0xfffd) in new_content:  # partial unicode character, don't send it yet.
+                continue
+
+            chunk = chat_streaming_chunk(new_content)
+
+            seen_content = answer
+            yield chunk
+
+        # stop generation if tool_calls were generated previously
+        if len(tool_calls) > 0:
+            break
+
+    token_count = shared.model.last_prompt_token_count if hasattr(shared.model, 'last_prompt_token_count') else 0
+    completion_token_count = len(encode(answer)[0])
+    stop_reason = "stop"
+    if len(tool_calls) > 0:
+        stop_reason = "tool_calls"
+    if token_count + completion_token_count >= generate_params['truncation_length'] or completion_token_count >= generate_params['max_new_tokens']:
+        stop_reason = "length"
+
+    if stream:
+        chunk = chat_streaming_chunk('', tool_calls)
+        chunk[resp_list][0]['finish_reason'] = stop_reason
+        chunk['usage'] = {
+            "prompt_tokens": token_count,
+            "completion_tokens": completion_token_count,
+            "total_tokens": token_count + completion_token_count
+        }
+
+        yield chunk
+    else:
+        resp = {
+            "id": cmpl_id,
+            "object": object_type,
+            "created": created_time,
+            "model": shared.model_name,
+            resp_list: [{
+                "index": 0,
+                "finish_reason": stop_reason,
+                "message": {"role": "assistant", "content": answer, **({"tool_calls": tool_calls} if tool_calls else {})},
+            }],
+            "usage": {
+                "prompt_tokens": token_count,
+                "completion_tokens": completion_token_count,
+                "total_tokens": token_count + completion_token_count
+            }
+        }
+        if logprob_proc:  # not official for chat yet
+            top_logprobs = convert_logprobs_to_tiktoken(model=requested_model, logprobs=logprob_proc.token_alternatives)
+            resp[resp_list][0]["logprobs"] = {'top_logprobs': [top_logprobs]}
+        # else:
+        #     resp[resp_list][0]["logprobs"] = None
+
+        yield resp
+
+
+def completions_common(body: dict, is_legacy: bool = False, stream=False, stop_event=None):
+    object_type = 'text_completion'
+    created_time = int(time.time())
+    cmpl_id = "conv-%d" % (int(time.time() * 1000000000))
+    resp_list = 'data' if is_legacy else 'choices'
+
+    prompt_str = 'context' if is_legacy else 'prompt'
+
+    # Handle both prompt and messages format for unified multimodal support
+    if prompt_str not in body or body[prompt_str] is None:
+        if 'messages' in body:
+            # Convert messages format to prompt for completions endpoint
+            prompt_text = ""
+            for message in body.get('messages', []):
+                if isinstance(message, dict) and 'content' in message:
+                    # Extract text content from multimodal messages
+                    content = message['content']
+                    if isinstance(content, str):
+                        prompt_text += content
+                    elif isinstance(content, list):
+                        for item in content:
+                            if isinstance(item, dict) and item.get('type') == 'text':
+                                prompt_text += item.get('text', '')
+
+            # Allow empty prompts for image-only requests
+            body[prompt_str] = prompt_text
+        else:
+            raise InvalidRequestError("Missing required input", param=prompt_str)
+
+    # common params
+    generate_params = process_parameters(body, is_legacy=is_legacy)
+    max_tokens = generate_params['max_new_tokens']
+    generate_params['stream'] = stream
+    if stop_event is not None:
+        generate_params['stop_event'] = stop_event
+    requested_model = generate_params.pop('model')
+    logprob_proc = generate_params.pop('logprob_proc', None)
+    suffix = body['suffix'] if body['suffix'] else ''
+    echo = body['echo']
+
+    # Add messages to generate_params if present for multimodal processing
+    if body.get('messages'):
+        generate_params['messages'] = body['messages']
+        raw_images = convert_openai_messages_to_images(generate_params['messages'])
+        if raw_images:
+            logger.info(f"Found {len(raw_images)} image(s) in request.")
+            generate_params['raw_images'] = raw_images
+
+    if not stream:
+        prompt_arg = body[prompt_str]
+
+        # Handle empty/None prompts (e.g., image-only requests)
+        if prompt_arg is None:
+            prompt_arg = ""
+
+        if isinstance(prompt_arg, str) or (isinstance(prompt_arg, list) and len(prompt_arg) > 0 and isinstance(prompt_arg[0], int)):
+            prompt_arg = [prompt_arg]
+
+        resp_list_data = []
+        total_completion_token_count = 0
+        total_prompt_token_count = 0
+
+        for idx, prompt in enumerate(prompt_arg, start=0):
+            if isinstance(prompt, list) and len(prompt) > 0 and isinstance(prompt[0], int):
+                # token lists
+                if requested_model == shared.model_name:
+                    prompt = decode(prompt)[0]
+                else:
+                    try:
+                        encoder = tiktoken.encoding_for_model(requested_model)
+                        prompt = encoder.decode(prompt)
+                    except KeyError:
+                        prompt = decode(prompt)[0]
+
+            prefix = prompt if echo else ''
+
+            # generate reply #######################################
+            debug_msg({'prompt': prompt, 'generate_params': generate_params})
+            generator = generate_reply(prompt, generate_params, is_chat=False)
+            answer = ''
+
+            for a in generator:
+                answer = a
+
+            token_count = len(encode(prompt)[0])
+            total_prompt_token_count += token_count
+            completion_token_count = len(encode(answer)[0])
+            total_completion_token_count += completion_token_count
+            stop_reason = "stop"
+            if token_count + completion_token_count >= generate_params['truncation_length'] or completion_token_count >= max_tokens:
+                stop_reason = "length"
+
+            respi = {
+                "index": idx,
+                "finish_reason": stop_reason,
+                "text": prefix + answer + suffix,
+                "logprobs": {'top_logprobs': [logprob_proc.token_alternatives]} if logprob_proc else None,
+            }
+
+            resp_list_data.extend([respi])
+
+        resp = {
+            "id": cmpl_id,
+            "object": object_type,
+            "created": created_time,
+            "model": shared.model_name,
+            resp_list: resp_list_data,
+            "usage": {
+                "prompt_tokens": total_prompt_token_count,
+                "completion_tokens": total_completion_token_count,
+                "total_tokens": total_prompt_token_count + total_completion_token_count
+            }
+        }
+
+        yield resp
+    else:
+        prompt = body[prompt_str]
+        if isinstance(prompt, list):
+            if prompt and isinstance(prompt[0], int):
+                try:
+                    encoder = tiktoken.encoding_for_model(requested_model)
+                    prompt = encoder.decode(prompt)
+                except KeyError:
+                    prompt = decode(prompt)[0]
+            else:
+                raise InvalidRequestError(message="API Batched generation not yet supported.", param=prompt_str)
+
+        prefix = prompt if echo else ''
+        token_count = len(encode(prompt)[0])
+
+        def text_streaming_chunk(content):
+            # begin streaming
+            chunk = {
+                "id": cmpl_id,
+                "object": object_type,
+                "created": created_time,
+                "model": shared.model_name,
+                resp_list: [{
+                    "index": 0,
+                    "finish_reason": None,
+                    "text": content,
+                    "logprobs": {'top_logprobs': [logprob_proc.token_alternatives]} if logprob_proc else None,
+                }],
+            }
+
+            return chunk
+
+        yield text_streaming_chunk(prefix)
+
+        # generate reply #######################################
+        debug_msg({'prompt': prompt, 'generate_params': generate_params})
+        generator = generate_reply(prompt, generate_params, is_chat=False)
+        answer = ''
+        seen_content = ''
+        completion_token_count = 0
+
+        for a in generator:
+            answer = a
+
+            len_seen = len(seen_content)
+            new_content = answer[len_seen:]
+
+            if not new_content or chr(0xfffd) in new_content:  # partial unicode character, don't send it yet.
+                continue
+
+            seen_content = answer
+            chunk = text_streaming_chunk(new_content)
+            yield chunk
+
+        completion_token_count = len(encode(answer)[0])
+        stop_reason = "stop"
+        if token_count + completion_token_count >= generate_params['truncation_length'] or completion_token_count >= max_tokens:
+            stop_reason = "length"
+
+        chunk = text_streaming_chunk(suffix)
+        chunk[resp_list][0]["finish_reason"] = stop_reason
+        chunk["usage"] = {
+            "prompt_tokens": token_count,
+            "completion_tokens": completion_token_count,
+            "total_tokens": token_count + completion_token_count
+        }
+
+        yield chunk
+
+
+def chat_completions(body: dict, is_legacy: bool = False, stop_event=None) -> dict:
+    generator = chat_completions_common(body, is_legacy, stream=False, stop_event=stop_event)
+    return deque(generator, maxlen=1).pop()
+
+
+def stream_chat_completions(body: dict, is_legacy: bool = False, stop_event=None):
+    for resp in chat_completions_common(body, is_legacy, stream=True, stop_event=stop_event):
+        yield resp
+
+
+def completions(body: dict, is_legacy: bool = False, stop_event=None) -> dict:
+    generator = completions_common(body, is_legacy, stream=False, stop_event=stop_event)
+    return deque(generator, maxlen=1).pop()
+
+
+def stream_completions(body: dict, is_legacy: bool = False, stop_event=None):
+    for resp in completions_common(body, is_legacy, stream=True, stop_event=stop_event):
+        yield resp
+
+
+def validateTools(tools: list[dict]):
+    # Validate each tool definition in the JSON array
+    valid_tools = None
+    for idx in range(len(tools)):
+        tool = tools[idx]
+        try:
+            tool_definition = ToolDefinition(**tool)
+            # Backfill defaults so Jinja2 templates don't crash on missing fields
+            func = tool.get("function", {})
+            if "description" not in func:
+                func["description"] = ""
+            if "parameters" not in func:
+                func["parameters"] = {"type": "object", "properties": {}}
+            if valid_tools is None:
+                valid_tools = []
+            valid_tools.append(tool)
+        except ValidationError:
+            raise InvalidRequestError(message=f"Invalid tool specification at index {idx}.", param='tools')
+
+    return valid_tools

extensions/openai/embeddings.py

@@ -3,10 +3,9 @@ import os
 import numpy as np
 from transformers import AutoModel
 
-from .errors import ServiceUnavailableError
-from .utils import debug_msg, float_list_to_base64
+from extensions.openai.errors import ServiceUnavailableError
+from extensions.openai.utils import debug_msg, float_list_to_base64
 from modules.logging_colors import logger
-from modules import shared
 
 embeddings_params_initialized = False
 
@@ -18,12 +17,14 @@ def initialize_embedding_params():
     '''
     global embeddings_params_initialized
     if not embeddings_params_initialized:
+        from extensions.openai.script import params
+
         global st_model, embeddings_model, embeddings_device
 
-        st_model = os.environ.get("OPENEDAI_EMBEDDING_MODEL", 'sentence-transformers/all-mpnet-base-v2')
+        st_model = os.environ.get("OPENEDAI_EMBEDDING_MODEL", params.get('embedding_model', 'all-mpnet-base-v2'))
         embeddings_model = None
         # OPENEDAI_EMBEDDING_DEVICE: auto (best or cpu), cpu, cuda, ipu, xpu, mkldnn, opengl, opencl, ideep, hip, ve, fpga, ort, xla, lazy, vulkan, mps, meta, hpu, mtia, privateuseone
-        embeddings_device = os.environ.get("OPENEDAI_EMBEDDING_DEVICE", 'cpu')
+        embeddings_device = os.environ.get("OPENEDAI_EMBEDDING_DEVICE", params.get('embedding_device', 'cpu'))
         if embeddings_device.lower() == 'auto':
             embeddings_device = None
 
@@ -40,14 +41,14 @@ def load_embedding_model(model: str):
     initialize_embedding_params()
     global embeddings_device, embeddings_model
     try:
-        logger.info(f"Try embedding model: {model} on {embeddings_device}")
+        print(f"Try embedding model: {model} on {embeddings_device}")
         if 'jina-embeddings' in model:
-            embeddings_model = AutoModel.from_pretrained(model, trust_remote_code=shared.args.trust_remote_code)
+            embeddings_model = AutoModel.from_pretrained(model, trust_remote_code=True)  # trust_remote_code is needed to use the encode method
             embeddings_model = embeddings_model.to(embeddings_device)
         else:
             embeddings_model = SentenceTransformer(model, device=embeddings_device)
 
-        logger.info(f"Loaded embedding model: {model}")
+        print(f"Loaded embedding model: {model}")
     except Exception as e:
         embeddings_model = None
         raise ServiceUnavailableError(f"Error: Failed to load embedding model: {model}", internal_message=repr(e))

extensions/openai/images.py

@@ -4,12 +4,9 @@ OpenAI-compatible image generation using local diffusion models.
 
 import base64
 import io
-import json
 import time
 
-from PIL.PngImagePlugin import PngInfo
-
-from .errors import ServiceUnavailableError
+from extensions.openai.errors import ServiceUnavailableError
 from modules import shared
 
 
@@ -18,7 +15,7 @@ def generations(request):
     Generate images using the loaded diffusion model.
     Returns dict with 'created' timestamp and 'data' list of images.
     """
-    from modules.ui_image_generation import build_generation_metadata, generate
+    from modules.ui_image_generation import generate
 
     if shared.image_model is None:
         raise ServiceUnavailableError("No image model loaded. Load a model via the UI first.")
@@ -49,18 +46,10 @@ def generations(request):
     if not images:
         raise ServiceUnavailableError("Image generation failed or produced no images.")
 
-    # Build response with per-batch metadata (seed increments per batch)
-    base_seed = state.get('image_seed_resolved', state['image_seed'])
-    batch_size = int(state['image_batch_size'])
-
+    # Build response
     resp = {'created': int(time.time()), 'data': []}
-    for idx, img in enumerate(images):
-        batch_seed = base_seed + idx // batch_size
-        metadata = build_generation_metadata(state, batch_seed)
-        metadata_json = json.dumps(metadata, ensure_ascii=False)
-        png_info = PngInfo()
-        png_info.add_text("image_gen_settings", metadata_json)
-        b64 = _image_to_base64(img, png_info)
+    for img in images:
+        b64 = _image_to_base64(img)
 
         image_obj = {'revised_prompt': request.prompt}
 
@@ -74,7 +63,7 @@ def generations(request):
     return resp
 
 
-def _image_to_base64(image, png_info=None) -> str:
+def _image_to_base64(image) -> str:
     buffered = io.BytesIO()
-    image.save(buffered, format="PNG", pnginfo=png_info)
+    image.save(buffered, format="PNG")
     return base64.b64encode(buffered.getvalue()).decode('utf-8')

extensions/openai/logits.py

@@ -1,4 +1,4 @@
-from .completions import process_parameters
+from extensions.openai.completions import process_parameters
 from modules.logits import get_next_logits
 
 

extensions/openai/models.py

@@ -1,8 +1,8 @@
-from modules import loaders, shared
+from modules import shared, ui
 from modules.logging_colors import logger
 from modules.LoRA import add_lora_to_model
 from modules.models import load_model, unload_model
-from modules.models_settings import get_model_metadata, load_instruction_template, update_model_parameters
+from modules.models_settings import get_model_metadata, update_model_parameters
 from modules.utils import get_available_loras, get_available_models
 
 
@@ -20,14 +20,10 @@ def list_models():
 
 def list_models_openai_format():
     """Returns model list in OpenAI API format"""
-    if shared.model_name and shared.model_name != 'None':
-        data = [model_info_dict(shared.model_name)]
-    else:
-        data = []
-
+    model_names = get_available_models()
     return {
         "object": "list",
-        "data": data
+        "data": [model_info_dict(name) for name in model_names]
     }
 
 
@@ -42,27 +38,19 @@ def model_info_dict(model_name: str) -> dict:
 
 def _load_model(data):
     model_name = data["model_name"]
-    args = data.get("args")
+    args = data["args"]
+    settings = data["settings"]
 
     unload_model()
     model_settings = get_model_metadata(model_name)
+    update_model_parameters(model_settings)
 
     # Update shared.args with custom model loading settings
     # Security: only allow keys that correspond to model loading
     # parameters exposed in the UI. Never allow security-sensitive
     # flags like trust_remote_code or extra_flags to be set via the API.
     blocked_keys = {'extra_flags'}
-    allowed_keys = set(loaders.list_model_elements()) - blocked_keys
-
-    # Reset all loader args to their startup values before applying new ones,
-    # so settings from a previous API load don't leak into this one.
-    # Include blocked keys in the reset (safe: restores startup value, not API-controlled).
-    for k in allowed_keys | blocked_keys:
-        if hasattr(shared.args, k) and hasattr(shared.original_args, k):
-            setattr(shared.args, k, getattr(shared.original_args, k))
-
-    update_model_parameters(model_settings)
-
+    allowed_keys = set(ui.list_model_elements()) - blocked_keys
     if args:
         for k in args:
             if k in allowed_keys and hasattr(shared.args, k):
@@ -70,12 +58,15 @@ def _load_model(data):
 
     shared.model, shared.tokenizer = load_model(model_name)
 
-    if data.get("instruction_template_str") is not None:
-        shared.settings['instruction_template_str'] = data["instruction_template_str"]
-        logger.info("INSTRUCTION TEMPLATE: set to custom Jinja2 string")
-    elif data.get("instruction_template") is not None:
-        shared.settings['instruction_template_str'] = load_instruction_template(data["instruction_template"])
-        logger.info(f"INSTRUCTION TEMPLATE: {data['instruction_template']}")
+    # Update shared.settings with custom generation defaults
+    if settings:
+        for k in settings:
+            if k in shared.settings:
+                shared.settings[k] = settings[k]
+                if k == 'truncation_length':
+                    logger.info(f"TRUNCATION LENGTH (UPDATED): {shared.settings['truncation_length']}")
+                elif k == 'instruction_template':
+                    logger.info(f"INSTRUCTION TEMPLATE (UPDATED): {shared.settings['instruction_template']}")
 
 
 def list_loras():

extensions/openai/moderations.py

@@ -3,7 +3,7 @@ import time
 import numpy as np
 from numpy.linalg import norm
 
-from .embeddings import get_embeddings
+from extensions.openai.embeddings import get_embeddings
 
 moderations_disabled = False  # return 0/false
 category_embeddings = None
@@ -64,4 +64,6 @@ def moderations(input):
                 'category_scores': category_scores,
             }])
 
+    print(results)
+
     return results

extensions/openai/script.py

@@ -10,27 +10,24 @@ from threading import Thread
 
 import uvicorn
 from fastapi import Depends, FastAPI, Header, HTTPException
-from fastapi.exceptions import RequestValidationError
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.requests import Request
 from fastapi.responses import JSONResponse
+from pydub import AudioSegment
 from sse_starlette import EventSourceResponse
 from starlette.concurrency import iterate_in_threadpool
 
-import modules.api.completions as OAIcompletions
-import modules.api.logits as OAIlogits
-import modules.api.models as OAImodels
-import modules.api.anthropic as Anthropic
-from .tokens import token_count, token_decode, token_encode
-from .errors import OpenAIError
-from .utils import _start_cloudflared
+import extensions.openai.completions as OAIcompletions
+import extensions.openai.logits as OAIlogits
+import extensions.openai.models as OAImodels
+from extensions.openai.tokens import token_count, token_decode, token_encode
+from extensions.openai.utils import _start_cloudflared
 from modules import shared
 from modules.logging_colors import logger
 from modules.models import unload_model
 from modules.text_generation import stop_everything_event  # used by /v1/internal/stop-generation
 
 from .typing import (
-    AnthropicRequest,
     ChatCompletionRequest,
     ChatCompletionResponse,
     ChatPromptResponse,
@@ -55,6 +52,12 @@ from .typing import (
     to_dict
 )
 
+params = {
+    'embedding_device': 'cpu',
+    'embedding_model': 'sentence-transformers/all-mpnet-base-v2',
+    'debug': 0
+}
+
 
 async def _wait_for_disconnect(request: Request, stop_event: threading.Event):
     """Block until the client disconnects, then signal the stop_event."""
@@ -77,23 +80,9 @@ def verify_admin_key(authorization: str = Header(None)) -> None:
         raise HTTPException(status_code=401, detail="Unauthorized")
 
 
-def verify_anthropic_key(x_api_key: str = Header(None, alias="x-api-key")) -> None:
-    expected_api_key = shared.args.api_key
-    if expected_api_key and (x_api_key is None or x_api_key != expected_api_key):
-        raise HTTPException(status_code=401, detail="Unauthorized")
-
-
-class AnthropicError(Exception):
-    def __init__(self, message: str, error_type: str = "invalid_request_error", status_code: int = 400):
-        self.message = message
-        self.error_type = error_type
-        self.status_code = status_code
-
-
 app = FastAPI()
 check_key = [Depends(verify_api_key)]
 check_admin_key = [Depends(verify_admin_key)]
-check_anthropic_key = [Depends(verify_anthropic_key)]
 
 # Configure CORS settings to allow all origins, methods, and headers
 app.add_middleware(
@@ -105,42 +94,6 @@ app.add_middleware(
 )
 
 
-@app.exception_handler(OpenAIError)
-async def openai_error_handler(request: Request, exc: OpenAIError):
-    error_type = "server_error" if exc.code >= 500 else "invalid_request_error"
-    return JSONResponse(
-        status_code=exc.code,
-        content={"error": {
-            "message": exc.message,
-            "type": error_type,
-            "param": getattr(exc, 'param', None),
-            "code": None
-        }}
-    )
-
-
-@app.exception_handler(AnthropicError)
-async def anthropic_error_handler(request: Request, exc: AnthropicError):
-    return JSONResponse(
-        status_code=exc.status_code,
-        content={"type": "error", "error": {"type": exc.error_type, "message": exc.message}}
-    )
-
-
-@app.exception_handler(RequestValidationError)
-async def validation_error_handler(request: Request, exc: RequestValidationError):
-    if request.url.path.startswith("/v1/messages"):
-        messages = "; ".join(
-            f"{'.'.join(str(l) for l in e['loc'])}: {e['msg']}" for e in exc.errors()
-        )
-        return JSONResponse(
-            status_code=400,
-            content={"type": "error", "error": {"type": "invalid_request_error", "message": messages}}
-        )
-
-    return JSONResponse(status_code=422, content={"detail": exc.errors()})
-
-
 @app.middleware("http")
 async def validate_host_header(request: Request, call_next):
     # Be strict about only approving access to localhost by default
@@ -166,12 +119,6 @@ async def openai_completions(request: Request, request_data: CompletionRequest):
     is_legacy = "/generate" in path
 
     if request_data.stream:
-        if (request_data.n or 1) > 1:
-            return JSONResponse(
-                status_code=400,
-                content={"error": {"message": "n > 1 is not supported with streaming.", "type": "invalid_request_error", "param": "n", "code": None}}
-            )
-
         stop_event = threading.Event()
 
         async def generator():
@@ -183,8 +130,6 @@ async def openai_completions(request: Request, request_data: CompletionRequest):
                         break
 
                     yield {"data": json.dumps(resp)}
-
-                yield {"data": "[DONE]"}
             finally:
                 stop_event.set()
                 response.close()
@@ -225,8 +170,6 @@ async def openai_chat_completions(request: Request, request_data: ChatCompletion
                         break
 
                     yield {"data": json.dumps(resp)}
-
-                yield {"data": "[DONE]"}
             finally:
                 stop_event.set()
                 response.close()
@@ -250,76 +193,6 @@ async def openai_chat_completions(request: Request, request_data: ChatCompletion
         return JSONResponse(response)
 
 
-@app.post('/v1/messages', dependencies=check_anthropic_key)
-async def anthropic_messages(request: Request, request_data: AnthropicRequest):
-    body = to_dict(request_data)
-    model = body.get('model') or shared.model_name or 'unknown'
-
-    try:
-        converted = Anthropic.convert_request(body)
-    except Exception as e:
-        raise AnthropicError(message=str(e))
-
-    try:
-        return await _anthropic_generate(request, request_data, converted, model)
-    except OpenAIError as e:
-        error_type = "invalid_request_error" if e.code < 500 else "api_error"
-        if e.code == 503:
-            error_type = "overloaded_error"
-        raise AnthropicError(message=e.message, error_type=error_type, status_code=e.code)
-    except Exception as e:
-        raise AnthropicError(message=str(e) or "Internal server error", error_type="api_error", status_code=500)
-
-
-async def _anthropic_generate(request, request_data, converted, model):
-    if request_data.stream:
-        stop_event = threading.Event()
-
-        async def generator():
-            converter = Anthropic.StreamConverter(model)
-            response = OAIcompletions.stream_chat_completions(converted, is_legacy=False, stop_event=stop_event)
-            try:
-                async for resp in iterate_in_threadpool(response):
-                    disconnected = await request.is_disconnected()
-                    if disconnected:
-                        break
-
-                    for event in converter.process_chunk(resp):
-                        yield event
-
-                for event in converter.finish():
-                    yield event
-            except OpenAIError as e:
-                error_type = "invalid_request_error" if e.code < 500 else "api_error"
-                if e.code == 503:
-                    error_type = "overloaded_error"
-                yield {
-                    "event": "error",
-                    "data": json.dumps({"type": "error", "error": {"type": error_type, "message": e.message}})
-                }
-            finally:
-                stop_event.set()
-                response.close()
-
-        return EventSourceResponse(generator(), sep="\n")
-
-    else:
-        stop_event = threading.Event()
-        monitor = asyncio.create_task(_wait_for_disconnect(request, stop_event))
-        try:
-            openai_resp = await asyncio.to_thread(
-                OAIcompletions.chat_completions,
-                converted,
-                is_legacy=False,
-                stop_event=stop_event
-            )
-        finally:
-            stop_event.set()
-            monitor.cancel()
-
-        return JSONResponse(Anthropic.build_response(openai_resp, model))
-
-
 @app.get("/v1/models", dependencies=check_key)
 @app.get("/v1/models/{model}", dependencies=check_key)
 async def handle_models(request: Request):
@@ -346,7 +219,6 @@ def handle_billing_usage():
 @app.post('/v1/audio/transcriptions', dependencies=check_key)
 async def handle_audio_transcription(request: Request):
     import speech_recognition as sr
-    from pydub import AudioSegment
 
     r = sr.Recognizer()
 
@@ -378,7 +250,7 @@ async def handle_audio_transcription(request: Request):
 
 @app.post('/v1/images/generations', response_model=ImageGenerationResponse, dependencies=check_key)
 async def handle_image_generation(request_data: ImageGenerationRequest):
-    import modules.api.images as OAIimages
+    import extensions.openai.images as OAIimages
 
     response = await asyncio.to_thread(OAIimages.generations, request_data)
     return JSONResponse(response)
@@ -386,7 +258,7 @@ async def handle_image_generation(request_data: ImageGenerationRequest):
 
 @app.post("/v1/embeddings", response_model=EmbeddingsResponse, dependencies=check_key)
 async def handle_embeddings(request: Request, request_data: EmbeddingsRequest):
-    import modules.api.embeddings as OAIembeddings
+    import extensions.openai.embeddings as OAIembeddings
 
     input = request_data.input
     if not input:
@@ -401,7 +273,7 @@ async def handle_embeddings(request: Request, request_data: EmbeddingsRequest):
 
 @app.post("/v1/moderations", dependencies=check_key)
 async def handle_moderations(request: Request):
-    import modules.api.moderations as OAImoderations
+    import extensions.openai.moderations as OAImoderations
 
     body = await request.json()
     input = body["input"]
@@ -475,8 +347,10 @@ async def handle_list_models():
 @app.post("/v1/internal/model/load", dependencies=check_admin_key)
 async def handle_load_model(request_data: LoadModelRequest):
     '''
-    The "args" parameter can be used to modify loader flags before loading
-    a model. Example:
+    This endpoint is experimental and may change in the future.
+
+    The "args" parameter can be used to modify flags like "--load-in-4bit"
+    or "--n-gpu-layers" before loading a model. Example:
 
     ```
     "args": {
@@ -485,13 +359,18 @@ async def handle_load_model(request_data: LoadModelRequest):
     }
     ```
 
-    Loader args are reset to their startup defaults between loads, so
-    settings from a previous load do not leak into the next one.
+    Note that those settings will remain after loading the model. So you
+    may need to change them back to load a second model.
 
-    The "instruction_template" parameter sets the default instruction
-    template by name (from user_data/instruction-templates/). The
-    "instruction_template_str" parameter sets it as a raw Jinja2 string
-    and takes precedence over "instruction_template".
+    The "settings" parameter is also a dict but with keys for the
+    shared.settings object. It can be used to modify the default instruction
+    template like this:
+
+    ```
+    "settings": {
+      "instruction_template": "Alpaca"
+    }
+    ```
     '''
 
     try:
@@ -499,17 +378,12 @@ async def handle_load_model(request_data: LoadModelRequest):
         return JSONResponse(content="OK")
     except Exception:
         traceback.print_exc()
-        raise HTTPException(status_code=500, detail="Failed to load the model.")
+        raise HTTPException(status_code=400, detail="Failed to load the model.")
 
 
 @app.post("/v1/internal/model/unload", dependencies=check_admin_key)
 async def handle_unload_model():
-    try:
-        unload_model()
-        return JSONResponse(content="OK")
-    except Exception:
-        traceback.print_exc()
-        raise HTTPException(status_code=500, detail="Failed to unload the model.")
+    unload_model()
 
 
 @app.get("/v1/internal/lora/list", response_model=LoraListResponse, dependencies=check_admin_key)
@@ -537,8 +411,8 @@ async def handle_unload_loras():
 def find_available_port(starting_port):
     """Try the starting port, then find an available one if it's taken."""
     try:
+        # Try to create a socket with the starting port
         with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
-            s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
             s.bind(('', starting_port))
             return starting_port
     except OSError:
@@ -559,13 +433,10 @@ def run_server():
 
     # In the server configuration:
     server_addrs = []
-    if shared.args.listen and shared.args.listen_host:
-        server_addrs.append(shared.args.listen_host)
-    else:
-        if os.environ.get('OPENEDAI_ENABLE_IPV6', shared.args.api_enable_ipv6):
-            server_addrs.append('::' if shared.args.listen else '::1')
-        if not os.environ.get('OPENEDAI_DISABLE_IPV4', shared.args.api_disable_ipv4):
-            server_addrs.append('0.0.0.0' if shared.args.listen else '127.0.0.1')
+    if os.environ.get('OPENEDAI_ENABLE_IPV6', shared.args.api_enable_ipv6):
+        server_addrs.append('[::]' if shared.args.listen else '[::1]')
+    if not os.environ.get('OPENEDAI_DISABLE_IPV4', shared.args.api_disable_ipv4):
+        server_addrs.append('0.0.0.0' if shared.args.listen else '127.0.0.1')
 
     if not server_addrs:
         raise Exception('you MUST enable IPv6 or IPv4 for the API to work')
@@ -576,15 +447,15 @@ def run_server():
             port,
             shared.args.public_api_id,
             max_attempts=3,
-            on_start=lambda url: logger.info(f'OpenAI/Anthropic-compatible API URL:\n\n{url}/v1\n')
+            on_start=lambda url: logger.info(f'OpenAI-compatible API URL:\n\n{url}\n')
         )
     else:
         url_proto = 'https://' if (ssl_certfile and ssl_keyfile) else 'http://'
-        urls = [f'{url_proto}[{addr}]:{port}/v1' if ':' in addr else f'{url_proto}{addr}:{port}/v1' for addr in server_addrs]
+        urls = [f'{url_proto}{addr}:{port}' for addr in server_addrs]
         if len(urls) > 1:
-            logger.info('OpenAI/Anthropic-compatible API URLs:\n\n' + '\n'.join(urls) + '\n')
+            logger.info('OpenAI-compatible API URLs:\n\n' + '\n'.join(urls) + '\n')
         else:
-            logger.info('OpenAI/Anthropic-compatible API URL:\n\n' + '\n'.join(urls) + '\n')
+            logger.info('OpenAI-compatible API URL:\n\n' + '\n'.join(urls) + '\n')
 
     # Log API keys
     if shared.args.api_key:
@@ -601,15 +472,7 @@ def run_server():
     uvicorn.run(app, host=server_addrs, port=port, ssl_certfile=ssl_certfile, ssl_keyfile=ssl_keyfile, access_log=False)
 
 
-_server_started = False
-
-
 def setup():
-    global _server_started
-    if _server_started:
-        return
-
-    _server_started = True
     if shared.args.nowebui:
         run_server()
     else:

extensions/openai/typing.py

@@ -99,10 +99,6 @@ class ToolCall(BaseModel):
     function: FunctionCall
 
 
-class StreamOptions(BaseModel):
-    include_usage: bool | None = False
-
-
 class CompletionRequestParams(BaseModel):
     model: str | None = Field(default=None, description="Unused parameter. To change the model, use the /v1/internal/model/load endpoint.")
     prompt: str | List[str] | None = Field(default=None, description="Text prompt for completion. Can also use 'messages' format for multimodal.")
@@ -113,11 +109,10 @@ class CompletionRequestParams(BaseModel):
     logit_bias: dict | None = None
     logprobs: int | None = None
     max_tokens: int | None = 512
-    n: int | None = Field(default=1, description="Number of completions to generate. Only supported without streaming.")
+    n: int | None = Field(default=1, description="Unused parameter.")
     presence_penalty: float | None = shared.args.presence_penalty
     stop: str | List[str] | None = None
     stream: bool | None = False
-    stream_options: StreamOptions | None = None
     suffix: str | None = None
     temperature: float | None = shared.args.temperature
     top_p: float | None = shared.args.top_p
@@ -144,33 +139,22 @@ class CompletionResponse(BaseModel):
 
 
 class ChatCompletionRequestParams(BaseModel):
-    messages: List[dict] = Field(..., min_length=1)
+    messages: List[dict]
     model: str | None = Field(default=None, description="Unused parameter. To change the model, use the /v1/internal/model/load endpoint.")
     frequency_penalty: float | None = shared.args.frequency_penalty
     function_call: str | dict | None = Field(default=None, description="Unused parameter.")
     functions: List[dict] | None = Field(default=None, description="Unused parameter.")
     tools: List[dict] | None = Field(default=None, description="Tools signatures passed via MCP.")
-    tool_choice: str | dict | None = Field(default=None, description="Controls tool use: 'auto', 'none', 'required', or {\"type\": \"function\", \"function\": {\"name\": \"...\"}}.")
     logit_bias: dict | None = None
-    logprobs: bool | None = None
-    top_logprobs: int | None = None
     max_tokens: int | None = None
-    max_completion_tokens: int | None = None
     n: int | None = Field(default=1, description="Unused parameter.")
     presence_penalty: float | None = shared.args.presence_penalty
     stop: str | List[str] | None = None
     stream: bool | None = False
-    stream_options: StreamOptions | None = None
     temperature: float | None = shared.args.temperature
     top_p: float | None = shared.args.top_p
     user: str | None = Field(default=None, description="Unused parameter.")
 
-    @model_validator(mode='after')
-    def resolve_max_tokens(self):
-        if self.max_tokens is None and self.max_completion_tokens is not None:
-            self.max_tokens = self.max_completion_tokens
-        return self
-
     mode: str = Field(default='instruct', description="Valid options: instruct, chat, chat-instruct.")
 
     instruction_template: str | None = Field(default=None, description="An instruction template defined under text-generation-webui/user_data/instruction-templates. If not set, the correct template will be automatically obtained from the model metadata.")
@@ -271,8 +255,7 @@ class ModelListResponse(BaseModel):
 class LoadModelRequest(BaseModel):
     model_name: str
     args: dict | None = None
-    instruction_template: str | None = Field(default=None, description="An instruction template defined under text-generation-webui/user_data/instruction-templates. Sets the default template for all subsequent API requests.")
-    instruction_template_str: str | None = Field(default=None, description="A Jinja2 instruction template string. If set, takes precedence over instruction_template.")
+    settings: dict | None = None
 
 
 class LoraListResponse(BaseModel):
@@ -283,25 +266,6 @@ class LoadLorasRequest(BaseModel):
     lora_names: List[str]
 
 
-class AnthropicRequestParams(BaseModel):
-    model: str | None = None
-    messages: List[dict] = Field(..., min_length=1)
-    max_tokens: int
-    system: str | list | None = None
-    temperature: float | None = shared.args.temperature
-    top_p: float | None = shared.args.top_p
-    stop_sequences: list[str] | None = None
-    stream: bool = False
-    tools: list[dict] | None = None
-    tool_choice: dict | None = None
-    thinking: dict | None = None
-    metadata: dict | None = None
-
-
-class AnthropicRequest(GenerationOptions, AnthropicRequestParams):
-    pass
-
-
 class ImageGenerationRequest(BaseModel):
     """Image-specific parameters for generation."""
     prompt: str

extensions/openai/utils.py

@@ -0,0 +1,527 @@
+import base64
+import json
+import os
+import random
+import re
+import time
+import traceback
+from typing import Callable, Optional
+
+import numpy as np
+
+
+def float_list_to_base64(float_array: np.ndarray) -> str:
+    # Convert the list to a float32 array that the OpenAPI client expects
+    # float_array = np.array(float_list, dtype="float32")
+
+    # Get raw bytes
+    bytes_array = float_array.tobytes()
+
+    # Encode bytes into base64
+    encoded_bytes = base64.b64encode(bytes_array)
+
+    # Turn raw base64 encoded bytes into ASCII
+    ascii_string = encoded_bytes.decode('ascii')
+    return ascii_string
+
+
+def debug_msg(*args, **kwargs):
+    from extensions.openai.script import params
+    if os.environ.get("OPENEDAI_DEBUG", params.get('debug', 0)):
+        print(*args, **kwargs)
+
+
+def _start_cloudflared(port: int, tunnel_id: str, max_attempts: int = 3, on_start: Optional[Callable[[str], None]] = None):
+    try:
+        from flask_cloudflared import _run_cloudflared
+    except ImportError:
+        print('You should install flask_cloudflared manually')
+        raise Exception(
+            'flask_cloudflared not installed. Make sure you installed the requirements.txt for this extension.')
+
+    for _ in range(max_attempts):
+        try:
+            if tunnel_id is not None:
+                public_url = _run_cloudflared(port, port + 1, tunnel_id=tunnel_id)
+            else:
+                public_url = _run_cloudflared(port, port + 1)
+
+            if on_start:
+                on_start(public_url)
+
+            return
+        except Exception:
+            traceback.print_exc()
+            time.sleep(3)
+
+        raise Exception('Could not start cloudflared.')
+
+
+def getToolCallId() -> str:
+    letter_bytes = "abcdefghijklmnopqrstuvwxyz0123456789"
+    b = [random.choice(letter_bytes) for _ in range(8)]
+    return "call_" + "".join(b).lower()
+
+
+def checkAndSanitizeToolCallCandidate(candidate_dict: dict, tool_names: list[str]):
+    # check if property 'function' exists and is a dictionary, otherwise adapt dict
+    if 'function' not in candidate_dict and 'name' in candidate_dict and isinstance(candidate_dict['name'], str):
+        candidate_dict = {"type": "function", "function": candidate_dict}
+    if 'function' in candidate_dict and isinstance(candidate_dict['function'], str):
+        candidate_dict['name'] = candidate_dict['function']
+        del candidate_dict['function']
+        candidate_dict = {"type": "function", "function": candidate_dict}
+    if 'function' in candidate_dict and isinstance(candidate_dict['function'], dict):
+        # check if 'name' exists within 'function' and is part of known tools
+        if 'name' in candidate_dict['function'] and candidate_dict['function']['name'] in tool_names:
+            candidate_dict["type"] = "function"  # ensure required property 'type' exists and has the right value
+            # map property 'parameters' used by some older models to 'arguments'
+            if "arguments" not in candidate_dict["function"] and "parameters" in candidate_dict["function"]:
+                candidate_dict["function"]["arguments"] = candidate_dict["function"]["parameters"]
+                del candidate_dict["function"]["parameters"]
+            return candidate_dict
+    return None
+
+
+def _extractBalancedJson(text: str, start: int) -> str | None:
+    """Extract a balanced JSON object from text starting at the given position.
+
+    Walks through the string tracking brace depth and string boundaries
+    to correctly handle arbitrary nesting levels.
+    """
+    if start >= len(text) or text[start] != '{':
+        return None
+    depth = 0
+    in_string = False
+    escape_next = False
+    for i in range(start, len(text)):
+        c = text[i]
+        if escape_next:
+            escape_next = False
+            continue
+        if c == '\\' and in_string:
+            escape_next = True
+            continue
+        if c == '"':
+            in_string = not in_string
+            continue
+        if in_string:
+            continue
+        if c == '{':
+            depth += 1
+        elif c == '}':
+            depth -= 1
+            if depth == 0:
+                return text[start:i + 1]
+    return None
+
+
+def _parseChannelToolCalls(answer: str, tool_names: list[str]):
+    """Parse channel-based tool calls used by GPT-OSS and similar models.
+
+    Format:
+        <|channel|>commentary to=functions.func_name <|constrain|>json<|message|>{"arg": "value"}
+    """
+    matches = []
+    for m in re.finditer(
+        r'<\|channel\|>commentary to=functions\.([^<\s]+)\s*(?:<\|constrain\|>json)?<\|message\|>',
+        answer
+    ):
+        func_name = m.group(1).strip()
+        if func_name not in tool_names:
+            continue
+        json_str = _extractBalancedJson(answer, m.end())
+        if json_str is None:
+            continue
+        try:
+            arguments = json.loads(json_str)
+            matches.append({
+                "type": "function",
+                "function": {
+                    "name": func_name,
+                    "arguments": arguments
+                }
+            })
+        except json.JSONDecodeError:
+            pass
+    return matches
+
+
+def _parseBareNameToolCalls(answer: str, tool_names: list[str]):
+    """Parse bare function-name style tool calls used by Mistral and similar models.
+
+    Format:
+        functionName{"arg": "value"}
+    Multiple calls are concatenated directly or separated by whitespace.
+    """
+    matches = []
+    # Match tool name followed by opening brace, then extract balanced JSON
+    escaped_names = [re.escape(name) for name in tool_names]
+    pattern = r'(?:' + '|'.join(escaped_names) + r')\s*\{'
+    for match in re.finditer(pattern, answer):
+        text = match.group(0)
+        name = None
+        for n in tool_names:
+            if text.startswith(n):
+                name = n
+                break
+        if not name:
+            continue
+        brace_start = match.end() - 1
+        json_str = _extractBalancedJson(answer, brace_start)
+        if json_str is None:
+            continue
+        try:
+            arguments = json.loads(json_str)
+            matches.append({
+                "type": "function",
+                "function": {
+                    "name": name,
+                    "arguments": arguments
+                }
+            })
+        except json.JSONDecodeError:
+            pass
+    return matches
+
+
+def _parseXmlParamToolCalls(answer: str, tool_names: list[str]):
+    """Parse XML-parameter style tool calls used by Qwen3.5 and similar models.
+
+    Format:
+        <tool_call>
+        <function=function_name>
+        <parameter=param_name>value</parameter>
+        </function>
+        </tool_call>
+    """
+    matches = []
+    for tc_match in re.finditer(r'<tool_call>\s*(.*?)\s*</tool_call>', answer, re.DOTALL):
+        tc_content = tc_match.group(1)
+        func_match = re.search(r'<function=([^>]+)>', tc_content)
+        if not func_match:
+            continue
+        func_name = func_match.group(1).strip()
+        if func_name not in tool_names:
+            continue
+        arguments = {}
+        for param_match in re.finditer(r'<parameter=([^>]+)>\s*(.*?)\s*</parameter>', tc_content, re.DOTALL):
+            param_name = param_match.group(1).strip()
+            param_value = param_match.group(2).strip()
+            try:
+                param_value = json.loads(param_value)
+            except (json.JSONDecodeError, ValueError):
+                pass  # keep as string
+            arguments[param_name] = param_value
+        matches.append({
+            "type": "function",
+            "function": {
+                "name": func_name,
+                "arguments": arguments
+            }
+        })
+    return matches
+
+
+def _parseKimiToolCalls(answer: str, tool_names: list[str]):
+    """Parse Kimi-K2-style tool calls using pipe-delimited tokens.
+
+    Format:
+        <|tool_calls_section_begin|>
+        <|tool_call_begin|>functions.func_name:index<|tool_call_argument_begin|>{"arg": "value"}<|tool_call_end|>
+        <|tool_calls_section_end|>
+    """
+    matches = []
+    for m in re.finditer(
+        r'<\|tool_call_begin\|>\s*(?:functions\.)?(\S+?)(?::\d+)?\s*<\|tool_call_argument_begin\|>\s*',
+        answer
+    ):
+        func_name = m.group(1).strip()
+        if func_name not in tool_names:
+            continue
+        json_str = _extractBalancedJson(answer, m.end())
+        if json_str is None:
+            continue
+        try:
+            arguments = json.loads(json_str)
+            matches.append({
+                "type": "function",
+                "function": {
+                    "name": func_name,
+                    "arguments": arguments
+                }
+            })
+        except json.JSONDecodeError:
+            pass
+    return matches
+
+
+def _parseMiniMaxToolCalls(answer: str, tool_names: list[str]):
+    """Parse MiniMax-style tool calls using invoke/parameter XML tags.
+
+    Format:
+        <minimax:tool_call>
+        <invoke name="function_name">
+        <parameter name="param_name">value</parameter>
+        </invoke>
+        </minimax:tool_call>
+    """
+    matches = []
+    for tc_match in re.finditer(r'<minimax:tool_call>\s*(.*?)\s*</minimax:tool_call>', answer, re.DOTALL):
+        tc_content = tc_match.group(1)
+        # Split on <invoke> to handle multiple parallel calls in one block
+        for invoke_match in re.finditer(r'<invoke\s+name="([^"]+)">(.*?)</invoke>', tc_content, re.DOTALL):
+            func_name = invoke_match.group(1).strip()
+            if func_name not in tool_names:
+                continue
+            invoke_body = invoke_match.group(2)
+            arguments = {}
+            for param_match in re.finditer(r'<parameter\s+name="([^"]+)">\s*(.*?)\s*</parameter>', invoke_body, re.DOTALL):
+                param_name = param_match.group(1).strip()
+                param_value = param_match.group(2).strip()
+                try:
+                    param_value = json.loads(param_value)
+                except (json.JSONDecodeError, ValueError):
+                    pass  # keep as string
+                arguments[param_name] = param_value
+            matches.append({
+                "type": "function",
+                "function": {
+                    "name": func_name,
+                    "arguments": arguments
+                }
+            })
+    return matches
+
+
+def _parseDeepSeekToolCalls(answer: str, tool_names: list[str]):
+    """Parse DeepSeek-style tool calls using fullwidth Unicode token delimiters.
+
+    Format:
+        <｜tool▁calls▁begin｜><｜tool▁call▁begin｜>func_name<｜tool▁sep｜>{"arg": "value"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>
+    """
+    matches = []
+    for m in re.finditer(
+        r'<｜tool▁call▁begin｜>\s*(\S+?)\s*<｜tool▁sep｜>\s*',
+        answer
+    ):
+        func_name = m.group(1).strip()
+        if func_name not in tool_names:
+            continue
+        json_str = _extractBalancedJson(answer, m.end())
+        if json_str is None:
+            continue
+        try:
+            arguments = json.loads(json_str)
+            matches.append({
+                "type": "function",
+                "function": {
+                    "name": func_name,
+                    "arguments": arguments
+                }
+            })
+        except json.JSONDecodeError:
+            pass
+    return matches
+
+
+def _parseGlmToolCalls(answer: str, tool_names: list[str]):
+    """Parse GLM-style tool calls using arg_key/arg_value XML pairs.
+
+    Format:
+        <tool_call>function_name
+        <arg_key>key1</arg_key>
+        <arg_value>value1</arg_value>
+        </tool_call>
+    """
+    matches = []
+    for tc_match in re.finditer(r'<tool_call>\s*(.*?)\s*</tool_call>', answer, re.DOTALL):
+        tc_content = tc_match.group(1)
+        # First non-tag text is the function name
+        name_match = re.match(r'([^<\s]+)', tc_content.strip())
+        if not name_match:
+            continue
+        func_name = name_match.group(1).strip()
+        if func_name not in tool_names:
+            continue
+        # Extract arg_key/arg_value pairs
+        keys = [k.group(1).strip() for k in re.finditer(r'<arg_key>\s*(.*?)\s*</arg_key>', tc_content, re.DOTALL)]
+        vals = [v.group(1).strip() for v in re.finditer(r'<arg_value>\s*(.*?)\s*</arg_value>', tc_content, re.DOTALL)]
+        if len(keys) != len(vals):
+            continue
+        arguments = {}
+        for k, v in zip(keys, vals):
+            try:
+                v = json.loads(v)
+            except (json.JSONDecodeError, ValueError):
+                pass  # keep as string
+            arguments[k] = v
+        matches.append({
+            "type": "function",
+            "function": {
+                "name": func_name,
+                "arguments": arguments
+            }
+        })
+    return matches
+
+
+def _parsePythonicToolCalls(answer: str, tool_names: list[str]):
+    """Parse pythonic-style tool calls used by Llama 4 and similar models.
+
+    Format:
+        [func_name(param1="value1", param2="value2"), func_name2(...)]
+    """
+    matches = []
+    # Match a bracketed list of function calls
+    bracket_match = re.search(r'\[([^\[\]]+)\]', answer)
+    if not bracket_match:
+        return matches
+
+    inner = bracket_match.group(1)
+
+    # Build pattern for known tool names
+    escaped_names = [re.escape(name) for name in tool_names]
+    name_pattern = '|'.join(escaped_names)
+
+    for call_match in re.finditer(
+        r'(' + name_pattern + r')\(([^)]*)\)',
+        inner
+    ):
+        func_name = call_match.group(1)
+        params_str = call_match.group(2).strip()
+        arguments = {}
+
+        if params_str:
+            # Parse key="value" pairs, handling commas inside quoted values
+            for param_match in re.finditer(
+                r'(\w+)\s*=\s*("(?:[^"\\]|\\.)*"|\'(?:[^\'\\]|\\.)*\'|[^,\)]+)',
+                params_str
+            ):
+                param_name = param_match.group(1)
+                param_value = param_match.group(2).strip()
+                # Strip surrounding quotes
+                if (param_value.startswith('"') and param_value.endswith('"')) or \
+                   (param_value.startswith("'") and param_value.endswith("'")):
+                    param_value = param_value[1:-1]
+                # Try to parse as JSON for numeric/bool/null values
+                try:
+                    param_value = json.loads(param_value)
+                except (json.JSONDecodeError, ValueError):
+                    pass
+                arguments[param_name] = param_value
+
+        matches.append({
+            "type": "function",
+            "function": {
+                "name": func_name,
+                "arguments": arguments
+            }
+        })
+
+    return matches
+
+
+def parseToolCall(answer: str, tool_names: list[str]):
+    matches = []
+
+    # abort on very short answers to save computation cycles
+    if len(answer) < 10:
+        return matches
+
+    # Check for DeepSeek-style tool calls (fullwidth Unicode token delimiters)
+    matches = _parseDeepSeekToolCalls(answer, tool_names)
+    if matches:
+        return matches
+
+    # Check for Kimi-K2-style tool calls (pipe-delimited tokens)
+    matches = _parseKimiToolCalls(answer, tool_names)
+    if matches:
+        return matches
+
+    # Check for channel-based tool calls (e.g. GPT-OSS format)
+    matches = _parseChannelToolCalls(answer, tool_names)
+    if matches:
+        return matches
+
+    # Check for MiniMax-style tool calls (invoke/parameter XML tags)
+    matches = _parseMiniMaxToolCalls(answer, tool_names)
+    if matches:
+        return matches
+
+    # Check for GLM-style tool calls (arg_key/arg_value XML pairs)
+    matches = _parseGlmToolCalls(answer, tool_names)
+    if matches:
+        return matches
+
+    # Check for XML-parameter style tool calls (e.g. Qwen3.5 format)
+    matches = _parseXmlParamToolCalls(answer, tool_names)
+    if matches:
+        return matches
+
+    # Check for bare function-name style tool calls (e.g. Mistral format)
+    matches = _parseBareNameToolCalls(answer, tool_names)
+    if matches:
+        return matches
+
+    # Check for pythonic-style tool calls (e.g. Llama 4 format)
+    matches = _parsePythonicToolCalls(answer, tool_names)
+    if matches:
+        return matches
+
+    # Define the regex pattern to find the JSON content wrapped in <function>, <tools>, <tool_call>, and other tags observed from various models
+    patterns = [r"(```[^\n]*)\n(.*?)```", r"<([^>]+)>(.*?)</\1>"]
+
+    for pattern in patterns:
+        for match in re.finditer(pattern, answer, re.DOTALL):
+            # print(match.group(2))
+            if match.group(2) is None:
+                continue
+            # remove backtick wraps if present
+            candidate = re.sub(r"^```(json|xml|python[^\n]*)\n", "", match.group(2).strip())
+            candidate = re.sub(r"```$", "", candidate.strip())
+            # unwrap inner tags
+            candidate = re.sub(pattern, r"\2", candidate.strip(), flags=re.DOTALL)
+            # llm might have generated multiple json objects separated by linebreaks, check for this pattern and try parsing each object individually
+            if re.search(r"\}\s*\n\s*\{", candidate) is not None:
+                candidate = re.sub(r"\}\s*\n\s*\{", "},\n{", candidate)
+            if not candidate.strip().startswith("["):
+                candidate = "[" + candidate + "]"
+
+            candidates = []
+            try:
+                # parse the candidate JSON into a dictionary
+                candidates = json.loads(candidate)
+                if not isinstance(candidates, list):
+                    candidates = [candidates]
+            except json.JSONDecodeError:
+                # Ignore invalid JSON silently
+                continue
+
+            for candidate_dict in candidates:
+                checked_candidate = checkAndSanitizeToolCallCandidate(candidate_dict, tool_names)
+                if checked_candidate is not None:
+                    matches.append(checked_candidate)
+
+        # last resort if nothing has been mapped: LLM might have produced plain json tool call without xml-like tags
+        if len(matches) == 0:
+            try:
+                candidate = answer
+                # llm might have generated multiple json objects separated by linebreaks, check for this pattern and try parsing each object individually
+                if re.search(r"\}\s*\n\s*\{", candidate) is not None:
+                    candidate = re.sub(r"\}\s*\n\s*\{", "},\n{", candidate)
+                if not candidate.strip().startswith("["):
+                    candidate = "[" + candidate + "]"
+                # parse the candidate JSON into a dictionary
+                candidates = json.loads(candidate)
+                if not isinstance(candidates, list):
+                    candidates = [candidates]
+                for candidate_dict in candidates:
+                    checked_candidate = checkAndSanitizeToolCallCandidate(candidate_dict, tool_names)
+                    if checked_candidate is not None:
+                        matches.append(checked_candidate)
+            except json.JSONDecodeError:
+                # Ignore invalid JSON silently
+                pass
+
+    return matches

extensions/superbooga/download_urls.py

@@ -2,11 +2,8 @@ import concurrent.futures
 
 import requests
 
-from modules.web_search import _validate_url
-
 
 def download_single(url):
-    _validate_url(url)
     headers = {
         'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'
     }

extensions/superboogav2/api.py

@@ -107,7 +107,7 @@ class Handler(BaseHTTPRequestHandler):
 
             elif path in ['/api/v1/delete', '/api/delete']:
                 metadata = body.get('metadata')
-                if metadata is None:
+                if corpus is None:
                     self._send_412_error("Missing parameter 'metadata'")
                     return
 

extensions/superboogav2/download_urls.py

@@ -5,14 +5,12 @@ import requests
 from bs4 import BeautifulSoup
 
 import extensions.superboogav2.parameters as parameters
-from modules.web_search import _validate_url
 
 from .data_processor import process_and_add_to_collector
 from .utils import create_metadata_source
 
 
 def _download_single(url):
-    _validate_url(url)
     response = requests.get(url, timeout=5)
     if response.status_code == 200:
         return response.content

extensions/whisper_stt/readme.md

@@ -7,8 +7,8 @@ Allows you to enter your inputs in chat mode using your microphone.
 To adjust your default settings, you can add the following to your settings.yaml file.
 
 ```
-whisper_stt-whisper_language: chinese
-whisper_stt-whisper_model: tiny
+whisper_stt-whipser_language: chinese
+whisper_stt-whipser_model: tiny
 whisper_stt-auto_submit: False
 ```
 

extensions/whisper_stt/script.py

@@ -18,13 +18,13 @@ input_hijack = {
 
 # parameters which can be customized in settings.yaml of webui
 params = {
-    'whisper_language': 'english',
-    'whisper_model': 'small.en',
+    'whipser_language': 'english',
+    'whipser_model': 'small.en',
     'auto_submit': True
 }
 
 startup_device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-WHISPERMODEL = whisper.load_model(params['whisper_model'], device=startup_device)
+WHISPERMODEL = whisper.load_model(params['whipser_model'], device=startup_device)
 
 
 def chat_input_modifier(text, visible_text, state):
@@ -36,7 +36,7 @@ def chat_input_modifier(text, visible_text, state):
         return text, visible_text
 
 
-def do_stt(audio, whisper_language):
+def do_stt(audio, whipser_language):
     # use pydub to convert sample_rate and sample_width for whisper input
     dubaudio = AudioSegment.from_file(io.BytesIO(audio))
     dubaudio = dubaudio.set_channels(1)
@@ -46,20 +46,20 @@ def do_stt(audio, whisper_language):
     # same method to get the array as openai whisper repo used from wav file
     audio_np = np.frombuffer(dubaudio.raw_data, np.int16).flatten().astype(np.float32) / 32768.0
 
-    if len(whisper_language) == 0:
+    if len(whipser_language) == 0:
         result = WHISPERMODEL.transcribe(audio=audio_np)
     else:
-        result = WHISPERMODEL.transcribe(audio=audio_np, language=whisper_language)
+        result = WHISPERMODEL.transcribe(audio=audio_np, language=whipser_language)
     return result["text"]
 
 
-def auto_transcribe(audio, auto_submit, whisper_language):
+def auto_transcribe(audio, auto_submit, whipser_language):
     if audio is None or audio == "":
         print("Whisper received no audio data")
         return "", ""
     audio_bytes = base64.b64decode(audio.split(',')[1])
 
-    transcription = do_stt(audio_bytes, whisper_language)
+    transcription = do_stt(audio_bytes, whipser_language)
     if auto_submit:
         input_hijack.update({"state": True, "value": [transcription, transcription]})
     return transcription
@@ -78,7 +78,7 @@ def reload_whispermodel(whisper_model_name: str, whisper_language: str, device:
                 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
             WHISPERMODEL = whisper.load_model(whisper_model_name, device=device)
-            params.update({"whisper_model": whisper_model_name})
+            params.update({"whipser_model": whisper_model_name})
             if ".en" in whisper_model_name:
                 whisper_language = "english"
             audio_update = gr.Audio.update(interactive=True)
@@ -96,8 +96,8 @@ def ui():
             with gr.Accordion("Settings", open=False):
                 auto_submit = gr.Checkbox(label='Submit the transcribed audio automatically', value=params['auto_submit'])
                 device_dropd = gr.Dropdown(label='Device', value=str(startup_device), choices=["cuda", "cpu", "none"])
-                whisper_model_dropd = gr.Dropdown(label='Whisper Model', value=params['whisper_model'], choices=["tiny.en", "base.en", "small.en", "medium.en", "tiny", "base", "small", "medium", "large", "turbo"])
-                whisper_language = gr.Dropdown(label='Whisper Language', value=params['whisper_language'], choices=["english", "chinese", "german", "spanish", "russian", "korean", "french", "japanese", "portuguese", "turkish", "polish", "catalan", "dutch", "arabic", "swedish", "italian", "indonesian", "hindi", "finnish", "vietnamese", "hebrew", "ukrainian", "greek", "malay", "czech", "romanian", "danish", "hungarian", "tamil", "norwegian", "thai", "urdu", "croatian", "bulgarian", "lithuanian", "latin", "maori", "malayalam", "welsh", "slovak", "telugu", "persian", "latvian", "bengali", "serbian", "azerbaijani", "slovenian", "kannada", "estonian", "macedonian", "breton", "basque", "icelandic", "armenian", "nepali", "mongolian", "bosnian", "kazakh", "albanian", "swahili", "galician", "marathi", "punjabi", "sinhala", "khmer", "shona", "yoruba", "somali", "afrikaans", "occitan", "georgian", "belarusian", "tajik", "sindhi", "gujarati", "amharic", "yiddish", "lao", "uzbek", "faroese", "haitian creole", "pashto", "turkmen", "nynorsk", "maltese", "sanskrit", "luxembourgish", "myanmar", "tibetan", "tagalog", "malagasy", "assamese", "tatar", "hawaiian", "lingala", "hausa", "bashkir", "javanese", "sundanese"])
+                whisper_model_dropd = gr.Dropdown(label='Whisper Model', value=params['whipser_model'], choices=["tiny.en", "base.en", "small.en", "medium.en", "tiny", "base", "small", "medium", "large", "turbo"])
+                whisper_language = gr.Dropdown(label='Whisper Language', value=params['whipser_language'], choices=["english", "chinese", "german", "spanish", "russian", "korean", "french", "japanese", "portuguese", "turkish", "polish", "catalan", "dutch", "arabic", "swedish", "italian", "indonesian", "hindi", "finnish", "vietnamese", "hebrew", "ukrainian", "greek", "malay", "czech", "romanian", "danish", "hungarian", "tamil", "norwegian", "thai", "urdu", "croatian", "bulgarian", "lithuanian", "latin", "maori", "malayalam", "welsh", "slovak", "telugu", "persian", "latvian", "bengali", "serbian", "azerbaijani", "slovenian", "kannada", "estonian", "macedonian", "breton", "basque", "icelandic", "armenian", "nepali", "mongolian", "bosnian", "kazakh", "albanian", "swahili", "galician", "marathi", "punjabi", "sinhala", "khmer", "shona", "yoruba", "somali", "afrikaans", "occitan", "georgian", "belarusian", "tajik", "sindhi", "gujarati", "amharic", "yiddish", "lao", "uzbek", "faroese", "haitian creole", "pashto", "turkmen", "nynorsk", "maltese", "sanskrit", "luxembourgish", "myanmar", "tibetan", "tagalog", "malagasy", "assamese", "tatar", "hawaiian", "lingala", "hausa", "bashkir", "javanese", "sundanese"])
 
     audio.change(
         auto_transcribe, [audio, auto_submit, whisper_language], [shared.gradio['textbox']]).then(
@@ -105,7 +105,7 @@ def ui():
 
     device_dropd.input(reload_whispermodel, [whisper_model_dropd, whisper_language, device_dropd], [whisper_model_dropd, whisper_language, device_dropd, audio])
     whisper_model_dropd.change(reload_whispermodel, [whisper_model_dropd, whisper_language, device_dropd], [whisper_model_dropd, whisper_language, device_dropd, audio])
-    whisper_language.change(lambda x: params.update({"whisper_language": x}), whisper_language, None)
+    whisper_language.change(lambda x: params.update({"whipser_language": x}), whisper_language, None)
     auto_submit.change(lambda x: params.update({"auto_submit": x}), auto_submit, None)
 
 

js/dark_theme.js

@@ -1,6 +1,6 @@
 function toggleDarkMode() {
   document.body.classList.toggle("dark");
-  const currentCSS = document.getElementById("highlight-css");
+  var currentCSS = document.getElementById("highlight-css");
   if (currentCSS.getAttribute("href") === "file/css/highlightjs/github-dark.min.css") {
     currentCSS.setAttribute("href", "file/css/highlightjs/github.min.css");
   } else {
@@ -9,10 +9,12 @@ function toggleDarkMode() {
 
   // Re-highlight all code blocks once stylesheet loads
   currentCSS.onload = function() {
-    // Clear data-highlighted so hljs will re-process with the new theme
-    document.querySelectorAll("#chat .message-body pre code[data-highlighted]").forEach((codeBlock) => {
-      delete codeBlock.dataset.highlighted;
+    const messageBodies = document.getElementById("chat").querySelectorAll(".message-body");
+    messageBodies.forEach((messageBody) => {
+      const codeBlocks = messageBody.querySelectorAll("pre code");
+      codeBlocks.forEach((codeBlock) => {
+        hljs.highlightElement(codeBlock);
+      });
     });
-    doSyntaxHighlighting();
   };
 }

js/global_scope_js.js

@@ -1,35 +1,11 @@
-// -------------------------------------------------
-// Shared helpers
-// -------------------------------------------------
-
-function getProfilePictureUrl() {
-  return "/file/user_data/cache/pfp_character.png?time=" + Date.now();
-}
-
-const MESSAGE_SELECTOR = ".message, .user-message, .assistant-message";
-
-function getMessageElement(element) {
-  if (!element) return null;
-  return element.closest(MESSAGE_SELECTOR);
-}
-
-function isUserRole(messageElement) {
-  return messageElement.classList.contains("user-message") ||
-         messageElement.querySelector(".text-you") !== null ||
-         messageElement.querySelector(".circle-you") !== null;
-}
-
-// Trigger a synthetic 'input' event so Gradio picks up programmatic value changes
-function dispatchGradioInput(element) {
-  element.dispatchEvent(new Event("input", { bubbles: true }));
-}
-
 // -------------------------------------------------
 // Event handlers
 // -------------------------------------------------
 
 function copyToClipboard(element) {
-  const messageElement = getMessageElement(element);
+  if (!element) return;
+
+  const messageElement = element.closest(".message, .user-message, .assistant-message");
   if (!messageElement) return;
 
   const rawText = messageElement.getAttribute("data-raw");
@@ -72,7 +48,9 @@ function fallbackCopyToClipboard(text) {
 }
 
 function branchHere(element) {
-  const messageElement = getMessageElement(element);
+  if (!element) return;
+
+  const messageElement = element.closest(".message, .user-message, .assistant-message");
   if (!messageElement) return;
 
   const index = messageElement.getAttribute("data-index");
@@ -91,7 +69,11 @@ function branchHere(element) {
   }
 
   branchIndexInput.value = index;
-  dispatchGradioInput(branchIndexInput);
+
+  // Trigger any 'change' or 'input' events Gradio might be listening for
+  const event = new Event("input", { bubbles: true });
+  branchIndexInput.dispatchEvent(event);
+
   branchButton.click();
 }
 
@@ -100,7 +82,9 @@ function branchHere(element) {
 // -------------------------------------------------
 
 function editHere(buttonElement) {
-  const messageElement = getMessageElement(buttonElement);
+  if (!buttonElement) return;
+
+  const messageElement = buttonElement.closest(".message, .user-message, .assistant-message");
   if (!messageElement) return;
 
   const messageBody = messageElement.querySelector(".message-body");
@@ -113,7 +97,12 @@ function editHere(buttonElement) {
     return;
   }
 
-  startEditing(messageElement, messageBody, isUserRole(messageElement));
+  // Determine role based on message element - handle different chat modes
+  const isUserMessage = messageElement.classList.contains("user-message") ||
+                       messageElement.querySelector(".text-you") !== null ||
+                       messageElement.querySelector(".circle-you") !== null;
+
+  startEditing(messageElement, messageBody, isUserMessage);
 }
 
 function startEditing(messageElement, messageBody, isUserMessage) {
@@ -220,22 +209,30 @@ function submitMessageEdit(index, newText, isUserMessage) {
   editTextInput.value = newText;
   editRoleInput.value = isUserMessage ? "user" : "assistant";
 
-  dispatchGradioInput(editIndexInput);
-  dispatchGradioInput(editTextInput);
-  dispatchGradioInput(editRoleInput);
+  editIndexInput.dispatchEvent(new Event("input", { bubbles: true }));
+  editTextInput.dispatchEvent(new Event("input", { bubbles: true }));
+  editRoleInput.dispatchEvent(new Event("input", { bubbles: true }));
 
   editButton.click();
   return true;
 }
 
 function navigateVersion(element, direction) {
-  const messageElement = getMessageElement(element);
+  if (!element) return;
+
+  const messageElement = element.closest(".message, .user-message, .assistant-message");
   if (!messageElement) return;
 
   const index = messageElement.getAttribute("data-index");
   if (!index) return;
 
-  const role = isUserRole(messageElement) ? "user" : "assistant";
+  // Determine role based on message element classes
+  let role = "assistant"; // Default role
+  if (messageElement.classList.contains("user-message") ||
+      messageElement.querySelector(".text-you") ||
+      messageElement.querySelector(".circle-you")) {
+    role = "user";
+  }
 
   const indexInput = document.getElementById("Navigate-message-index")?.querySelector("input");
   const directionInput = document.getElementById("Navigate-direction")?.querySelector("textarea");
@@ -251,9 +248,11 @@ function navigateVersion(element, direction) {
   directionInput.value = direction;
   roleInput.value = role;
 
-  dispatchGradioInput(indexInput);
-  dispatchGradioInput(directionInput);
-  dispatchGradioInput(roleInput);
+  // Trigger 'input' events for Gradio to pick up changes
+  const event = new Event("input", { bubbles: true });
+  indexInput.dispatchEvent(event);
+  directionInput.dispatchEvent(event);
+  roleInput.dispatchEvent(event);
 
   navigateButton.click();
 }
@@ -270,51 +269,9 @@ function removeLastClick() {
   document.getElementById("Remove-last").click();
 }
 
-function autoScrollToBottom() {
-  if (!window.isScrolled) {
-    const chatParent = document.getElementById("chat")?.parentNode?.parentNode?.parentNode;
-    if (chatParent) {
-      const maxScroll = chatParent.scrollHeight - chatParent.clientHeight;
-      if (maxScroll > 0 && chatParent.scrollTop < maxScroll - 1) {
-        chatParent.scrollTop = maxScroll;
-      }
-    }
-  }
-}
-
-function updateInstructPadding() {
-  const chatElement = document.getElementById("chat");
-  if (chatElement && chatElement.getAttribute("data-mode") === "instruct") {
-    const messagesContainer = chatElement.querySelector(".messages");
-    const lastChild = messagesContainer?.lastElementChild;
-    const prevSibling = lastChild?.previousElementSibling;
-    if (lastChild && prevSibling && chatElement.offsetHeight > 0) {
-      let bufferHeight = Math.max(0, Math.max(window.innerHeight - 128 - 84, window.innerHeight - prevSibling.offsetHeight - 84) - lastChild.offsetHeight);
-      if (window.innerWidth <= 924) {
-        bufferHeight = Math.max(0, bufferHeight - 32);
-      }
-      messagesContainer.style.paddingBottom = `${bufferHeight}px`;
-    }
-  }
-}
-
-let pendingMorphdomData = null;
-let morphdomRafId = null;
-
 function handleMorphdomUpdate(data) {
-  pendingMorphdomData = data;
-  if (!morphdomRafId) {
-    morphdomRafId = requestAnimationFrame(() => {
-      morphdomRafId = null;
-      applyMorphdomUpdate(pendingMorphdomData);
-      pendingMorphdomData = null;
-    });
-  }
-}
-
-function applyMorphdomUpdate(data) {
   // Determine target element and use it as query scope
-  let target_element, target_html;
+  var target_element, target_html;
   if (data.last_message_only) {
     const childNodes = document.getElementsByClassName("messages")[0].childNodes;
     target_element = childNodes[childNodes.length - 1];
@@ -326,21 +283,27 @@ function applyMorphdomUpdate(data) {
 
   const queryScope = target_element;
 
-  // Track open blocks and store their scroll positions
+  // Track open blocks
   const openBlocks = new Set();
-  const scrollPositions = {};
   queryScope.querySelectorAll(".thinking-block").forEach(block => {
     const blockId = block.getAttribute("data-block-id");
+    // If block exists and is open, add to open set
     if (blockId && block.hasAttribute("open")) {
       openBlocks.add(blockId);
-      const content = block.querySelector(".thinking-content");
-      if (content) {
-        const isAtBottom = Math.abs((content.scrollHeight - content.scrollTop) - content.clientHeight) < 5;
-        scrollPositions[blockId] = {
-          position: content.scrollTop,
-          isAtBottom: isAtBottom
-        };
-      }
+    }
+  });
+
+  // Store scroll positions for any open blocks
+  const scrollPositions = {};
+  queryScope.querySelectorAll(".thinking-block[open]").forEach(block => {
+    const content = block.querySelector(".thinking-content");
+    const blockId = block.getAttribute("data-block-id");
+    if (content && blockId) {
+      const isAtBottom = Math.abs((content.scrollHeight - content.scrollTop) - content.clientHeight) < 5;
+      scrollPositions[blockId] = {
+        position: content.scrollTop,
+        isAtBottom: isAtBottom
+      };
     }
   });
 
@@ -350,8 +313,8 @@ function applyMorphdomUpdate(data) {
     {
       onBeforeElUpdated: function(fromEl, toEl) {
         // Preserve code highlighting
-        if (fromEl.tagName === "PRE") {
-          const fromCode = fromEl.querySelector("code[data-highlighted]");
+        if (fromEl.tagName === "PRE" && fromEl.querySelector("code[data-highlighted]")) {
+          const fromCode = fromEl.querySelector("code");
           const toCode = toEl.querySelector("code");
 
           if (fromCode && toCode && fromCode.textContent === toCode.textContent) {
@@ -396,23 +359,10 @@ function applyMorphdomUpdate(data) {
     }
   );
 
-  // Syntax highlighting and LaTeX
-  if (window.doSyntaxHighlighting) {
-    window.doSyntaxHighlighting();
-  }
-
-  // Auto-scroll runs both before and after padding update.
-  // Before: so content growth isn't hidden by padding absorption.
-  // After: so padding-added space is also scrolled into view.
-  autoScrollToBottom();
-  updateInstructPadding();
-  autoScrollToBottom();
-
   // Add toggle listeners for new blocks
   queryScope.querySelectorAll(".thinking-block").forEach(block => {
     if (!block._hasToggleListener) {
       block.addEventListener("toggle", function(e) {
-        const wasScrolled = window.isScrolled;
         if (this.open) {
           const content = this.querySelector(".thinking-content");
           if (content) {
@@ -421,12 +371,6 @@ function applyMorphdomUpdate(data) {
             }, 0);
           }
         }
-        autoScrollToBottom();
-        updateInstructPadding();
-        autoScrollToBottom();
-        // Restore scroll state so the browser's layout adjustment
-        // from the toggle doesn't disable auto-scroll
-        window.isScrolled = wasScrolled;
       });
       block._hasToggleListener = true;
     }

js/main.js

@@ -2,13 +2,6 @@
 // Main
 // ------------------------------------------------
 
-// Sync highlight.js theme with the actual Gradio theme
-var defined_hljs_css = document.body.classList.contains("dark") ? "file/css/highlightjs/github-dark.min.css" : "file/css/highlightjs/github.min.css";
-var hljsCssElement = document.getElementById("highlight-css");
-if (hljsCssElement.getAttribute("href") !== defined_hljs_css) {
-  hljsCssElement.setAttribute("href", defined_hljs_css);
-}
-
 let main_parent = document.getElementById("chat-tab").parentNode;
 let extensions = document.getElementById("extensions");
 
@@ -50,18 +43,21 @@ document.querySelector(".header_bar").addEventListener("click", function(event)
 //------------------------------------------------
 
 // --- Helper functions --- //
-function isModifiedKeyboardEvent(event) {
-  return event instanceof KeyboardEvent &&
-    (event.shiftKey || event.ctrlKey || event.altKey || event.metaKey);
+function isModifiedKeyboardEvent() {
+  return (event instanceof KeyboardEvent &&
+    event.shiftKey ||
+    event.ctrlKey ||
+    event.altKey ||
+    event.metaKey);
 }
 
-function isFocusedOnEditableTextbox(event) {
+function isFocusedOnEditableTextbox() {
   if (event.target.tagName === "INPUT" || event.target.tagName === "TEXTAREA") {
     return !!event.target.value;
   }
-  return false;
 }
 
+let previousTabId = "chat-tab-button";
 document.addEventListener("keydown", function(event) {
   // Stop generation on Esc pressed
   if (event.key === "Escape") {
@@ -115,14 +111,14 @@ document.addEventListener("keydown", function(event) {
   }
 
   // --- Simple version navigation --- //
-  if (!isFocusedOnEditableTextbox(event)) {
+  if (!isFocusedOnEditableTextbox()) {
     // Version navigation on Arrow keys (horizontal)
-    if (!isModifiedKeyboardEvent(event) && event.key === "ArrowLeft") {
+    if (!isModifiedKeyboardEvent() && event.key === "ArrowLeft") {
       event.preventDefault();
       navigateLastAssistantMessage("left");
     }
 
-    else if (!isModifiedKeyboardEvent(event) && event.key === "ArrowRight") {
+    else if (!isModifiedKeyboardEvent() && event.key === "ArrowRight") {
       event.preventDefault();
       if (!navigateLastAssistantMessage("right")) {
         // If can't navigate right (last version), regenerate
@@ -149,26 +145,21 @@ targetElement.classList.add("pretty_scrollbar");
 targetElement.classList.add("chat-parent");
 window.isScrolled = false;
 let scrollTimeout;
-let lastScrollTop = 0;
-let lastScrollHeight = 0;
-let lastClientHeight = 0;
 
 targetElement.addEventListener("scroll", function() {
   let diff = targetElement.scrollHeight - targetElement.clientHeight;
-  let isAtBottomNow = Math.abs(targetElement.scrollTop - diff) <= 10 || diff <= 0;
+  let isAtBottomNow = Math.abs(targetElement.scrollTop - diff) <= 10 || diff == 0;
 
+  // Add scrolling class to disable hover effects
   if (window.isScrolled || !isAtBottomNow) {
-    targetElement.classList.add("scrolling"); // Disables hover effects during scroll
+    targetElement.classList.add("scrolling");
   }
 
   if(isAtBottomNow) {
     window.isScrolled = false;
-  } else if (targetElement.scrollTop < lastScrollTop && targetElement.scrollHeight >= lastScrollHeight && targetElement.clientHeight <= lastClientHeight) {
+  } else {
     window.isScrolled = true;
   }
-  lastScrollTop = targetElement.scrollTop;
-  lastScrollHeight = targetElement.scrollHeight;
-  lastClientHeight = targetElement.clientHeight;
 
   // Clear previous timeout and set new one
   clearTimeout(scrollTimeout);
@@ -179,28 +170,65 @@ targetElement.addEventListener("scroll", function() {
 });
 
 // Create a MutationObserver instance
-const observer = new MutationObserver(function() {
+const observer = new MutationObserver(function(mutations) {
+  // Check if this is just the scrolling class being toggled
+  const isScrollingClassOnly = mutations.every(mutation =>
+    mutation.type === "attributes" &&
+    mutation.attributeName === "class" &&
+    mutation.target === targetElement
+  );
+
   if (targetElement.classList.contains("_generating")) {
     typing.parentNode.classList.add("visible-dots");
     document.getElementById("stop").style.display = "flex";
     document.getElementById("Generate").style.display = "none";
-    // If the user is near the bottom, ensure auto-scroll is enabled
-    // for the new reply. This catches cases where isScrolled was
-    // incorrectly set to true by layout shifts during page load, etc.
-    const diff = targetElement.scrollHeight - targetElement.clientHeight;
-    if (Math.abs(targetElement.scrollTop - diff) <= 10 || diff <= 0) {
-      window.isScrolled = false;
-    }
   } else {
     typing.parentNode.classList.remove("visible-dots");
     document.getElementById("stop").style.display = "none";
     document.getElementById("Generate").style.display = "flex";
   }
+
+  doSyntaxHighlighting();
+
+  if (!window.isScrolled && !isScrollingClassOnly) {
+    const maxScroll = targetElement.scrollHeight - targetElement.clientHeight;
+    if (maxScroll > 0 && targetElement.scrollTop < maxScroll - 1) {
+      targetElement.scrollTop = maxScroll;
+    }
+  }
+
+  const chatElement = document.getElementById("chat");
+  if (chatElement && chatElement.getAttribute("data-mode") === "instruct") {
+    const messagesContainer = chatElement.querySelector(".messages");
+    const lastChild = messagesContainer?.lastElementChild;
+    const prevSibling = lastChild?.previousElementSibling;
+    if (lastChild && prevSibling) {
+      // Add padding to the messages container to create room for the last message.
+      // The purpose of this is to avoid constant scrolling during streaming in
+      // instruct mode.
+      let bufferHeight = Math.max(0, Math.max(window.innerHeight - 128 - 84, window.innerHeight - prevSibling.offsetHeight - 84) - lastChild.offsetHeight);
+
+      // Subtract header height when screen width is <= 924px
+      if (window.innerWidth <= 924) {
+        bufferHeight = Math.max(0, bufferHeight - 32);
+      }
+
+      messagesContainer.style.paddingBottom = `${bufferHeight}px`;
+    }
+  }
 });
 
-// Only watch for attribute changes on targetElement (e.g. _generating class)
+// Configure the observer to watch for changes in the subtree and attributes
+const config = {
+  childList: true,
+  subtree: true,
+  characterData: true,
+  attributeOldValue: true,
+  characterDataOldValue: true
+};
+
 // Start observing the target element
-observer.observe(targetElement, { attributes: true });
+observer.observe(targetElement, config);
 
 //------------------------------------------------
 // Handle syntax highlighting / LaTeX
@@ -215,89 +243,77 @@ function isElementVisibleOnScreen(element) {
   );
 }
 
-window.doSyntaxHighlighting = function() {
+function doSyntaxHighlighting() {
   const messageBodies = document.getElementById("chat").querySelectorAll(".message-body");
 
   if (messageBodies.length > 0) {
-    let hasSeenVisible = false;
+    observer.disconnect();
 
-    // Go from last message to first so we can early-exit once past visible area
-    for (let i = messageBodies.length - 1; i >= 0; i--) {
-      const messageBody = messageBodies[i];
+    try {
+      let hasSeenVisible = false;
 
-      if (isElementVisibleOnScreen(messageBody)) {
-        hasSeenVisible = true;
+      // Go from last message to first
+      for (let i = messageBodies.length - 1; i >= 0; i--) {
+        const messageBody = messageBodies[i];
 
-        // Handle both code and math in a single pass through each message
-        const codeBlocks = messageBody.querySelectorAll("pre code:not([data-highlighted])");
-        codeBlocks.forEach((codeBlock) => {
-          hljs.highlightElement(codeBlock);
-          codeBlock.setAttribute("data-highlighted", "true");
-          codeBlock.classList.add("pretty_scrollbar");
-        });
+        if (isElementVisibleOnScreen(messageBody)) {
+          hasSeenVisible = true;
 
-        const mathContainers = messageBody.querySelectorAll("p, span, li, td, th, h1, h2, h3, h4, h5, h6, blockquote, figcaption, caption, dd, dt");
-        // Only render math in individually visible containers (the outer check is on the message body)
-        mathContainers.forEach(container => {
-          if (isElementVisibleOnScreen(container)) {
-            renderMathInElement(container, {
-              delimiters: [
-                { left: "$$", right: "$$", display: true },
-                { left: "$", right: "$", display: false },
-                { left: "\\(", right: "\\)", display: false },
-                { left: "\\[", right: "\\]", display: true },
-              ],
-            });
-          }
-        });
-      } else if (hasSeenVisible) {
-      // We've seen visible messages but this one is not visible
-      // Since we're going from last to first, we can break
-        break;
+          // Handle both code and math in a single pass through each message
+          const codeBlocks = messageBody.querySelectorAll("pre code:not([data-highlighted])");
+          codeBlocks.forEach((codeBlock) => {
+            hljs.highlightElement(codeBlock);
+            codeBlock.setAttribute("data-highlighted", "true");
+            codeBlock.classList.add("pretty_scrollbar");
+          });
+
+          // Only render math in visible elements
+          const mathContainers = messageBody.querySelectorAll("p, span, li, td, th, h1, h2, h3, h4, h5, h6, blockquote, figcaption, caption, dd, dt");
+          mathContainers.forEach(container => {
+            if (isElementVisibleOnScreen(container)) {
+              renderMathInElement(container, {
+                delimiters: [
+                  { left: "$$", right: "$$", display: true },
+                  { left: "$", right: "$", display: false },
+                  { left: "\\(", right: "\\)", display: false },
+                  { left: "\\[", right: "\\]", display: true },
+                ],
+              });
+            }
+          });
+        } else if (hasSeenVisible) {
+        // We've seen visible messages but this one is not visible
+        // Since we're going from last to first, we can break
+          break;
+        }
       }
+    } finally {
+      observer.observe(targetElement, config);
     }
   }
 }
-const doSyntaxHighlighting = window.doSyntaxHighlighting;
 
 //------------------------------------------------
 // Add some scrollbars
 //------------------------------------------------
-const scrollbarElements = document.querySelectorAll(".add_scrollbar textarea, .add_scrollbar .drag-drop-list");
-for(let i = 0; i < scrollbarElements.length; i++) {
-  scrollbarElements[i].classList.remove("scroll-hide");
-  scrollbarElements[i].classList.add("pretty_scrollbar");
-  scrollbarElements[i].style.resize = "none";
-}
-
-
-//------------------------------------------------
-// Tools: inject "Refresh list" link into the label
-//------------------------------------------------
-const toolsTitle = document.querySelector("#tools-group > [data-testid='block-info']");
-const toolsInfo = toolsTitle ? toolsTitle.nextElementSibling : null;
-if (toolsInfo) {
-  const refreshLink = document.createElement("span");
-  refreshLink.textContent = " [Refresh list]";
-  refreshLink.className = "tools-refresh-link";
-  refreshLink.addEventListener("click", function(e) {
-    e.preventDefault();
-    document.querySelector("#tools-refresh-btn").click();
-  });
-  toolsInfo.appendChild(refreshLink);
+const textareaElements = document.querySelectorAll(".add_scrollbar textarea");
+for(i = 0; i < textareaElements.length; i++) {
+  textareaElements[i].classList.remove("scroll-hide");
+  textareaElements[i].classList.add("pretty_scrollbar");
+  textareaElements[i].style.resize = "none";
 }
 
 //------------------------------------------------
 // Remove some backgrounds
 //------------------------------------------------
 const noBackgroundelements = document.querySelectorAll(".no-background");
-for(let i = 0; i < noBackgroundelements.length; i++) {
+for(i = 0; i < noBackgroundelements.length; i++) {
   noBackgroundelements[i].parentNode.style.border = "none";
   noBackgroundelements[i].parentNode.parentNode.parentNode.style.alignItems = "center";
 }
 
 const slimDropdownElements = document.querySelectorAll(".slim-dropdown");
-for (let i = 0; i < slimDropdownElements.length; i++) {
+for (i = 0; i < slimDropdownElements.length; i++) {
   const parentNode = slimDropdownElements[i].parentNode;
   parentNode.style.background = "transparent";
   parentNode.style.border = "0";
@@ -309,19 +325,18 @@ for (let i = 0; i < slimDropdownElements.length; i++) {
 // https://github.com/SillyTavern/SillyTavern/blob/6c8bd06308c69d51e2eb174541792a870a83d2d6/public/script.js
 //------------------------------------------------
 var buttonsInChat = document.querySelectorAll("#chat-tab #chat-buttons button, #chat-tab #chat-buttons #show-controls");
-var hoverContainer = document.getElementById("gr-hover-container");
 var button = document.getElementById("hover-element-button");
 var menu = document.getElementById("hover-menu");
 var istouchscreen = (navigator.maxTouchPoints > 0) || "ontouchstart" in document.documentElement;
 
 function showMenu() {
-  menu.style.display = "flex";
+  menu.style.display = "flex"; // Show the menu
 }
 
 function hideMenu() {
-  menu.style.display = "none";
+  menu.style.display = "none"; // Hide the menu
   if (!istouchscreen) {
-    document.querySelector("#chat-input textarea").focus();
+    document.querySelector("#chat-input textarea").focus(); // Focus on the chat input
   }
 }
 
@@ -330,6 +345,7 @@ if (buttonsInChat.length > 0) {
     const thisButton = buttonsInChat[i];
     menu.appendChild(thisButton);
 
+    // Only apply transformations to button elements
     if (thisButton.tagName.toLowerCase() === "button") {
       thisButton.addEventListener("click", () => {
         hideMenu();
@@ -339,6 +355,7 @@ if (buttonsInChat.length > 0) {
       const matches = buttonText.match(/(\(.*?\))/);
 
       if (matches && matches.length > 1) {
+        // Apply the transparent-substring class to the matched substring
         const substring = matches[1];
         const newText = buttonText.replace(substring, `&nbsp;<span class="transparent-substring">${substring.slice(1, -1)}</span>`);
         thisButton.innerHTML = newText;
@@ -347,19 +364,16 @@ if (buttonsInChat.length > 0) {
   }
 }
 
-var menuInteracting = false;
+function isMouseOverButtonOrMenu() {
+  return menu.matches(":hover") || button.matches(":hover");
+}
 
-hoverContainer.addEventListener("mouseenter", function () {
+button.addEventListener("mouseenter", function () {
   if (!istouchscreen) {
     showMenu();
   }
 });
 
-hoverContainer.addEventListener("mousedown", function () {
-  menuInteracting = true;
-  setTimeout(function () { menuInteracting = false; }, 300);
-});
-
 button.addEventListener("click", function () {
   if (menu.style.display === "flex") {
     hideMenu();
@@ -369,39 +383,49 @@ button.addEventListener("click", function () {
   }
 });
 
-hoverContainer.addEventListener("mouseleave", function () {
-  if (!istouchscreen) {
-    setTimeout(function () {
-      if (!hoverContainer.matches(":hover") && !menu.matches(":hover")) {
-        hideMenu();
-      }
-    }, 50);
-  }
+// Add event listener for mouseleave on the button
+button.addEventListener("mouseleave", function () {
+  // Delay to prevent menu hiding when the mouse leaves the button into the menu
+  setTimeout(function () {
+    if (!isMouseOverButtonOrMenu()) {
+      hideMenu();
+    }
+  }, 100);
+});
+
+// Add event listener for mouseleave on the menu
+menu.addEventListener("mouseleave", function () {
+  // Delay to prevent menu hide when the mouse leaves the menu into the button
+  setTimeout(function () {
+    if (!isMouseOverButtonOrMenu()) {
+      hideMenu();
+    }
+  }, 100);
 });
 
 // Add event listener for click anywhere in the document
 document.addEventListener("click", function (event) {
+  const target = event.target;
+
   // Check if the click is outside the button/menu and the menu is visible
-  if (!menuInteracting && !event.target.closest("#gr-hover-container") && menu.style.display === "flex") {
+  if (!isMouseOverButtonOrMenu() && menu.style.display === "flex") {
     hideMenu();
   }
 
-  const target = event.target;
-
-  if (target.classList.contains("pfp_character")) {
+  if (event.target.classList.contains("pfp_character")) {
     toggleBigPicture();
   }
 
   // Handle sidebar clicks on mobile
   if (isMobile()) {
-    // Check if the click did NOT originate from any of the specified toggle buttons or elements
+  // Check if the click did NOT originate from any of the specified toggle buttons or elements
     if (
       target.closest("#navigation-toggle") !== navigationToggle &&
-      target.closest("#past-chats-toggle") !== pastChatsToggle &&
-      target.closest("#chat-controls-toggle") !== chatControlsToggle &&
-      target.closest(".header_bar") !== headerBar &&
-      target.closest("#past-chats-row") !== pastChatsRow &&
-      target.closest("#chat-controls") !== chatControlsRow
+    target.closest("#past-chats-toggle") !== pastChatsToggle &&
+    target.closest("#chat-controls-toggle") !== chatControlsToggle &&
+    target.closest(".header_bar") !== headerBar &&
+    target.closest("#past-chats-row") !== pastChatsRow &&
+    target.closest("#chat-controls") !== chatControlsRow
     ) {
       handleIndividualSidebarClose(event);
     }
@@ -418,19 +442,27 @@ document.getElementById("chat-input-row").classList.add("chat-input-positioned")
 //------------------------------------------------
 const chatTextArea = document.getElementById("chat-input").querySelector("textarea");
 
-function focusOnVisible(element) {
-  var observer = new IntersectionObserver((entries) => {
+function respondToChatInputVisibility(element, callback) {
+  var options = {
+    root: document.documentElement,
+  };
+
+  var observer = new IntersectionObserver((entries, observer) => {
     entries.forEach(entry => {
-      if (entry.intersectionRatio > 0) {
-        element.focus();
-      }
+      callback(entry.intersectionRatio > 0);
     });
-  }, { root: document.documentElement });
+  }, options);
 
   observer.observe(element);
 }
 
-focusOnVisible(chatTextArea);
+function handleChatInputVisibilityChange(isVisible) {
+  if (isVisible) {
+    chatTextArea.focus();
+  }
+}
+
+respondToChatInputVisibility(chatTextArea, handleChatInputVisibilityChange);
 
 //------------------------------------------------
 // Show enlarged character picture when the profile
@@ -440,7 +472,8 @@ let bigPictureVisible = false;
 
 function addBigPicture() {
   var imgElement = document.createElement("img");
-  imgElement.src = getProfilePictureUrl();
+  var timestamp = new Date().getTime();
+  imgElement.src = "/file/user_data/cache/pfp_character.png?time=" + timestamp;
   imgElement.classList.add("bigProfilePicture");
   imgElement.addEventListener("load", function () {
     this.style.visibility = "visible";
@@ -454,8 +487,9 @@ function addBigPicture() {
 }
 
 function deleteBigPicture() {
-  document.querySelectorAll(".bigProfilePicture").forEach(function (element) {
-    element.remove();
+  var bigProfilePictures = document.querySelectorAll(".bigProfilePicture");
+  bigProfilePictures.forEach(function (element) {
+    element.parentNode.removeChild(element);
   });
 }
 
@@ -469,11 +503,44 @@ function toggleBigPicture() {
   }
 }
 
+//------------------------------------------------
+// Handle the chat input box growth
+//------------------------------------------------
+
+// Cache DOM elements
+const chatContainer = document.getElementById("chat").parentNode.parentNode.parentNode;
+const chatInput = document.querySelector("#chat-input textarea");
+
+// Variables to store current dimensions
+let currentChatInputHeight = chatInput.clientHeight;
+
 //------------------------------------------------
 // Focus on the rename text area when it becomes visible
 //------------------------------------------------
 const renameTextArea = document.getElementById("rename-row").querySelector("textarea");
-focusOnVisible(renameTextArea);
+
+function respondToRenameVisibility(element, callback) {
+  var options = {
+    root: document.documentElement,
+  };
+
+  var observer = new IntersectionObserver((entries, observer) => {
+    entries.forEach(entry => {
+      callback(entry.intersectionRatio > 0);
+    });
+  }, options);
+
+  observer.observe(element);
+}
+
+
+function handleVisibilityChange(isVisible) {
+  if (isVisible) {
+    renameTextArea.focus();
+  }
+}
+
+respondToRenameVisibility(renameTextArea, handleVisibilityChange);
 
 //------------------------------------------------
 // Adjust the chat tab margin if no extension UI
@@ -494,38 +561,6 @@ document.querySelectorAll(".focus-on-chat-input").forEach(element => {
   });
 });
 
-//------------------------------------------------
-// "New chat" hover menu with incognito option
-//------------------------------------------------
-
-(function() {
-  const newChatBtn = document.getElementById("new-chat-btn");
-
-  const wrapper = document.createElement("div");
-  wrapper.id = "new-chat-wrapper";
-  newChatBtn.replaceWith(wrapper);
-  wrapper.appendChild(newChatBtn);
-
-  const arrow = document.createElement("span");
-  arrow.className = "new-chat-arrow";
-  arrow.textContent = "\u25BE";
-
-  const menu = document.createElement("div");
-  menu.className = "new-chat-menu";
-  const option = document.createElement("div");
-  option.className = "new-chat-menu-item";
-  option.textContent = "Incognito chat";
-  menu.appendChild(option);
-
-  arrow.appendChild(menu);
-  wrapper.appendChild(arrow);
-
-  option.addEventListener("click", function(e) {
-    e.stopPropagation();
-    document.querySelector("#incognito-chat-btn").click();
-  });
-})();
-
 //------------------------------------------------
 // Fix a border around the "past chats" menu
 //------------------------------------------------
@@ -679,21 +714,21 @@ function handleIndividualSidebarClose(event) {
 
   // Close navigation bar if click is outside and it is open
   if (!headerBar.contains(target) && !headerBar.classList.contains("sidebar-hidden")) {
-    toggleSidebar(headerBar, navigationToggle);
+    toggleSidebar(headerBar, navigationToggle, true);
   }
 
   // Close past chats row if click is outside and it is open
   if (!pastChatsRow.contains(target) && !pastChatsRow.classList.contains("sidebar-hidden")) {
-    toggleSidebar(pastChatsRow, pastChatsToggle);
+    toggleSidebar(pastChatsRow, pastChatsToggle, true);
   }
 
   // Close chat controls row if click is outside and it is open
   if (!chatControlsRow.contains(target) && !chatControlsRow.classList.contains("sidebar-hidden")) {
-    toggleSidebar(chatControlsRow, chatControlsToggle);
+    toggleSidebar(chatControlsRow, chatControlsToggle, true);
   }
 }
 
-function toggleSidebar(sidebar, toggle) {
+function toggleSidebar(sidebar, toggle, forceClose = false) {
   const isCurrentlyHidden = sidebar.classList.contains("sidebar-hidden");
   const shouldClose = !isCurrentlyHidden;
 
@@ -718,6 +753,11 @@ function toggleSidebar(sidebar, toggle) {
     toggle.classList.toggle("chat-controls-open", !shouldClose);
     toggle.innerHTML = shouldClose ? leftArrowSVG : rightArrowSVG;
   }
+
+  // Mobile handling
+  if (isMobile()) {
+    sidebar.classList.toggle("sidebar-shown", !shouldClose);
+  }
 }
 
 // Function to check if the device is mobile
@@ -777,17 +817,17 @@ pastChatsToggle.addEventListener("click", () => {
   const isCurrentlyOpen = !pastChatsRow.classList.contains("sidebar-hidden");
   toggleSidebar(pastChatsRow, pastChatsToggle);
 
-  // On desktop, sync both sidebars together
+  // On desktop, open/close both sidebars at the same time
   if (!isMobile()) {
     if (isCurrentlyOpen) {
       // If we just closed the left sidebar, also close the right sidebar
       if (!chatControlsRow.classList.contains("sidebar-hidden")) {
-        toggleSidebar(chatControlsRow, chatControlsToggle);
+        toggleSidebar(chatControlsRow, chatControlsToggle, true);
       }
     } else {
       // If we just opened the left sidebar, also open the right sidebar
       if (chatControlsRow.classList.contains("sidebar-hidden")) {
-        toggleSidebar(chatControlsRow, chatControlsToggle);
+        toggleSidebar(chatControlsRow, chatControlsToggle, false);
       }
     }
   }
@@ -797,17 +837,17 @@ chatControlsToggle.addEventListener("click", () => {
   const isCurrentlyOpen = !chatControlsRow.classList.contains("sidebar-hidden");
   toggleSidebar(chatControlsRow, chatControlsToggle);
 
-  // On desktop, sync both sidebars together
+  // On desktop, open/close both sidebars at the same time
   if (!isMobile()) {
     if (isCurrentlyOpen) {
       // If we just closed the right sidebar, also close the left sidebar
       if (!pastChatsRow.classList.contains("sidebar-hidden")) {
-        toggleSidebar(pastChatsRow, pastChatsToggle);
+        toggleSidebar(pastChatsRow, pastChatsToggle, true);
       }
     } else {
       // If we just opened the right sidebar, also open the left sidebar
       if (pastChatsRow.classList.contains("sidebar-hidden")) {
-        toggleSidebar(pastChatsRow, pastChatsToggle);
+        toggleSidebar(pastChatsRow, pastChatsToggle, false);
       }
     }
   }
@@ -827,7 +867,7 @@ if (isMobile()) {
   const textarea = document.querySelector("#chat-input textarea");
 
   if (textarea) {
-    // Force textarea height recalculation by simulating content change
+    // Simulate adding and removing a newline
     textarea.value += "\n";
     textarea.dispatchEvent(new Event("input", { bubbles: true }));
     textarea.value = textarea.value.slice(0, -1);
@@ -1050,13 +1090,15 @@ document.fonts.addEventListener("loadingdone", (event) => {
     const currentHeight = chatInputRow.offsetHeight;
     const heightDifference = currentHeight - originalHeight;
     chatParent.style.marginBottom = `${originalMarginBottom + heightDifference}px`;
-    if (!window.isScrolled) {
-      chatParent.scrollTop = chatParent.scrollHeight - chatParent.clientHeight;
-    }
   }
 
-  // Watch for size changes that affect height
-  new ResizeObserver(updateMargin).observe(chatInputRow);
+  // Watch for changes that might affect height
+  const observer = new MutationObserver(updateMargin);
+  observer.observe(chatInputRow, {
+    childList: true,
+    subtree: true,
+    attributes: true
+  });
 
   // Also listen for window resize
   window.addEventListener("resize", updateMargin);

js/save_files.js

@@ -1,9 +1,10 @@
 // Functions for downloading JSON files
 function getCurrentTimestamp() {
   const now = new Date();
-  const timezoneOffset = now.getTimezoneOffset() * 60000; // Convert minutes to milliseconds
+  const timezoneOffset = now.getTimezoneOffset() * 60000; // Convert to milliseconds
   const localTime = new Date(now.getTime() - timezoneOffset);
-  return localTime.toISOString().replace(/[-:]/g, "").slice(0, 15);
+  const formattedTimestamp = localTime.toISOString().replace(/[-:]/g, "").slice(0, 15);
+  return formattedTimestamp;
 }
 
 function saveFile(contents, filename) {
@@ -17,18 +18,23 @@ function saveFile(contents, filename) {
 }
 
 function saveHistory(history, character, mode) {
-  let path;
+  let path = null;
 
   if (["chat", "chat-instruct"].includes(mode) && character && character.trim() !== "") {
     path = `history_${character}_${getCurrentTimestamp()}.json`;
   } else {
-    path = `history_${mode || "unknown"}_${getCurrentTimestamp()}.json`;
+    try {
+      path = `history_${mode}_${getCurrentTimestamp()}.json`;
+    } catch (error) {
+      path = `history_${getCurrentTimestamp()}.json`;
+    }
   }
-
   saveFile(history, path);
 }
 
 function saveSession(session) {
-  const path = `session_${getCurrentTimestamp()}.json`;
+  let path = null;
+
+  path = `session_${getCurrentTimestamp()}.json`;
   saveFile(session, path);
 }

js/show_controls.js

@@ -1,11 +1,13 @@
+const chatParent = document.querySelector(".chat-parent");
+
 function toggle_controls(value) {
-  const navToggle = document.getElementById("navigation-toggle");
-  const pastChatsToggle = document.getElementById("past-chats-toggle");
   const extensions = document.querySelector("#extensions");
-  const galleryExtension = document.getElementById("gallery-extension");
 
   if (value) {
     // SHOW MODE: Click toggles to show hidden sidebars
+    const navToggle = document.getElementById("navigation-toggle");
+    const pastChatsToggle = document.getElementById("past-chats-toggle");
+
     if (navToggle && document.querySelector(".header_bar")?.classList.contains("sidebar-hidden")) {
       navToggle.click();
     }
@@ -17,11 +19,17 @@ function toggle_controls(value) {
     if (extensions) {
       extensions.style.display = "inherit";
     }
-    if (galleryExtension) {
-      galleryExtension.style.display = "block";
+
+    let gallery_element = document.getElementById("gallery-extension");
+    if (gallery_element) {
+      gallery_element.style.display = "block";
     }
+
   } else {
     // HIDE MODE: Click toggles to hide visible sidebars
+    const navToggle = document.getElementById("navigation-toggle");
+    const pastChatsToggle = document.getElementById("past-chats-toggle");
+
     if (navToggle && !document.querySelector(".header_bar")?.classList.contains("sidebar-hidden")) {
       navToggle.click();
     }
@@ -33,8 +41,5 @@ function toggle_controls(value) {
     if (extensions) {
       extensions.style.display = "none";
     }
-    if (galleryExtension) {
-      galleryExtension.style.display = "none";
-    }
   }
 }

js/switch_tabs.js

@@ -2,9 +2,17 @@ function scrollToTop() {
   window.scrollTo({ top: 0 });
 }
 
-function findButtonsByText(buttonText, container = document) {
-  return Array.from(container.getElementsByTagName("button"))
-    .filter(btn => btn.textContent.trim() === buttonText);
+function findButtonsByText(buttonText) {
+  const buttons = document.getElementsByTagName("button");
+  const matchingButtons = [];
+
+  for (let i = 0; i < buttons.length; i++) {
+    if (buttons[i].textContent.trim() === buttonText) {
+      matchingButtons.push(buttons[i]);
+    }
+  }
+
+  return matchingButtons;
 }
 
 function switch_to_chat() {
@@ -31,9 +39,13 @@ function switch_to_character() {
 
 function switch_to_image_ai_generate() {
   const container = document.querySelector("#image-ai-tab");
-  const generateBtn = findButtonsByText("Generate", container)[0];
-  if (generateBtn) {
-    generateBtn.click();
+  const buttons = container.getElementsByTagName("button");
+
+  for (let i = 0; i < buttons.length; i++) {
+    if (buttons[i].textContent.trim() === "Generate") {
+      buttons[i].click();
+      break;
+    }
   }
 
   scrollToTop();

js/update_big_picture.js

@@ -1,6 +1,7 @@
 function updateBigPicture() {
   var existingElement = document.querySelector(".bigProfilePicture");
   if (existingElement) {
-    existingElement.src = getProfilePictureUrl();
+    var timestamp = new Date().getTime();
+    existingElement.src = "/file/user_data/cache/pfp_character.png?time=" + timestamp;
   }
 }

modules/api/anthropic.py

@@ -1,468 +0,0 @@
-import json
-import time
-
-from modules import shared
-
-
-def convert_request(body: dict) -> dict:
-    """Transform Anthropic Messages API body into the dict that chat_completions_common expects."""
-    messages = []
-
-    # System message
-    system = body.get('system')
-    if system:
-        if isinstance(system, list):
-            # List of content blocks like [{"type":"text","text":"..."}]
-            text_parts = [block.get('text', '') for block in system if isinstance(block, dict) and block.get('type') == 'text']
-            system_text = '\n'.join(text_parts)
-        else:
-            system_text = str(system)
-        if system_text:
-            messages.append({"role": "system", "content": system_text})
-
-    # Convert messages
-    for msg in body.get('messages', []):
-        role = msg.get('role')
-        content = msg.get('content')
-
-        if isinstance(content, str):
-            messages.append({"role": role, "content": content})
-            continue
-
-        if not isinstance(content, list):
-            messages.append({"role": role, "content": str(content) if content else ""})
-            continue
-
-        if role == 'assistant':
-            # Split into text content, tool_calls, and skip thinking blocks
-            text_parts = []
-            tool_calls = []
-            for block in content:
-                btype = block.get('type')
-                if btype == 'text':
-                    text_parts.append(block.get('text', ''))
-                elif btype == 'tool_use':
-                    tool_calls.append({
-                        "id": block.get('id', ''),
-                        "type": "function",
-                        "function": {
-                            "name": block.get('name', ''),
-                            "arguments": json.dumps(block.get('input', {}))
-                        }
-                    })
-                elif btype == 'thinking':
-                    pass  # Strip thinking blocks
-
-            assistant_msg = {"role": "assistant", "content": '\n'.join(text_parts) if text_parts else ""}
-            if tool_calls:
-                assistant_msg["tool_calls"] = tool_calls
-            messages.append(assistant_msg)
-
-        elif role == 'user':
-            # Handle tool_result blocks and regular content
-            regular_parts = []
-            for block in content:
-                btype = block.get('type')
-                if btype == 'tool_result':
-                    # Emit any accumulated regular content first
-                    if regular_parts:
-                        if len(regular_parts) == 1 and regular_parts[0].get('type') == 'text':
-                            messages.append({"role": "user", "content": regular_parts[0]['text']})
-                        else:
-                            messages.append({"role": "user", "content": regular_parts})
-                        regular_parts = []
-                    # Convert tool_result to OpenAI tool message
-                    tool_content = block.get('content', '')
-                    if isinstance(tool_content, list):
-                        tool_content = '\n'.join(
-                            b.get('text', '') for b in tool_content
-                            if isinstance(b, dict) and b.get('type') == 'text'
-                        )
-                    messages.append({
-                        "role": "tool",
-                        "tool_call_id": block.get('tool_use_id', ''),
-                        "content": str(tool_content)
-                    })
-                elif btype == 'text':
-                    regular_parts.append({"type": "text", "text": block.get('text', '')})
-                elif btype == 'image':
-                    source = block.get('source', {})
-                    if source.get('type') == 'base64':
-                        media_type = source.get('media_type', 'image/png')
-                        data = source.get('data', '')
-                        regular_parts.append({
-                            "type": "image_url",
-                            "image_url": {"url": f"data:{media_type};base64,{data}"}
-                        })
-                elif btype == 'thinking':
-                    pass  # Strip thinking blocks
-
-            if regular_parts:
-                if len(regular_parts) == 1 and regular_parts[0].get('type') == 'text':
-                    messages.append({"role": "user", "content": regular_parts[0]['text']})
-                else:
-                    messages.append({"role": "user", "content": regular_parts})
-        else:
-            messages.append({"role": role, "content": str(content)})
-
-    # Start with all fields from the original body (includes GenerationOptions defaults)
-    result = dict(body)
-
-    # Remove Anthropic-specific fields that don't map directly
-    for key in ('system', 'stop_sequences', 'tools', 'tool_choice', 'thinking', 'metadata'):
-        result.pop(key, None)
-
-    # Set converted fields
-    result['messages'] = messages
-    result['max_tokens'] = body.get('max_tokens', 4096)
-    result['stream'] = body.get('stream', False)
-    result['mode'] = 'instruct'
-
-    # Ensure ChatCompletionRequestParams defaults are present
-    result.setdefault('continue_', False)
-    result.setdefault('instruction_template', None)
-    result.setdefault('instruction_template_str', None)
-    result.setdefault('character', None)
-    result.setdefault('bot_name', None)
-    result.setdefault('context', None)
-    result.setdefault('greeting', None)
-    result.setdefault('user_name', None)
-    result.setdefault('user_bio', None)
-    result.setdefault('chat_template_str', None)
-    result.setdefault('chat_instruct_command', 'Continue the chat dialogue below. Write a single reply for the character "<|character|>".\n\n<|prompt|>')
-    result.setdefault('frequency_penalty', None)
-    result.setdefault('presence_penalty', None)
-    result.setdefault('logit_bias', None)
-    result.setdefault('logprobs', None)
-    result.setdefault('top_logprobs', None)
-    result.setdefault('n', 1)
-    result.setdefault('model', None)
-    result.setdefault('functions', None)
-    result.setdefault('function_call', None)
-    result.setdefault('stream_options', None)
-    result.setdefault('user', None)
-    result.setdefault('stop', None)
-    result.setdefault('tool_choice', None)
-
-    # Always request usage in streaming so the usage-only chunk triggers
-    # the deferred message_delta/message_stop with accurate output_tokens
-    if body.get('stream', False):
-        result['stream_options'] = {'include_usage': True}
-
-    # Map stop_sequences -> stop
-    if body.get('stop_sequences'):
-        result['stop'] = body['stop_sequences']
-
-    # Tools
-    if body.get('tools'):
-        result['tools'] = [
-            {
-                "type": "function",
-                "function": {
-                    "name": t.get('name', ''),
-                    "description": t.get('description', ''),
-                    "parameters": t.get('input_schema', {"type": "object", "properties": {}})
-                }
-            }
-            for t in body['tools']
-        ]
-
-    # Tool choice
-    tc = body.get('tool_choice')
-    if tc and isinstance(tc, dict):
-        tc_type = tc.get('type')
-        if tc_type == 'auto':
-            result['tool_choice'] = 'auto'
-        elif tc_type == 'any':
-            result['tool_choice'] = 'required'
-        elif tc_type == 'tool':
-            result['tool_choice'] = {"type": "function", "function": {"name": tc.get('name', '')}}
-        elif tc_type == 'none':
-            result['tool_choice'] = 'none'
-    else:
-        result.setdefault('tool_choice', None)
-
-    # Thinking
-    thinking = body.get('thinking')
-    if thinking and isinstance(thinking, dict) and thinking.get('type') in ('enabled', 'adaptive'):
-        result['enable_thinking'] = True
-
-    return result
-
-
-_FINISH_REASON_MAP = {
-    "stop": "end_turn",
-    "length": "max_tokens",
-    "tool_calls": "tool_use",
-}
-
-
-def build_response(openai_resp: dict, model: str) -> dict:
-    """Transform OpenAI chat completion response dict into Anthropic Messages format."""
-    resp_id = openai_resp.get('id', 'msg_unknown')
-    if resp_id.startswith('chatcmpl-'):
-        resp_id = 'msg_' + resp_id[9:]
-
-    choice = openai_resp.get('choices', [{}])[0]
-    message = choice.get('message', {})
-
-    content = []
-
-    # Reasoning/thinking content
-    reasoning = message.get('reasoning_content')
-    if reasoning:
-        content.append({"type": "thinking", "thinking": reasoning, "signature": ""})
-
-    # Text content
-    text = message.get('content')
-    if text:
-        content.append({"type": "text", "text": text})
-
-    # Tool calls
-    tool_calls = message.get('tool_calls')
-    if tool_calls:
-        for tc in tool_calls:
-            func = tc.get('function', {})
-            try:
-                input_data = json.loads(func.get('arguments', '{}'))
-            except (json.JSONDecodeError, TypeError):
-                input_data = {}
-            content.append({
-                "type": "tool_use",
-                "id": tc.get('id', ''),
-                "name": func.get('name', ''),
-                "input": input_data
-            })
-
-    finish_reason = choice.get('finish_reason', 'stop')
-    stop_reason = _FINISH_REASON_MAP.get(finish_reason, 'end_turn')
-
-    usage = openai_resp.get('usage', {})
-
-    return {
-        "id": resp_id,
-        "type": "message",
-        "role": "assistant",
-        "content": content,
-        "model": model,
-        "stop_reason": stop_reason,
-        "stop_sequence": None,
-        "usage": {
-            "input_tokens": usage.get('prompt_tokens', 0),
-            "output_tokens": usage.get('completion_tokens', 0),
-        }
-    }
-
-
-class StreamConverter:
-    """Stateful converter: processes one OpenAI chunk at a time, yields Anthropic SSE events.
-
-    When include_usage is enabled in the OpenAI request, the final chunk with
-    finish_reason has usage=None, followed by a separate usage-only chunk
-    (choices=[], usage={...}).  We defer emitting message_delta and message_stop
-    until we receive that usage chunk so output_tokens is accurate.
-    """
-
-    def __init__(self, model: str):
-        self.model = model
-        self.msg_id = "msg_%d" % int(time.time() * 1000000000)
-        self.block_index = 0
-        self.in_thinking = False
-        self.in_text = False
-        self.input_tokens = 0
-        self.output_tokens = 0
-        self.tool_calls_accum = {}
-        self.stop_reason = "end_turn"
-        self._pending_finish = False  # True after we've seen finish_reason
-
-    def process_chunk(self, chunk: dict) -> list[dict]:
-        """Process a single OpenAI streaming chunk; return list of Anthropic SSE event dicts."""
-        events = []
-        choices = chunk.get('choices', [])
-        usage = chunk.get('usage')
-
-        if usage:
-            self.input_tokens = usage.get('prompt_tokens', self.input_tokens)
-            self.output_tokens = usage.get('completion_tokens', self.output_tokens)
-
-        # Usage-only chunk (choices=[]) arrives after the finish chunk
-        if not choices:
-            if self._pending_finish:
-                events.extend(self.finish())
-            return events
-
-        choice = choices[0]
-        delta = choice.get('delta', {})
-        finish_reason = choice.get('finish_reason')
-
-        # First chunk with role
-        if 'role' in delta:
-            events.append({
-                "event": "message_start",
-                "data": json.dumps({
-                    "type": "message_start",
-                    "message": {
-                        "id": self.msg_id,
-                        "type": "message",
-                        "role": "assistant",
-                        "content": [],
-                        "model": self.model,
-                        "stop_reason": None,
-                        "stop_sequence": None,
-                        "usage": {"input_tokens": self.input_tokens, "output_tokens": 0}
-                    }
-                })
-            })
-            events.append({"event": "ping", "data": json.dumps({"type": "ping"})})
-            return events
-
-        # Reasoning content
-        reasoning_content = delta.get('reasoning_content')
-        if reasoning_content:
-            if not self.in_thinking:
-                self.in_thinking = True
-                events.append({
-                    "event": "content_block_start",
-                    "data": json.dumps({
-                        "type": "content_block_start",
-                        "index": self.block_index,
-                        "content_block": {"type": "thinking", "thinking": "", "signature": ""}
-                    })
-                })
-            events.append({
-                "event": "content_block_delta",
-                "data": json.dumps({
-                    "type": "content_block_delta",
-                    "index": self.block_index,
-                    "delta": {"type": "thinking_delta", "thinking": reasoning_content}
-                })
-            })
-            return events
-
-        # Text content
-        text_content = delta.get('content')
-        if text_content:
-            if self.in_thinking:
-                events.append({
-                    "event": "content_block_stop",
-                    "data": json.dumps({"type": "content_block_stop", "index": self.block_index})
-                })
-                self.in_thinking = False
-                self.block_index += 1
-
-            if not self.in_text:
-                self.in_text = True
-                events.append({
-                    "event": "content_block_start",
-                    "data": json.dumps({
-                        "type": "content_block_start",
-                        "index": self.block_index,
-                        "content_block": {"type": "text", "text": ""}
-                    })
-                })
-            events.append({
-                "event": "content_block_delta",
-                "data": json.dumps({
-                    "type": "content_block_delta",
-                    "index": self.block_index,
-                    "delta": {"type": "text_delta", "text": text_content}
-                })
-            })
-            return events
-
-        # Tool calls in delta
-        chunk_tool_calls = delta.get('tool_calls')
-        if chunk_tool_calls:
-            for tc in chunk_tool_calls:
-                tc_id = tc.get('id', '')
-                tc_idx = tc.get('index', 0)
-                func = tc.get('function', {})
-                if tc_id:
-                    self.tool_calls_accum[tc_idx] = {
-                        "id": tc_id,
-                        "name": func.get('name', ''),
-                        "arguments": func.get('arguments', '')
-                    }
-                elif tc_idx in self.tool_calls_accum:
-                    self.tool_calls_accum[tc_idx]["arguments"] += func.get('arguments', '')
-
-        # Final chunk — close open content blocks, defer message_delta/stop for usage
-        if finish_reason is not None:
-            self.stop_reason = _FINISH_REASON_MAP.get(finish_reason, 'end_turn')
-
-            if self.in_thinking:
-                events.append({
-                    "event": "content_block_stop",
-                    "data": json.dumps({"type": "content_block_stop", "index": self.block_index})
-                })
-                self.in_thinking = False
-                self.block_index += 1
-
-            if self.in_text:
-                events.append({
-                    "event": "content_block_stop",
-                    "data": json.dumps({"type": "content_block_stop", "index": self.block_index})
-                })
-                self.in_text = False
-                self.block_index += 1
-
-            for tc_idx in sorted(self.tool_calls_accum.keys()):
-                tc = self.tool_calls_accum[tc_idx]
-                arguments_str = tc["arguments"] or "{}"
-
-                events.append({
-                    "event": "content_block_start",
-                    "data": json.dumps({
-                        "type": "content_block_start",
-                        "index": self.block_index,
-                        "content_block": {
-                            "type": "tool_use",
-                            "id": tc["id"],
-                            "name": tc["name"],
-                            "input": {}
-                        }
-                    })
-                })
-                # Emit the full input as a single input_json_delta so SDK
-                # clients that reconstruct from deltas get the correct data
-                events.append({
-                    "event": "content_block_delta",
-                    "data": json.dumps({
-                        "type": "content_block_delta",
-                        "index": self.block_index,
-                        "delta": {
-                            "type": "input_json_delta",
-                            "partial_json": arguments_str
-                        }
-                    })
-                })
-                events.append({
-                    "event": "content_block_stop",
-                    "data": json.dumps({"type": "content_block_stop", "index": self.block_index})
-                })
-                self.block_index += 1
-
-            # Defer message_delta/stop — usage chunk may follow
-            self._pending_finish = True
-
-        return events
-
-    def finish(self) -> list[dict]:
-        """Emit deferred message_delta and message_stop. Safe to call multiple times."""
-        if not self._pending_finish:
-            return []
-        self._pending_finish = False
-        return [
-            {
-                "event": "message_delta",
-                "data": json.dumps({
-                    "type": "message_delta",
-                    "delta": {"stop_reason": self.stop_reason, "stop_sequence": None},
-                    "usage": {"input_tokens": self.input_tokens, "output_tokens": self.output_tokens}
-                })
-            },
-            {
-                "event": "message_stop",
-                "data": json.dumps({"type": "message_stop"})
-            }
-        ]

modules/api/utils.py

@@ -1,53 +0,0 @@
-import base64
-import os
-import time
-import traceback
-from typing import Callable, Optional
-
-import numpy as np
-
-
-def float_list_to_base64(float_array: np.ndarray) -> str:
-    # Convert the list to a float32 array that the OpenAPI client expects
-    # float_array = np.array(float_list, dtype="float32")
-
-    # Get raw bytes
-    bytes_array = float_array.tobytes()
-
-    # Encode bytes into base64
-    encoded_bytes = base64.b64encode(bytes_array)
-
-    # Turn raw base64 encoded bytes into ASCII
-    ascii_string = encoded_bytes.decode('ascii')
-    return ascii_string
-
-
-def debug_msg(*args, **kwargs):
-    if int(os.environ.get("OPENEDAI_DEBUG", 0)):
-        print(*args, **kwargs)
-
-
-def _start_cloudflared(port: int, tunnel_id: str, max_attempts: int = 3, on_start: Optional[Callable[[str], None]] = None):
-    try:
-        from flask_cloudflared import _run_cloudflared
-    except ImportError:
-        print('You should install flask_cloudflared manually')
-        raise Exception(
-            'flask_cloudflared not installed. Make sure you installed the requirements.txt for this extension.')
-
-    for _ in range(max_attempts):
-        try:
-            if tunnel_id is not None:
-                public_url = _run_cloudflared(port, port + 1, tunnel_id=tunnel_id)
-            else:
-                public_url = _run_cloudflared(port, port + 1)
-
-            if on_start:
-                on_start(public_url)
-
-            return
-        except Exception:
-            traceback.print_exc()
-            time.sleep(3)
-
-    raise Exception('Could not start cloudflared.')

modules/callbacks.py

@@ -1,8 +1,8 @@
+import traceback
 from queue import Queue
 from threading import Thread
 
 import modules.shared as shared
-from modules.logging_colors import logger
 
 
 class StopNowException(Exception):
@@ -34,11 +34,12 @@ class Iteratorize:
 
         def gentask():
             try:
-                ret = self.mfunc(callback=_callback, *self.args, **self.kwargs)
+                ret = self.mfunc(callback=_callback, *args, **self.kwargs)
             except StopNowException:
                 pass
             except Exception:
-                logger.exception("Failed in generation callback")
+                traceback.print_exc()
+                pass
 
             self.q.put(self.sentinel)
             if self.c_callback:

modules/exllamav3.py

@@ -1,6 +1,6 @@
-import math
 import queue
 import threading
+import traceback
 from pathlib import Path
 from typing import Any, List, Tuple
 
@@ -9,7 +9,6 @@ import torch
 from exllamav3 import Cache, Config, Generator, Model, Tokenizer
 from exllamav3.cache import CacheLayer_fp16, CacheLayer_quant
 from exllamav3.generator import Job
-from exllamav3.generator.filter import Filter
 from exllamav3.generator.sampler import (
     CustomSampler,
     SS_AdaptiveP,
@@ -33,30 +32,8 @@ from modules.text_generation import get_max_prompt_length
 try:
     import flash_attn
 except Exception:
-    logger.warning('Failed to load flash-attention due to the following error:', exc_info=True)
-
-
-class LogitBiasFilter(Filter):
-    """Filter subclass that applies a static additive logit bias mask."""
-
-    def __init__(self, tokenizer, logit_bias_dict):
-        super().__init__(tokenizer=tokenizer, trigger_token=None, prefix_str=None, eos_after_completed=False)
-        self.logit_bias_dict = logit_bias_dict
-        self._mask = None
-
-    def reset(self): pass
-    def accept_token(self, token): pass
-    def is_completed(self): return False
-    def use_background_worker(self): return False
-
-    def get_next_logit_mask(self):
-        if self._mask is None:
-            self._mask = torch.zeros((1, self.vocab_size), dtype=self.logits_dtype)
-            for token_id_str, bias in self.logit_bias_dict.items():
-                token_id = int(token_id_str)
-                if 0 <= token_id < self.vocab_size:
-                    self._mask[0, token_id] = bias
-        return self._mask
+    logger.warning('Failed to load flash-attention due to the following error:\n')
+    traceback.print_exc()
 
 
 class ConcurrentGenerator:
@@ -76,16 +53,7 @@ class ConcurrentGenerator:
                 if not self.job_queues:
                     self.has_jobs.clear()
                     continue
-                try:
-                    results = self.generator.iterate()
-                except Exception:
-                    logger.exception("Exception in ConcurrentGenerator iterate loop")
-                    for q in self.job_queues.values():
-                        q.put(None)
-                    self.job_queues.clear()
-                    self.generator.clear_queue()
-                    self.has_jobs.clear()
-                    continue
+                results = self.generator.iterate()
             for result in results:
                 job = result["job"]
                 q = self.job_queues.get(job)
@@ -121,10 +89,6 @@ class Exllamav3Model:
     def __init__(self):
         pass
 
-    @property
-    def device(self) -> torch.device:
-        return torch.device(0)
-
     @classmethod
     def from_pretrained(cls, path_to_model):
         path_to_model = Path(f'{shared.args.model_dir}') / Path(path_to_model)
@@ -185,21 +149,8 @@ class Exllamav3Model:
             load_params['tensor_p'] = True
             load_params['tp_backend'] = shared.args.tp_backend
 
-        # Load vision and draft before the main model so autosplit
-        # accounts for their VRAM usage.
-
-        # Load vision model component (ExLlamaV3 native)
-        vision_model = None
-        if "vision_config" in config.config_dict:
-            logger.info("Vision component detected in model config. Attempting to load...")
-            try:
-                vision_model = Model.from_config(config, component="vision")
-                vision_model.load(progressbar=True)
-                logger.info("Vision model loaded successfully.")
-            except Exception as e:
-                logger.warning(f"Vision model loading failed (multimodal disabled): {e}")
-        else:
-            logger.info("No vision component in model config. Skipping multimodal setup.")
+        model.load(**load_params)
+        tokenizer = Tokenizer.from_config(config)
 
         # Initialize draft model for speculative decoding
         draft_model = None
@@ -215,8 +166,23 @@ class Exllamav3Model:
                 logger.warning(f"Draft model not found at {draft_path}, speculative decoding disabled.")
             else:
                 draft_config = Config.from_directory(str(draft_path))
+
+                # Set context size for draft model with 256-multiple validation
+                if shared.args.ctx_size_draft > 0:
+                    draft_max_tokens = shared.args.ctx_size_draft
+                else:
+                    draft_max_tokens = shared.args.ctx_size
+
+                # Validate draft model context size is a multiple of 256
+                if draft_max_tokens % 256 != 0:
+                    adjusted_draft_tokens = ((draft_max_tokens // 256) + 1) * 256
+                    logger.warning(f"Draft model max_num_tokens must be a multiple of 256. Adjusting from {draft_max_tokens} to {adjusted_draft_tokens}")
+                    draft_max_tokens = adjusted_draft_tokens
+
+                draft_config.max_seq_len = draft_max_tokens
+
                 draft_model = Model.from_config(draft_config)
-                draft_cache = Cache(draft_model, max_num_tokens=max_tokens, layer_type=layer_type, **cache_kwargs)
+                draft_cache = Cache(draft_model, max_num_tokens=draft_max_tokens, layer_type=layer_type, **cache_kwargs)
 
                 draft_load_params = {'progressbar': True}
                 if split:
@@ -225,9 +191,18 @@ class Exllamav3Model:
                 draft_model.load(**draft_load_params)
                 logger.info(f"Draft model loaded successfully. Max speculative tokens: {shared.args.draft_max}")
 
-        # Load main model last
-        model.load(**load_params)
-        tokenizer = Tokenizer.from_config(config)
+        # Load vision model component (ExLlamaV3 native)
+        vision_model = None
+        if "vision_config" in config.config_dict:
+            logger.info("Vision component detected in model config. Attempting to load...")
+            try:
+                vision_model = Model.from_config(config, component="vision")
+                vision_model.load(progressbar=True)
+                logger.info("Vision model loaded successfully.")
+            except Exception as e:
+                logger.warning(f"Vision model loading failed (multimodal disabled): {e}")
+        else:
+            logger.info("No vision component in model config. Skipping multimodal setup.")
 
         generator = Generator(
             model=model,
@@ -410,31 +385,11 @@ class Exllamav3Model:
         else:
             max_new_tokens = state['max_new_tokens']
 
-        # Use full EOS token list from config (may contain multiple IDs)
+        # Get stop conditions
         stop_conditions = []
         if not state['ban_eos_token']:
-            for eos_id in self.config.eos_token_id_list:
-                if eos_id is not None:
-                    stop_conditions.append(eos_id)
-
-        # Build filters for logit_bias (OpenAI API)
-        filters = []
-        logit_bias = state.get('logit_bias')
-        if logit_bias:
-            filters.append(LogitBiasFilter(self.tokenizer, logit_bias))
-
-        # Suppress EOS tokens via logit bias so they are never sampled
-        if state['ban_eos_token']:
-            eos_bias = {}
-            for eos_id in self.config.eos_token_id_list:
-                if eos_id is not None:
-                    eos_bias[str(eos_id)] = float('-inf')
-            if eos_bias:
-                filters.append(LogitBiasFilter(self.tokenizer, eos_bias))
-
-        # Logprobs support (OpenAI API)
-        logprobs = state.get('logprobs', 0) or 0
-        return_top_tokens = logprobs if logprobs > 0 else 0
+            if hasattr(self.tokenizer, 'eos_token_id') and self.tokenizer.eos_token_id is not None:
+                stop_conditions.append(self.tokenizer.eos_token_id)
 
         seed = state.get('seed', -1)
         job = Job(
@@ -445,15 +400,11 @@ class Exllamav3Model:
             sampler=sampler,
             seed=seed if seed >= 0 else None,
             stop_conditions=stop_conditions if stop_conditions else None,
-            filters=filters if filters else None,
-            return_top_tokens=return_top_tokens,
-            return_probs=return_top_tokens > 0,
         )
 
         # Stream generation
         response_text = ""
         stop_event = state.get('stop_event')
-        self.last_completion_probabilities = []
 
         result_queue = self.parallel_generator.submit(job)
         try:
@@ -465,61 +416,14 @@ class Exllamav3Model:
                 except queue.Empty:
                     continue
                 if result is None or result.get("eos"):
-                    # Capture logprobs from the final eos result too
-                    if result is not None and return_top_tokens > 0:
-                        self._capture_logprobs(result)
                     break
                 chunk = result.get("text", "")
-
-                # Capture logprobs from streaming results
-                if return_top_tokens > 0:
-                    self._capture_logprobs(result)
-
                 if chunk:
                     response_text += chunk
                     yield response_text
         finally:
             self.parallel_generator.cancel(job)
 
-    def _capture_logprobs(self, result):
-        """Convert ExLlamav3 top-k token data to the shared logprobs format."""
-        top_k_tokens = result.get("top_k_tokens")
-        top_k_probs = result.get("top_k_probs")
-        if top_k_tokens is None or top_k_probs is None:
-            return
-
-        id_to_piece = self.tokenizer.get_id_to_piece_list(True)
-        sampled_ids = result.get("token_ids")    # (batch, seq_len) - actually sampled tokens
-        sampled_probs = result.get("token_probs")  # (batch, seq_len) - their probabilities
-
-        def _piece(tid):
-            s = id_to_piece[tid] if tid < len(id_to_piece) else f"<{tid}>"
-            return s.replace('\u2581', ' ')
-
-        def _logprob(prob):
-            return math.log(prob) if prob > 0 else float("-inf")
-
-        # top_k_tokens shape: (batch, seq_len, k), top_k_probs same
-        for seq_idx in range(top_k_tokens.shape[1]):
-            entry = {"top_logprobs": []}
-            for k_idx in range(top_k_tokens.shape[2]):
-                token_id = top_k_tokens[0, seq_idx, k_idx].item()
-                prob = top_k_probs[0, seq_idx, k_idx].item()
-                entry["top_logprobs"].append({"token": _piece(token_id), "logprob": _logprob(prob)})
-
-            # Record the actually sampled token at the entry level so
-            # format_completion_logprobs uses it instead of top_logprobs[0]
-            # (they differ with non-greedy sampling).
-            if sampled_ids is not None:
-                sid = sampled_ids[0, seq_idx].item()
-                entry["token"] = _piece(sid)
-                if sampled_probs is not None:
-                    entry["logprob"] = _logprob(sampled_probs[0, seq_idx].item())
-                else:
-                    entry["logprob"] = None
-
-            self.last_completion_probabilities.append(entry)
-
     def generate(self, prompt, state):
         output = ""
         for chunk in self.generate_with_streaming(prompt, state):
@@ -527,31 +431,42 @@ class Exllamav3Model:
 
         return output
 
-    def get_prompt_logits(self, input_ids):
-        """Return logits for all positions via a single no-cache forward pass.
-
-        Used by prompt logprobs computation. Returns (1, seq_len, vocab) on CPU in float32.
-        """
-        import torch
-        input_ids_tensor = input_ids if isinstance(input_ids, torch.Tensor) else torch.tensor(input_ids, dtype=torch.long)
-        input_ids_tensor = input_ids_tensor.view(1, -1).cpu()
-        with torch.no_grad():
-            return self.model.forward(
-                input_ids=input_ids_tensor,
-                params={"attn_mode": "flash_attn_nc"}
-            ).cpu().float()
-
     def get_logits(self, token_ids, **kwargs):
         """
         Process a batch of token_ids and return the logits for the last token.
-        Uses flash_attn_nc (no cache) for correct results with recurrent models.
+        This will reset and overwrite the model's cache.
         """
+        # Initialize a single params dictionary that will be updated in-place
+        params = {
+            "cache": self.cache,
+            "reconstruct": False,
+            "attn_mode": "flash_attn",
+            "batch_shape": (1, self.max_tokens),
+            "past_len": 0
+        }
+        params.update(kwargs)
+
+        # Process prefix tokens to fill the cache and generate recurrent state
+        if token_ids.shape[-1] > 1:
+            prefix_ids = token_ids[:, :-1]
+
+            # This forward call updates the 'params' dict with the recurrent state
+            self.model.forward(
+                input_ids=prefix_ids,
+                params=params
+            )
+
+            # Update past_len for the next call
+            params["past_len"] = prefix_ids.shape[-1]
+
+        # Process the last token, now using the state-filled 'params' dict
+        last_token_ids = token_ids[:, -1:]
         logits = self.model.forward(
-            input_ids=token_ids,
-            params={"attn_mode": "flash_attn_nc"}
+            input_ids=last_token_ids,
+            params=params
         )
 
-        return logits[:, -1:, :].float().cpu()
+        return logits.float().cpu()
 
     def encode(self, string, **kwargs):
         add_bos = kwargs.pop('add_bos', True)

modules/exllamav3_hf.py

@@ -1,4 +1,5 @@
 import os
+import traceback
 from pathlib import Path
 from typing import Any, Dict, Optional, Union
 
@@ -20,15 +21,13 @@ from modules.logging_colors import logger
 try:
     import flash_attn
 except Exception:
-    logger.warning('Failed to load flash-attention due to the following error:', exc_info=True)
+    logger.warning('Failed to load flash-attention due to the following error:\n')
+    traceback.print_exc()
 
 
 class Exllamav3HF(PreTrainedModel, GenerationMixin):
     def __init__(self, model_dir):
         hf_config = PretrainedConfig.from_pretrained(model_dir)
-        # Ensure text_config is a proper object, not a dict (fixes qwen3_5_moe + transformers compat)
-        if isinstance(getattr(hf_config, 'text_config', None), dict):
-            hf_config.text_config = PretrainedConfig(**hf_config.text_config)
         super().__init__(hf_config)
 
         exl3_config = Config.from_directory(model_dir)
@@ -202,11 +201,26 @@ class Exllamav3HF(PreTrainedModel, GenerationMixin):
                 }
             ).to(input_ids.device).float()
         else:
-            # Labels path: single pass without cache for correct logits
-            logits = self.ex_model.forward(
-                input_ids=seq_tensor.view(1, -1),
-                params={"attn_mode": "flash_attn_nc"}
-            ).float().cpu()
+            # When processing with labels, handle as a complete sequence
+            # Process in chunks if the number of tokens is large
+            tokens_to_process = seq_tensor
+            all_logits = None
+
+            for i in range(0, tokens_to_process.shape[0], max_chunk_size):
+                chunk = tokens_to_process[i:i + max_chunk_size]
+                chunk_logits = self.ex_model.forward(
+                    input_ids=chunk.view(1, -1),
+                    params={
+                        "attn_mode": "flash_attn_nc",
+                    }
+                ).float()
+
+                if all_logits is None:
+                    all_logits = chunk_logits
+                else:
+                    all_logits = torch.cat([all_logits, chunk_logits], dim=1)
+
+            logits = all_logits
 
         if is_negative:
             self.past_seq_negative = seq_tensor

modules/extensions.py

@@ -1,10 +1,13 @@
 import importlib
 import importlib.util
 import sys
+import traceback
 from functools import partial
 from inspect import signature
 from pathlib import Path
 
+import gradio as gr
+
 import modules.shared as shared
 from modules.logging_colors import logger
 
@@ -32,7 +35,8 @@ def load_extensions():
         if name not in available_extensions:
             continue
 
-        logger.info(f'Loading the extension "{name}"')
+        if name != 'api':
+            logger.info(f'Loading the extension "{name}"')
 
         try:
             # Prefer user extension, fall back to system extension
@@ -73,7 +77,8 @@ def load_extensions():
             raise
 
         except Exception:
-            logger.exception(f'Failed to load the extension "{name}".')
+            logger.error(f'Failed to load the extension "{name}".')
+            traceback.print_exc()
 
 
 # This iterator returns the extensions in the order specified in the command-line
@@ -191,23 +196,24 @@ def _apply_custom_generate_reply():
 
 
 def _apply_custom_css():
-    return ''.join(
-        getattr(extension, 'custom_css')()
-        for extension, _ in iterator()
-        if hasattr(extension, 'custom_css')
-    )
+    all_css = ''
+    for extension, _ in iterator():
+        if hasattr(extension, 'custom_css'):
+            all_css += getattr(extension, 'custom_css')()
+
+    return all_css
 
 
 def _apply_custom_js():
-    return ''.join(
-        getattr(extension, 'custom_js')()
-        for extension, _ in iterator()
-        if hasattr(extension, 'custom_js')
-    )
+    all_js = ''
+    for extension, _ in iterator():
+        if hasattr(extension, 'custom_js'):
+            all_js += getattr(extension, 'custom_js')()
+
+    return all_js
 
 
 def create_extensions_block():
-    import gradio as gr
     to_display = []
     for extension, name in iterator():
         if hasattr(extension, "ui") and not (hasattr(extension, 'params') and extension.params.get('is_tab', False)):
@@ -222,7 +228,6 @@ def create_extensions_block():
 
 
 def create_extensions_tabs():
-    import gradio as gr
     for extension, name in iterator():
         if hasattr(extension, "ui") and (hasattr(extension, 'params') and extension.params.get('is_tab', False)):
             display_name = getattr(extension, 'params', {}).get('display_name', name)

modules/html_generator.py

@@ -10,17 +10,9 @@ import markdown
 from PIL import Image, ImageOps
 
 from modules import shared
-from modules.reasoning import extract_reasoning
 from modules.sane_markdown_lists import SaneListExtension
 from modules.utils import get_available_chat_styles
 
-# Pre-compiled regex for protecting markdown-sensitive characters inside LaTeX.
-# Covers $$...$$, \[...\], \(...\), and inline $...$ (when content contains \\).
-_LATEX_PATTERN = re.compile(
-    r'((?:^|[\r\n\s])\$\$[^`]*?\$\$)|\\\[(.*?)\\\]|\\\((.*?)\\\)|(?<!\$)\$(?!\$)([^\$\n]*\\\\[^\$\n]*?)\$(?!\$)',
-    re.DOTALL
-)
-
 # This is to store the paths to the thumbnails of the profile pictures
 image_cache = {}
 
@@ -116,41 +108,69 @@ def replace_blockquote(m):
     return m.group().replace('\n', '\n> ').replace('\\begin{blockquote}', '').replace('\\end{blockquote}', '')
 
 
+# Thinking block format definitions: (start_tag, end_tag, content_start_tag)
+# Use None for start_tag to match from beginning (end-only formats should be listed last)
+THINKING_FORMATS = [
+    ('<think>', '</think>', None),
+    ('<|channel|>analysis<|message|>', '<|end|>', '<|start|>assistant<|channel|>final<|message|>'),
+    ('<seed:think>', '</seed:think>', None),
+    ('<|think|>', '<|end|>', '<|content|>'),  # Solar Open
+    ('Thinking Process:', '</think>', None),  # Qwen3.5 verbose thinking outside tags
+    (None, '</think>', None),  # End-only variant (e.g., Qwen3-next)
+]
+
+
 def extract_thinking_block(string):
-    """Extract thinking blocks from the beginning of an HTML-escaped string."""
-    return extract_reasoning(string, html_escaped=True)
+    """Extract thinking blocks from the beginning of a string."""
+    if not string:
+        return None, string
+
+    for start_tag, end_tag, content_tag in THINKING_FORMATS:
+        end_esc = html.escape(end_tag)
+        content_esc = html.escape(content_tag) if content_tag else None
+
+        if start_tag is None:
+            # End-only format: require end tag, start from beginning
+            end_pos = string.find(end_esc)
+            if end_pos == -1:
+                continue
+            thought_start = 0
+        else:
+            # Normal format: require start tag
+            start_esc = html.escape(start_tag)
+            start_pos = string.find(start_esc)
+            if start_pos == -1:
+                continue
+            thought_start = start_pos + len(start_esc)
+            end_pos = string.find(end_esc, thought_start)
+
+        if end_pos == -1:
+            # End tag missing - check if content tag can serve as fallback
+            if content_esc:
+                content_pos = string.find(content_esc, thought_start)
+                if content_pos != -1:
+                    thought_end = content_pos
+                    content_start = content_pos + len(content_esc)
+                else:
+                    thought_end = len(string)
+                    content_start = len(string)
+            else:
+                thought_end = len(string)
+                content_start = len(string)
+        else:
+            thought_end = end_pos
+            if content_esc:
+                content_pos = string.find(content_esc, end_pos)
+                content_start = content_pos + len(content_esc) if content_pos != -1 else end_pos + len(end_esc)
+            else:
+                content_start = end_pos + len(end_esc)
+
+        return string[thought_start:thought_end], string[content_start:]
+
+    return None, string
 
 
-
-def build_tool_call_block(header, body, message_id, index):
-    """Build HTML for a tool call accordion block."""
-    block_id = f"tool-call-{message_id}-{index}"
-
-    if body == '...':
-        # Pending placeholder — no expandable body, just title with ellipsis
-        return f'''
-        <details class="thinking-block" data-block-id="{block_id}">
-            <summary class="thinking-header">
-                {tool_svg_small}
-                <span class="thinking-title">{html.escape(header)} ...</span>
-            </summary>
-        </details>
-        '''
-
-    # Build a plain <pre> directly to avoid highlight.js auto-detection
-    escaped_body = html.escape(body)
-    return f'''
-    <details class="thinking-block" data-block-id="{block_id}">
-        <summary class="thinking-header">
-            {tool_svg_small}
-            <span class="thinking-title">{html.escape(header)}</span>
-        </summary>
-        <div class="thinking-content pretty_scrollbar"><pre><code class="nohighlight">{escaped_body}</code></pre></div>
-    </details>
-    '''
-
-
-def build_thinking_block(thinking_content, message_id, has_remaining_content, thinking_index=0):
+def build_thinking_block(thinking_content, message_id, has_remaining_content):
     """Build HTML for a thinking block."""
     if thinking_content is None:
         return None
@@ -159,7 +179,7 @@ def build_thinking_block(thinking_content, message_id, has_remaining_content, th
     thinking_html = process_markdown_content(thinking_content)
 
     # Generate unique ID for the thinking block
-    block_id = f"thinking-{message_id}-{thinking_index}"
+    block_id = f"thinking-{message_id}-0"
 
     # Check if thinking is complete or still in progress
     is_streaming = not has_remaining_content
@@ -192,29 +212,28 @@ def process_markdown_content(string):
     if not string:
         return ""
 
-    # Define unique placeholders for LaTeX characters that conflict with markdown
+    # Define unique placeholders for LaTeX asterisks and underscores
     LATEX_ASTERISK_PLACEHOLDER = "LATEXASTERISKPLACEHOLDER"
     LATEX_UNDERSCORE_PLACEHOLDER = "LATEXUNDERSCOREPLACEHOLDER"
-    LATEX_PIPE_PLACEHOLDER = "LATEXPIPEPLACEHOLDER"
-
-    def protect_latex_content(content):
-        """Protect markdown-sensitive characters inside LaTeX."""
-        content = content.replace('*', LATEX_ASTERISK_PLACEHOLDER)
-        content = content.replace('_', LATEX_UNDERSCORE_PLACEHOLDER)
-        content = content.replace('|', LATEX_PIPE_PLACEHOLDER)
-        return content
 
     def protect_asterisks_underscores_in_latex(match):
-        """A replacer function for re.sub to protect markdown-sensitive characters in multiple LaTeX formats."""
+        """A replacer function for re.sub to protect asterisks and underscores in multiple LaTeX formats."""
         # Check which delimiter group was captured
         if match.group(1) is not None:  # Content from $$...$$
-            return protect_latex_content(match.group(1))
+            content = match.group(1)
+            modified_content = content.replace('*', LATEX_ASTERISK_PLACEHOLDER)
+            modified_content = modified_content.replace('_', LATEX_UNDERSCORE_PLACEHOLDER)
+            return f'{modified_content}'
         elif match.group(2) is not None:  # Content from \[...\]
-            return f'\\[{protect_latex_content(match.group(2))}\\]'
+            content = match.group(2)
+            modified_content = content.replace('*', LATEX_ASTERISK_PLACEHOLDER)
+            modified_content = modified_content.replace('_', LATEX_UNDERSCORE_PLACEHOLDER)
+            return f'\\[{modified_content}\\]'
         elif match.group(3) is not None:  # Content from \(...\)
-            return f'\\({protect_latex_content(match.group(3))}\\)'
-        elif match.group(4) is not None:  # Content from $...$
-            return f'${protect_latex_content(match.group(4).strip())}$'
+            content = match.group(3)
+            modified_content = content.replace('*', LATEX_ASTERISK_PLACEHOLDER)
+            modified_content = modified_content.replace('_', LATEX_UNDERSCORE_PLACEHOLDER)
+            return f'\\({modified_content}\\)'
 
         return match.group(0)  # Fallback
 
@@ -248,7 +267,9 @@ def process_markdown_content(string):
     string = re.sub(r"(.)```", r"\1\n```", string)
 
     # Protect asterisks and underscores within all LaTeX blocks before markdown conversion
-    string = _LATEX_PATTERN.sub(protect_asterisks_underscores_in_latex, string)
+    latex_pattern = re.compile(r'((?:^|[\r\n\s])\$\$[^`]*?\$\$)|\\\[(.*?)\\\]|\\\((.*?)\\\)',
+                               re.DOTALL)
+    string = latex_pattern.sub(protect_asterisks_underscores_in_latex, string)
 
     result = ''
     is_code = False
@@ -312,7 +333,6 @@ def process_markdown_content(string):
     # Restore the LaTeX asterisks and underscores after markdown conversion
     html_output = html_output.replace(LATEX_ASTERISK_PLACEHOLDER, '*')
     html_output = html_output.replace(LATEX_UNDERSCORE_PLACEHOLDER, '_')
-    html_output = html_output.replace(LATEX_PIPE_PLACEHOLDER, '|')
 
     # Remove extra newlines before </code>
     html_output = re.sub(r'\s*</code>', '</code>', html_output)
@@ -324,9 +344,6 @@ def process_markdown_content(string):
     # Unescape backslashes
     html_output = html_output.replace('\\\\', '\\')
 
-    # Wrap tables in a scrollable div
-    html_output = html_output.replace('<table>', '<div class="table-wrapper pretty_scrollbar"><table>').replace('</table>', '</table></div>')
-
     return html_output
 
 
@@ -343,66 +360,24 @@ def convert_to_markdown(string, message_id=None):
     if message_id is None:
         message_id = "unknown"
 
-    # Find tool call blocks by position, then process the text segments
-    # between them using extract_thinking_block (which supports all
-    # THINKING_FORMATS, including end-only variants like Qwen's).
-    tool_call_pattern = re.compile(r'<tool_call>(.*?)\n(.*?)\n</tool_call>', re.DOTALL)
-    tool_calls = list(tool_call_pattern.finditer(string))
+    # Extract different components from the string
+    thinking_content, remaining_content = extract_thinking_block(string)
 
-    if not tool_calls:
-        # No tool calls — use original single-pass extraction
-        thinking_content, remaining_content = extract_thinking_block(string)
-        blocks = []
-        thinking_html = build_thinking_block(thinking_content, message_id, bool(remaining_content))
-        if thinking_html:
-            blocks.append(thinking_html)
+    # Build individual HTML blocks
+    blocks = []
 
-        main_html = build_main_content_block(remaining_content)
-        if main_html:
-            blocks.append(main_html)
+    # Add thinking block if present
+    thinking_html = build_thinking_block(thinking_content, message_id, bool(remaining_content))
+    if thinking_html:
+        blocks.append(thinking_html)
 
-        return ''.join(blocks)
+    # Add main content block
+    main_html = build_main_content_block(remaining_content)
+    if main_html:
+        blocks.append(main_html)
 
-    # Split string into text segments around tool_call blocks and
-    # run extract_thinking_block on each segment for full format support.
-    html_parts = []
-    last_end = 0
-    tool_idx = 0
-    think_idx = 0
-
-    def process_text_segment(text, is_last_segment):
-        """Process a text segment between tool_call blocks for thinking content."""
-        nonlocal think_idx
-        if not text.strip():
-            return
-
-        while text.strip():
-            thinking_content, remaining = extract_thinking_block(text)
-            if thinking_content is None:
-                break
-            has_remaining = bool(remaining.strip()) or not is_last_segment
-            html_parts.append(build_thinking_block(thinking_content, message_id, has_remaining, think_idx))
-            think_idx += 1
-            text = remaining
-
-        if text.strip():
-            html_parts.append(process_markdown_content(text))
-
-    for tc in tool_calls:
-        # Process text before this tool_call
-        process_text_segment(string[last_end:tc.start()], is_last_segment=False)
-
-        # Add tool call accordion
-        header = tc.group(1).strip()
-        body = tc.group(2).strip()
-        html_parts.append(build_tool_call_block(header, body, message_id, tool_idx))
-        tool_idx += 1
-        last_end = tc.end()
-
-    # Process text after the last tool_call
-    process_text_segment(string[last_end:], is_last_segment=True)
-
-    return ''.join(html_parts)
+    # Assemble all blocks into final HTML
+    return ''.join(blocks)
 
 
 def convert_to_markdown_wrapped(string, message_id=None, use_cache=True):
@@ -460,7 +435,6 @@ branch_svg = '''<svg  xmlns="http://www.w3.org/2000/svg"  width="24"  height="24
 edit_svg = '''<svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="tabler-icon tabler-icon-pencil"><path d="M4 20h4l10.5 -10.5a2.828 2.828 0 1 0 -4 -4l-10.5 10.5v4"></path><path d="M13.5 6.5l4 4"></path></svg>'''
 info_svg = '''<svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="thinking-icon tabler-icon tabler-icon-info-circle"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M12 2a10 10 0 0 1 0 20a10 10 0 0 1 0 -20z" /><path d="M12 16v-4" /><path d="M12 8h.01" /></svg>'''
 info_svg_small = '''<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="thinking-icon tabler-icon tabler-icon-info-circle"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M12 2a10 10 0 0 1 0 20a10 10 0 0 1 0 -20z" /><path d="M12 16v-4" /><path d="M12 8h.01" /></svg>'''
-tool_svg_small = '''<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="thinking-icon tabler-icon tabler-icon-tool"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M7 10h3v-3l-3.5 -3.5a6 6 0 0 1 8 8l6 6a2 2 0 0 1 -3 3l-6 -6a6 6 0 0 1 -8 -8l3.5 3.5" /></svg>'''
 attachment_svg = '''<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M21.44 11.05l-9.19 9.19a6 6 0 0 1-8.48-8.48l9.19-9.19a4 4 0 0 1 5.66 5.66l-9.2 9.19a2 2 0 0 1-2.83-2.83l8.49-8.48"></path></svg>'''
 
 copy_button = f'<button class="footer-button footer-copy-button" title="Copy" onclick="copyToClipboard(this)">{copy_svg}</button>'

modules/image_models.py

@@ -10,49 +10,72 @@ def get_quantization_config(quant_method):
     Get the appropriate quantization config based on the selected method.
     Applies quantization to both the transformer and the text_encoder.
     """
-    if quant_method == 'none' or not quant_method:
-        return None
-
     import torch
+    # Import BitsAndBytesConfig from BOTH libraries to be safe
     from diffusers import BitsAndBytesConfig as DiffusersBnBConfig
     from diffusers import TorchAoConfig
     from diffusers.quantizers import PipelineQuantizationConfig
     from transformers import BitsAndBytesConfig as TransformersBnBConfig
 
-    torchao_methods = {
-        'torchao-int8wo': 'int8wo',
-        'torchao-fp4': 'fp4_e2m1',
-        'torchao-float8wo': 'float8wo',
-    }
+    if quant_method == 'none' or not quant_method:
+        return None
 
-    if quant_method == 'bnb-8bit':
+    # Bitsandbytes 8-bit quantization
+    elif quant_method == 'bnb-8bit':
         return PipelineQuantizationConfig(
             quant_mapping={
-                "transformer": DiffusersBnBConfig(load_in_8bit=True),
-                "text_encoder": TransformersBnBConfig(load_in_8bit=True)
+                "transformer": DiffusersBnBConfig(
+                    load_in_8bit=True
+                ),
+                "text_encoder": TransformersBnBConfig(
+                    load_in_8bit=True
+                )
             }
         )
 
+    # Bitsandbytes 4-bit quantization
     elif quant_method == 'bnb-4bit':
-        bnb_4bit_kwargs = dict(
-            load_in_4bit=True,
-            bnb_4bit_quant_type="nf4",
-            bnb_4bit_compute_dtype=torch.bfloat16,
-            bnb_4bit_use_double_quant=True
-        )
         return PipelineQuantizationConfig(
             quant_mapping={
-                "transformer": DiffusersBnBConfig(**bnb_4bit_kwargs),
-                "text_encoder": TransformersBnBConfig(**bnb_4bit_kwargs)
+                "transformer": DiffusersBnBConfig(
+                    load_in_4bit=True,
+                    bnb_4bit_quant_type="nf4",
+                    bnb_4bit_compute_dtype=torch.bfloat16,
+                    bnb_4bit_use_double_quant=True
+                ),
+                "text_encoder": TransformersBnBConfig(
+                    load_in_4bit=True,
+                    bnb_4bit_quant_type="nf4",
+                    bnb_4bit_compute_dtype=torch.bfloat16,
+                    bnb_4bit_use_double_quant=True
+                )
             }
         )
 
-    elif quant_method in torchao_methods:
-        ao_type = torchao_methods[quant_method]
+    # torchao int8 weight-only
+    elif quant_method == 'torchao-int8wo':
         return PipelineQuantizationConfig(
             quant_mapping={
-                "transformer": TorchAoConfig(ao_type),
-                "text_encoder": TorchAoConfig(ao_type)
+                "transformer": TorchAoConfig("int8wo"),
+                "text_encoder": TorchAoConfig("int8wo")
+            }
+        )
+
+    # torchao fp4 (e2m1)
+    elif quant_method == 'torchao-fp4':
+        return PipelineQuantizationConfig(
+            quant_mapping={
+                "transformer": TorchAoConfig("fp4_e2m1"),
+                "text_encoder": TorchAoConfig("fp4_e2m1")
+            }
+        )
+
+    # torchao float8 weight-only
+    elif quant_method == 'torchao-float8wo':
+        return PipelineQuantizationConfig(
+            quant_mapping={
+                "transformer": TorchAoConfig("float8wo"),
+                "text_encoder": TorchAoConfig("float8wo")
             }
         )
 
@@ -129,7 +152,7 @@ def load_image_model(model_name, dtype='bfloat16', attn_backend='sdpa', cpu_offl
 
         modules = ["transformer", "unet"]
 
-        # Set attention backend (diffusers defaults to native/SDPA)
+        # Set attention backend
         if attn_backend == 'flash_attention_2':
             for name in modules:
                 mod = getattr(pipe, name, None)

modules/image_utils.py

@@ -77,18 +77,7 @@ def process_message_content(content: Any) -> Tuple[str, List[Image.Image]]:
                     # Support external URLs
                     try:
                         import requests
-                        from urllib.parse import urljoin
-                        from modules.web_search import _validate_url
-                        _validate_url(image_url)
-                        url = image_url
-                        for _ in range(5):
-                            response = requests.get(url, timeout=10, allow_redirects=False)
-                            if response.is_redirect and 'Location' in response.headers:
-                                url = urljoin(url, response.headers['Location'])
-                                _validate_url(url)
-                            else:
-                                break
-
+                        response = requests.get(image_url, timeout=10)
                         response.raise_for_status()
                         image_data = response.content
                         image = Image.open(io.BytesIO(image_data))

modules/llama_cpp_server.py

@@ -1,7 +1,6 @@
 import json
 import os
 import pprint
-import shlex
 import re
 import socket
 import subprocess
@@ -11,6 +10,7 @@ import time
 from pathlib import Path
 from typing import Any, List
 
+import llama_cpp_binaries
 import requests
 
 from modules import shared
@@ -36,7 +36,6 @@ class LlamaServer:
         self.process = None
         self.session = requests.Session()
         self.vocabulary_size = None
-        self.n_ctx = None
         self.bos_token = "<s>"
         self.last_prompt_token_count = 0
 
@@ -130,24 +129,13 @@ class LlamaServer:
             # places it at the end of the chain regardless of position, so we
             # activate it based on the parameter value rather than sampler order.
             if state.get("adaptive_target", 0) > 0:
-                filtered_samplers.append("adaptive_p")
+                filtered_samplers.append("adaptive-p")
 
             payload["samplers"] = filtered_samplers
 
-        logit_bias = []
         if state['custom_token_bans']:
-            logit_bias.extend([[int(token_id.strip()), False] for token_id in state['custom_token_bans'].split(',') if token_id.strip()])
-
-        if state.get('logit_bias'):
-            for token_id_str, bias in state['logit_bias'].items():
-                logit_bias.append([int(token_id_str), bias])
-
-        if logit_bias:
-            payload["logit_bias"] = logit_bias
-
-        n_probs = state.get('logprobs', 0)
-        if n_probs and n_probs > 0:
-            payload["n_probs"] = n_probs
+            to_ban = [[int(token_id), False] for token_id in state['custom_token_bans'].split(',')]
+            payload["logit_bias"] = to_ban
 
         return payload
 
@@ -227,7 +215,6 @@ class LlamaServer:
                 response.raise_for_status()  # Raise an exception for HTTP errors
 
             full_text = ""
-            self.last_completion_probabilities = []
 
             # Process the streaming response
             stop_event = state.get('stop_event')
@@ -253,10 +240,6 @@ class LlamaServer:
                         full_text += data['content']
                         yield full_text
 
-                    # Capture logprobs if present
-                    if 'completion_probabilities' in data:
-                        self.last_completion_probabilities.extend(data['completion_probabilities'])
-
                     # Check if generation is complete
                     if data.get('stop', False):
                         break
@@ -310,45 +293,8 @@ class LlamaServer:
         else:
             raise Exception(f"Unexpected response format: 'completion_probabilities' not found in {result}")
 
-    def get_prompt_logprob_entries(self, token_ids, n_probs=5, prompt=""):
-        """Get logprob entries for prompt tokens via a single n_predict=0 request.
-
-        Requires llama.cpp server with prompt_logprobs support.
-        Returns entries in the standard format for format_completion_logprobs().
-        """
-        token_ids_list = token_ids.tolist() if hasattr(token_ids, 'tolist') else list(token_ids)
-
-        url = f"http://127.0.0.1:{self.port}/completion"
-        payload = {
-            "prompt": token_ids_list,
-            "n_predict": 0,
-            "n_probs": n_probs,
-            "prompt_logprobs": True,
-            "stream": False,
-            "cache_prompt": False,
-        }
-
-        response = self.session.post(url, json=payload)
-        result = response.json()
-
-        prompt_probs = result.get("prompt_probabilities", [])
-        if not prompt_probs:
-            return []
-
-        # Null first token (no conditioning context); use empty string for BOS
-        # or tokens that don't appear at the start of the prompt text.
-        first_token_str = self.decode([token_ids_list[0]])
-        if self.bos_token and first_token_str == self.bos_token:
-            first_token_str = ""
-        elif not prompt.startswith(first_token_str):
-            first_token_str = ""
-
-        entries = [{"token": first_token_str, "null_logprob": True}]
-        entries.extend(prompt_probs)
-        return entries
-
     def _get_vocabulary_size(self):
-        """Get and store the model's vocabulary size."""
+        """Get and store the model's maximum context length."""
         url = f"http://127.0.0.1:{self.port}/v1/models"
         response = self.session.get(url).json()
 
@@ -358,22 +304,16 @@ class LlamaServer:
                 self.vocabulary_size = model_info["meta"]["n_vocab"]
 
     def _get_bos_token(self):
-        """Get and store the model's BOS token and context size."""
+        """Get and store the model's BOS token."""
         url = f"http://127.0.0.1:{self.port}/props"
         response = self.session.get(url).json()
         if "bos_token" in response:
             self.bos_token = response["bos_token"]
 
-        # Get actual n_ctx from the server (important when --fit auto-selects it)
-        n_ctx = response.get("default_generation_settings", {}).get("n_ctx")
-        if n_ctx:
-            self.n_ctx = n_ctx
-
     def _is_port_available(self, port):
         """Check if a port is available for use."""
         with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
             try:
-                s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
                 s.bind(('', port))
                 return True
             except OSError:
@@ -394,16 +334,7 @@ class LlamaServer:
         """Start the llama.cpp server and wait until it's ready."""
         # Determine the server path
         if self.server_path is None:
-            if shared.args.ik:
-                try:
-                    import ik_llama_cpp_binaries
-                except ImportError:
-                    raise ImportError("--ik requires the ik_llama_cpp_binaries package. Install it with: pip install <ik_llama_cpp_binaries wheel URL>")
-
-                self.server_path = ik_llama_cpp_binaries.get_binary_path()
-            else:
-                import llama_cpp_binaries
-                self.server_path = llama_cpp_binaries.get_binary_path()
+            self.server_path = llama_cpp_binaries.get_binary_path()
 
         # Build the command
         cmd = [
@@ -418,14 +349,11 @@ class LlamaServer:
 
         if shared.args.ctx_size > 0:
             cmd += ["--ctx-size", str(shared.args.ctx_size)]
-        elif shared.args.gpu_layers >= 0:
-            cmd += ["--ctx-size", "8192"]
 
         if shared.args.gpu_layers >= 0:
             cmd += ["--gpu-layers", str(shared.args.gpu_layers), "--fit", "off"]
         else:
             cmd += ["--fit", "on"]
-            cmd += ["--fit-ctx", "8192"]
             if shared.args.fit_target:
                 cmd += ["--fit-target", shared.args.fit_target]
 
@@ -451,6 +379,10 @@ class LlamaServer:
         if shared.args.cache_type != "fp16" and shared.args.cache_type in llamacpp_valid_cache_types:
             cmd += ["--cache-type-k", shared.args.cache_type, "--cache-type-v", shared.args.cache_type]
             cache_type = shared.args.cache_type
+        if shared.args.compress_pos_emb != 1:
+            cmd += ["--rope-freq-scale", str(1.0 / shared.args.compress_pos_emb)]
+        if shared.args.rope_freq_base > 0:
+            cmd += ["--rope-freq-base", str(shared.args.rope_freq_base)]
         if shared.args.mmproj not in [None, 'None']:
             path = Path(shared.args.mmproj)
             if not path.exists():
@@ -493,32 +425,21 @@ class LlamaServer:
             elif extra_flags.startswith("'") and extra_flags.endswith("'"):
                 extra_flags = extra_flags[1:-1].strip()
 
-            if extra_flags.startswith('-'):
-                # New literal format: "--jinja --rpc 1222,1222"
-                cmd += shlex.split(extra_flags)
-            else:
-                # Legacy format: "flag1=value1,flag2,flag3=value3"
-                long_form_only = {'rpc', 'fit', 'pos', 'ppl'}
-
-                for flag_item in extra_flags.split(','):
-                    flag_item = flag_item.strip()
-                    if '=' in flag_item:
-                        flag, value = flag_item.split('=', 1)
-                        flag = flag.strip()
-                        value = value.strip()
-                        if len(flag) <= 3 and flag not in long_form_only:
-                            cmd += [f"-{flag}", value]
-                        else:
-                            cmd += [f"--{flag}", value]
+            for flag_item in extra_flags.split(','):
+                flag_item = flag_item.strip()
+                if '=' in flag_item:
+                    flag, value = flag_item.split('=', 1)
+                    flag = flag.strip()
+                    value = value.strip()
+                    if len(flag) <= 3:
+                        cmd += [f"-{flag}", value]
                     else:
-                        if len(flag_item) <= 3 and flag_item not in long_form_only:
-                            cmd.append(f"-{flag_item}")
-                        else:
-                            cmd.append(f"--{flag_item}")
-
-        # Patch flags for ik_llama.cpp compatibility
-        if shared.args.ik:
-            cmd = _patch_cmd_for_ik(cmd)
+                        cmd += [f"--{flag}", value]
+                else:
+                    if len(flag_item) <= 3:
+                        cmd.append(f"-{flag_item}")
+                    else:
+                        cmd.append(f"--{flag_item}")
 
         env = os.environ.copy()
         if os.name == 'posix':
@@ -534,7 +455,7 @@ class LlamaServer:
             print()
 
         gpu_layers_str = "auto" if shared.args.gpu_layers < 0 else str(shared.args.gpu_layers)
-        ctx_size_str = "auto" if shared.args.ctx_size == 0 and shared.args.gpu_layers < 0 else str(shared.args.ctx_size or 8192)
+        ctx_size_str = "auto" if shared.args.ctx_size == 0 else str(shared.args.ctx_size)
         logger.info(f"Using gpu_layers={gpu_layers_str} | ctx_size={ctx_size_str} | cache_type={cache_type}")
         # Start the server with pipes for output
         self.process = subprocess.Popen(
@@ -550,8 +471,9 @@ class LlamaServer:
         health_url = f"http://127.0.0.1:{self.port}/health"
         while True:
             # Check if process is still alive
-            exit_code = self.process.poll()
-            if exit_code is not None:
+            if self.process.poll() is not None:
+                # Process has terminated
+                exit_code = self.process.poll()
                 raise RuntimeError(f"Server process terminated unexpectedly with exit code: {exit_code}")
 
             try:
@@ -657,49 +579,3 @@ def filter_stderr_with_progress(process_stderr):
             process_stderr.close()
         except Exception:
             pass
-
-
-def _patch_cmd_for_ik(cmd):
-    """
-    Rewrite upstream llama.cpp flags to ik_llama.cpp equivalents:
-      --no-webui           → --webui none
-      --fit off            → (removed)
-      --fit on / --fit-ctx → --fit (bare flag)
-      --fit-target         → --fit-margin
-      --cache-reuse        → (removed, unsupported)
-      --swa-full           → (removed, unsupported)
-    """
-    # Add Hadamard KV cache rotation when using quantized cache types.
-    # This significantly improves quantized cache quality (especially q4_0)
-    # and is a no-op for MLA models like DeepSeek.
-    if shared.args.cache_type in ("q8_0", "q4_0"):
-        cmd += ["-khad", "-vhad"]
-
-    patched = []
-    i = 0
-    while i < len(cmd):
-        arg = cmd[i]
-
-        if arg == "--no-webui":
-            patched += ["--webui", "none"]
-        elif arg == "--fit" and i + 1 < len(cmd) and cmd[i + 1] in ("on", "off"):
-            val = cmd[i + 1]
-            i += 1
-            if val == "on":
-                patched.append("--fit")
-            # "off" → drop entirely
-        elif arg == "--fit-ctx":
-            patched.append("--fit")
-            i += 1  # skip the value
-        elif arg == "--fit-target":
-            patched.append("--fit-margin")
-        elif arg == "--cache-reuse":
-            i += 1  # skip the value
-        elif arg == "--swa-full":
-            pass  # bare flag, just drop it
-        else:
-            patched.append(arg)
-
-        i += 1
-
-    return patched

modules/loaders.py

@@ -1,6 +1,8 @@
 import functools
 from collections import OrderedDict
 
+import gradio as gr
+
 loaders_and_params = OrderedDict({
     'llama.cpp': [
         'gpu_layers',
@@ -15,12 +17,13 @@ loaders_and_params = OrderedDict({
         'tensor_split',
         'extra_flags',
         'streaming_llm',
+        'rope_freq_base',
+        'compress_pos_emb',
         'row_split',
         'no_kv_offload',
         'no_mmap',
         'mlock',
         'numa',
-        'ik',
         'parallel',
         'model_draft',
         'draft_max',
@@ -40,6 +43,8 @@ loaders_and_params = OrderedDict({
     'Transformers': [
         'gpu_split',
         'cpu_memory',
+        'alpha_value',
+        'compress_pos_emb',
         'compute_dtype',
         'quant_type',
         'load_in_8bit',
@@ -66,6 +71,7 @@ loaders_and_params = OrderedDict({
         'gpu_split',
         'model_draft',
         'draft_max',
+        'ctx_size_draft',
         'speculative_decoding_accordion',
         'enable_tp',
         'tp_backend',
@@ -202,7 +208,6 @@ loaders_samplers = {
         'ban_eos_token',
         'add_bos_token',
         'enable_thinking',
-        'reasoning_effort',
         'seed',
         'skip_special_tokens',
     },
@@ -239,7 +244,6 @@ loaders_samplers = {
         'reasoning_effort',
         'seed',
         'sampler_priority',
-        'custom_token_bans',
         'dry_sequence_breakers',
         'grammar_string',
         'grammar_file_row',
@@ -273,7 +277,6 @@ def list_all_samplers():
 
 
 def blacklist_samplers(loader, dynamic_temperature):
-    import gradio as gr
     all_samplers = list_all_samplers()
     output = []
 
@@ -291,77 +294,15 @@ def blacklist_samplers(loader, dynamic_temperature):
 
 @functools.cache
 def get_all_params():
-    from modules import shared
     all_params = set()
     for k in loaders_and_params:
         for el in loaders_and_params[k]:
             all_params.add(el)
 
-    if shared.args.portable:
-        all_params.discard('ik')
-
     return sorted(all_params)
 
 
-@functools.cache
-def list_model_elements():
-    elements = [
-        'filter_by_loader',
-        'loader',
-        'cpu_memory',
-        'gpu_layers',
-        'fit_target',
-        'cpu_moe',
-        'threads',
-        'threads_batch',
-        'batch_size',
-        'ubatch_size',
-        'ctx_size',
-        'cache_type',
-        'tensor_split',
-        'extra_flags',
-        'streaming_llm',
-        'gpu_split',
-        'compute_dtype',
-        'quant_type',
-        'load_in_8bit',
-        'load_in_4bit',
-        'attn_implementation',
-        'cpu',
-        'disk',
-        'row_split',
-        'no_kv_offload',
-        'no_mmap',
-        'mlock',
-        'numa',
-        'parallel',
-        'use_double_quant',
-        'bf16',
-        'enable_tp',
-        'tp_backend',
-        'cfg_cache',
-        'no_use_fast',
-        'model_draft',
-        'draft_max',
-        'gpu_layers_draft',
-        'device_draft',
-        'ctx_size_draft',
-        'spec_type',
-        'spec_ngram_size_n',
-        'spec_ngram_size_m',
-        'spec_ngram_min_hits',
-        'mmproj',
-    ]
-
-    from modules import shared
-    if not shared.args.portable:
-        elements.append('ik')
-
-    return elements
-
-
 def make_loader_params_visible(loader):
-    import gradio as gr
     params = []
     all_params = get_all_params()
     if loader in loaders_and_params:

modules/logits.py

@@ -1,9 +1,11 @@
 import time
+import traceback
 
 import numpy as np
 
 from modules import models, shared
 from modules.logging_colors import logger
+from modules.models import load_model
 from modules.text_generation import generate_reply
 from modules.utils import check_model_loaded
 
@@ -11,7 +13,8 @@ global_scores = None
 
 
 def get_next_logits(*args, **kwargs):
-    models.load_model_if_idle_unloaded()
+    if shared.args.idle_timeout > 0 and shared.model is None and shared.model_name not in [None, 'None']:
+        shared.model, shared.tokenizer = load_model(shared.model_name)
 
     needs_lock = not args[2]  # use_samplers
     if needs_lock:
@@ -20,7 +23,7 @@ def get_next_logits(*args, **kwargs):
     try:
         result = _get_next_logits(*args, **kwargs)
     except Exception:
-        logger.exception("Failed to get next logits")
+        traceback.print_exc()
         result = None
 
     if needs_lock:

modules/models.py

@@ -1,5 +1,4 @@
 import sys
-import threading
 import time
 
 import modules.shared as shared
@@ -8,15 +7,6 @@ from modules.models_settings import get_model_metadata
 from modules.utils import resolve_model_path
 
 last_generation_time = time.time()
-active_generation_count = 0
-_generation_count_lock = threading.Lock()
-
-
-def load_model_if_idle_unloaded():
-    global last_generation_time
-    if shared.args.idle_timeout > 0 and shared.model is None and shared.model_name not in [None, 'None']:
-        shared.model, shared.tokenizer = load_model(shared.model_name)
-        last_generation_time = time.time()
 
 
 def load_model(model_name, loader=None):
@@ -48,9 +38,6 @@ def load_model(model_name, loader=None):
         sampler_hijack.hijack_samplers()
 
     shared.args.loader = loader
-    if loader != 'llama.cpp' and shared.args.ctx_size == 0:
-        shared.args.ctx_size = 8192
-
     output = load_func_map[loader](model_name)
     if type(output) is tuple:
         model, tokenizer = output
@@ -67,8 +54,6 @@ def load_model(model_name, loader=None):
     if loader.lower().startswith('exllama') or loader.lower().startswith('tensorrt') or loader == 'llama.cpp':
         if shared.args.ctx_size > 0:
             shared.settings['truncation_length'] = shared.args.ctx_size
-        elif loader == 'llama.cpp' and hasattr(model, 'n_ctx') and model.n_ctx:
-            shared.settings['truncation_length'] = model.n_ctx
 
     shared.is_multimodal = False
     if loader.lower() in ('exllamav3', 'llama.cpp') and hasattr(model, 'is_multimodal'):
@@ -76,7 +61,8 @@ def load_model(model_name, loader=None):
 
     logger.info(f"Loaded \"{model_name}\" in {(time.time()-t0):.2f} seconds.")
     logger.info(f"LOADER: \"{loader}\"")
-    logger.info(f"CONTEXT LENGTH: {shared.settings['truncation_length']}")
+    logger.info(f"TRUNCATION LENGTH: {shared.settings['truncation_length']}")
+    logger.info(f"INSTRUCTION TEMPLATE: \"{metadata['instruction_template']}\"")
     return model, tokenizer
 
 
@@ -168,10 +154,7 @@ def unload_model_if_idle():
     while True:
         shared.generation_lock.acquire()
         try:
-            with _generation_count_lock:
-                is_active = active_generation_count > 0
-
-            if not is_active and time.time() - last_generation_time > shared.args.idle_timeout * 60:
+            if time.time() - last_generation_time > shared.args.idle_timeout * 60:
                 if shared.model is not None:
                     logger.info("Unloading the model for inactivity.")
                     unload_model(keep_model_name=True)

modules/models_settings.py

@@ -4,9 +4,10 @@ import re
 from math import floor
 from pathlib import Path
 
+import gradio as gr
 import yaml
 
-from modules import loaders, metadata_gguf, shared
+from modules import chat, loaders, metadata_gguf, shared, ui
 from modules.logging_colors import logger
 from modules.utils import resolve_model_path
 
@@ -15,6 +16,9 @@ def get_fallback_settings():
     return {
         'bf16': False,
         'ctx_size': 8192,
+        'rope_freq_base': 0,
+        'compress_pos_emb': 1,
+        'alpha_value': 1,
         'truncation_length': shared.settings['truncation_length'],
         'truncation_length_info': shared.settings['truncation_length'],
         'skip_special_tokens': shared.settings['skip_special_tokens'],
@@ -23,14 +27,18 @@ def get_fallback_settings():
 
 def get_model_metadata(model):
     model_path = resolve_model_path(model)
+    model_settings = {}
 
-    # Fallback settings
-    model_settings = get_fallback_settings()
+    # Get settings from user_data/models/config.yaml and user_data/models/config-user.yaml
+    settings = shared.model_config
+    for pat in settings:
+        if re.match(pat.lower(), Path(model).name.lower()):
+            for k in settings[pat]:
+                model_settings[k] = settings[pat][k]
 
     path = model_path / 'config.json'
     if path.exists():
-        with open(path, 'r', encoding='utf-8') as f:
-            hf_metadata = json.loads(f.read())
+        hf_metadata = json.loads(open(path, 'r', encoding='utf-8').read())
     else:
         hf_metadata = None
 
@@ -60,8 +68,14 @@ def get_model_metadata(model):
 
         for k in metadata:
             if k.endswith('.context_length'):
-                model_settings['ctx_size'] = 0
+                model_settings['ctx_size'] = min(metadata[k], 8192)
                 model_settings['truncation_length_info'] = metadata[k]
+            elif k.endswith('rope.freq_base'):
+                model_settings['rope_freq_base'] = metadata[k]
+            elif k.endswith('rope.scale_linear'):
+                model_settings['compress_pos_emb'] = metadata[k]
+            elif k.endswith('rope.scaling.factor'):
+                model_settings['compress_pos_emb'] = metadata[k]
             elif k.endswith('.block_count'):
                 model_settings['gpu_layers'] = -1
                 model_settings['max_gpu_layers'] = metadata[k] + 1
@@ -89,7 +103,7 @@ def get_model_metadata(model):
     else:
         # Transformers metadata
         if hf_metadata is not None:
-            metadata = hf_metadata
+            metadata = json.loads(open(path, 'r', encoding='utf-8').read())
             if 'pretrained_config' in metadata:
                 metadata = metadata['pretrained_config']
 
@@ -106,6 +120,15 @@ def get_model_metadata(model):
                 model_settings['ctx_size'] = min(value, 8192)
                 break
 
+            if 'rope_theta' in metadata:
+                model_settings['rope_freq_base'] = metadata['rope_theta']
+            elif 'attn_config' in metadata and 'rope_theta' in metadata['attn_config']:
+                model_settings['rope_freq_base'] = metadata['attn_config']['rope_theta']
+
+            if 'rope_scaling' in metadata and isinstance(metadata['rope_scaling'], dict) and all(key in metadata['rope_scaling'] for key in ('type', 'factor')):
+                if metadata['rope_scaling']['type'] == 'linear':
+                    model_settings['compress_pos_emb'] = metadata['rope_scaling']['factor']
+
             if 'torch_dtype' in metadata and metadata['torch_dtype'] == 'bfloat16':
                 model_settings['bf16'] = True
 
@@ -130,8 +153,7 @@ def get_model_metadata(model):
 
     # 3. Fall back to tokenizer_config.json metadata
     if path.exists():
-        with open(path, 'r', encoding='utf-8') as f:
-            metadata = json.loads(f.read())
+        metadata = json.loads(open(path, 'r', encoding='utf-8').read())
 
         # Only read from metadata if we haven't already loaded from .jinja or .json
         if template is None and 'chat_template' in metadata:
@@ -160,6 +182,10 @@ def get_model_metadata(model):
     if 'instruction_template' not in model_settings:
         model_settings['instruction_template'] = 'Alpaca'
 
+    # Ignore rope_freq_base if set to the default value
+    if 'rope_freq_base' in model_settings and model_settings['rope_freq_base'] == 10000:
+        model_settings.pop('rope_freq_base')
+
     # Apply user settings from user_data/models/config-user.yaml
     settings = shared.user_config
     for pat in settings:
@@ -173,7 +199,7 @@ def get_model_metadata(model):
 
     # Load instruction template if defined by name rather than by value
     if model_settings['instruction_template'] != 'Custom (obtained from model metadata)':
-        model_settings['instruction_template_str'] = load_instruction_template(model_settings['instruction_template'])
+        model_settings['instruction_template_str'] = chat.load_instruction_template(model_settings['instruction_template'])
 
     return model_settings
 
@@ -202,7 +228,7 @@ def update_model_parameters(state, initial=False):
     '''
     UI: update the command-line arguments based on the interface values
     '''
-    elements = loaders.list_model_elements()  # the names of the parameters
+    elements = ui.list_model_elements()  # the names of the parameters
 
     for i, element in enumerate(elements):
         if element not in state:
@@ -222,7 +248,6 @@ def apply_model_settings_to_state(model, state):
     '''
     UI: update the state variable with the model settings
     '''
-    import gradio as gr
     model_settings = get_model_metadata(model)
     if 'loader' in model_settings:
         loader = model_settings.pop('loader')
@@ -265,7 +290,7 @@ def save_model_settings(model, state):
     if model_regex not in user_config:
         user_config[model_regex] = {}
 
-    for k in loaders.list_model_elements():
+    for k in ui.list_model_elements():
         if k == 'loader' or k in loaders.loaders_and_params[state['loader']]:
             user_config[model_regex][k] = state[k]
 
@@ -394,108 +419,3 @@ def update_gpu_layers_and_vram(loader, model, gpu_layers, ctx_size, cache_type):
 
     vram_usage = estimate_vram(model, gpu_layers, ctx_size, cache_type)
     return f"<div id=\"vram-info\"'>Estimated VRAM to load the model: <span class=\"value\">{vram_usage:.0f} MiB</span></div>"
-
-
-def load_instruction_template(template):
-    if template == 'None':
-        return ''
-
-    for name in (template, 'Alpaca'):
-        path = shared.user_data_dir / 'instruction-templates' / f'{name}.yaml'
-        try:
-            with open(path, 'r', encoding='utf-8') as f:
-                file_contents = f.read()
-        except FileNotFoundError:
-            if name == template:
-                logger.warning(f"Instruction template '{template}' not found, falling back to Alpaca")
-            continue
-
-        break
-    else:
-        return ''
-    data = yaml.safe_load(file_contents)
-    if 'instruction_template' in data:
-        return data['instruction_template']
-    else:
-        return _jinja_template_from_old_format(data)
-
-
-def _jinja_template_from_old_format(params, verbose=False):
-    MASTER_TEMPLATE = """
-{%- set ns = namespace(found=false) -%}
-{%- for message in messages -%}
-    {%- if message['role'] == 'system' -%}
-        {%- set ns.found = true -%}
-    {%- endif -%}
-{%- endfor -%}
-{%- if not ns.found -%}
-    {{- '<|PRE-SYSTEM|>' + '<|SYSTEM-MESSAGE|>' + '<|POST-SYSTEM|>' -}}
-{%- endif %}
-{%- for message in messages %}
-    {%- if message['role'] == 'system' -%}
-        {{- '<|PRE-SYSTEM|>' + message['content'] + '<|POST-SYSTEM|>' -}}
-    {%- else -%}
-        {%- if message['role'] == 'user' -%}
-            {{-'<|PRE-USER|>' + message['content'] + '<|POST-USER|>'-}}
-        {%- else -%}
-            {{-'<|PRE-ASSISTANT|>' + message['content'] + '<|POST-ASSISTANT|>' -}}
-        {%- endif -%}
-    {%- endif -%}
-{%- endfor -%}
-{%- if add_generation_prompt -%}
-    {{-'<|PRE-ASSISTANT-GENERATE|>'-}}
-{%- endif -%}
-"""
-
-    if 'context' in params and '<|system-message|>' in params['context']:
-        pre_system = params['context'].split('<|system-message|>')[0]
-        post_system = params['context'].split('<|system-message|>')[1]
-    else:
-        pre_system = ''
-        post_system = ''
-
-    pre_user = params['turn_template'].split('<|user-message|>')[0].replace('<|user|>', params['user'])
-    post_user = params['turn_template'].split('<|user-message|>')[1].split('<|bot|>')[0]
-
-    pre_assistant = '<|bot|>' + params['turn_template'].split('<|bot-message|>')[0].split('<|bot|>')[1]
-    pre_assistant = pre_assistant.replace('<|bot|>', params['bot'])
-    post_assistant = params['turn_template'].split('<|bot-message|>')[1]
-
-    def preprocess(string):
-        return string.replace('\n', '\\n').replace('\'', '\\\'')
-
-    pre_system = preprocess(pre_system)
-    post_system = preprocess(post_system)
-    pre_user = preprocess(pre_user)
-    post_user = preprocess(post_user)
-    pre_assistant = preprocess(pre_assistant)
-    post_assistant = preprocess(post_assistant)
-
-    if verbose:
-        print(
-            '\n',
-            repr(pre_system) + '\n',
-            repr(post_system) + '\n',
-            repr(pre_user) + '\n',
-            repr(post_user) + '\n',
-            repr(pre_assistant) + '\n',
-            repr(post_assistant) + '\n',
-        )
-
-    result = MASTER_TEMPLATE
-    if 'system_message' in params:
-        result = result.replace('<|SYSTEM-MESSAGE|>', preprocess(params['system_message']))
-    else:
-        result = result.replace('<|SYSTEM-MESSAGE|>', '')
-
-    result = result.replace('<|PRE-SYSTEM|>', pre_system)
-    result = result.replace('<|POST-SYSTEM|>', post_system)
-    result = result.replace('<|PRE-USER|>', pre_user)
-    result = result.replace('<|POST-USER|>', post_user)
-    result = result.replace('<|PRE-ASSISTANT|>', pre_assistant)
-    result = result.replace('<|PRE-ASSISTANT-GENERATE|>', pre_assistant.rstrip(' '))
-    result = result.replace('<|POST-ASSISTANT|>', post_assistant)
-
-    result = result.strip()
-
-    return result

modules/presets.py

@@ -16,10 +16,9 @@ default_preset_values = {
     'dynatemp_exponent': 1,
     'smoothing_factor': 0,
     'smoothing_curve': 1,
+    'min_p': 0,
     'top_p': 1,
     'top_k': 0,
-    'min_p': 0,
-    'top_n_sigma': 0,
     'typical_p': 1,
     'xtc_threshold': 0.1,
     'xtc_probability': 0,
@@ -27,6 +26,7 @@ default_preset_values = {
     'eta_cutoff': 0,
     'tfs': 1,
     'top_a': 0,
+    'top_n_sigma': 0,
     'adaptive_target': 0,
     'adaptive_decay': 0.9,
     'dry_multiplier': 0,

modules/prompts.py

@@ -1,7 +1,6 @@
 from pathlib import Path
 
 from modules import shared, utils
-from modules.utils import sanitize_filename
 from modules.text_generation import get_encoded_length
 
 
@@ -19,7 +18,6 @@ def load_prompt(fname):
 
         return initial_content
 
-    fname = sanitize_filename(fname)
     file_path = shared.user_data_dir / 'logs' / 'notebook' / f'{fname}.txt'
     if file_path.exists():
         with open(file_path, 'r', encoding='utf-8') as f:

modules/reasoning.py

@@ -1,101 +0,0 @@
-import html as html_module
-
-# Thinking block format definitions: (start_tag, end_tag, content_start_tag)
-# Use None for start_tag to match from beginning (end-only formats should be listed last)
-THINKING_FORMATS = [
-    ('<think>', '</think>', None),
-    ('<|channel|>analysis<|message|>', '<|end|>', '<|channel|>final<|message|>'),
-    ('<|channel|>commentary<|message|>', '<|end|>', '<|channel|>final<|message|>'),
-    ('<seed:think>', '</seed:think>', None),
-    ('<|channel>thought', '<channel|>', None),  # Gemma 4
-    ('<|think|>', '<|end|>', '<|content|>'),  # Solar Open
-    # ('Thinking Process:', '</think>', None),  # Qwen3.5 verbose thinking outside tags -- removed: too prone to false positives in streaming
-    (None, '</think>', None),  # End-only variant (e.g., Qwen3-next)
-]
-
-
-def extract_reasoning(text, html_escaped=False):
-    """Extract reasoning/thinking blocks from the beginning of a string.
-
-    When html_escaped=True, tags are HTML-escaped before searching
-    (for use on already-escaped UI strings).
-
-    Returns (reasoning_content, final_content) where reasoning_content is
-    None if no thinking block is found.
-    """
-    if not text:
-        return None, text
-
-    esc = html_module.escape if html_escaped else lambda s: s
-
-    for start_tag, end_tag, content_tag in THINKING_FORMATS:
-        end_esc = esc(end_tag)
-        content_esc = esc(content_tag) if content_tag else None
-
-        if start_tag is None:
-            # End-only format: require end tag, start from beginning
-            end_pos = text.find(end_esc)
-            if end_pos == -1:
-                continue
-            thought_start = 0
-        else:
-            # Normal format: require start tag
-            start_esc = esc(start_tag)
-            start_pos = text.find(start_esc)
-            if start_pos == -1:
-                # During streaming, the start tag may be arriving partially.
-                # If the text is a prefix of a start tag, return empty content
-                # to prevent the partial tag from leaking.
-                stripped = text.strip()
-                if stripped and start_esc.startswith(stripped):
-                    return '', ''
-                continue
-            thought_start = start_pos + len(start_esc)
-            end_pos = text.find(end_esc, thought_start)
-
-        if end_pos == -1:
-            # End tag missing - check if content tag can serve as fallback
-            if content_esc:
-                content_pos = text.find(content_esc, thought_start)
-                if content_pos != -1:
-                    thought_end = content_pos
-                    content_start = content_pos + len(content_esc)
-                else:
-                    thought_end = len(text)
-                    content_start = len(text)
-            else:
-                thought_end = len(text)
-                content_start = len(text)
-        else:
-            thought_end = end_pos
-            if content_esc:
-                content_pos = text.find(content_esc, end_pos)
-                if content_pos != -1:
-                    content_start = content_pos + len(content_esc)
-                else:
-                    # Content tag not present yet.  In GPT-OSS the region
-                    # between <|end|> and the content tag contains internal
-                    # markup (<|start|>assistant…) that must not be shown.
-                    # Suppress it to prevent tag leaks during streaming.
-                    remainder = text[end_pos + len(end_esc):].lstrip()
-                    framing_token = esc('<|start|>')
-                    if not remainder or remainder.startswith(framing_token) or framing_token.startswith(remainder):
-                        content_start = len(text)
-                    else:
-                        content_start = end_pos + len(end_esc)
-            else:
-                content_start = end_pos + len(end_esc)
-
-        return text[thought_start:thought_end], text[content_start:].lstrip()
-
-    # Handle standalone GPT-OSS final channel marker without a preceding
-    # analysis/commentary block (the model skipped thinking entirely).
-    for marker in ['<|start|>assistant<|channel|>final<|message|>', '<|channel|>final<|message|>']:
-        marker_esc = esc(marker)
-        pos = text.find(marker_esc)
-        if pos != -1:
-            before = text[:pos].strip()
-            after = text[pos + len(marker_esc):]
-            return (before if before else None), after
-
-    return None, text

modules/shared.py

@@ -47,7 +47,7 @@ parser = argparse.ArgumentParser(description="Text Generation Web UI", conflict_
 # Basic settings
 group = parser.add_argument_group('Basic settings')
 group.add_argument('--user-data-dir', type=str, default=str(user_data_dir), help='Path to the user data directory. Default: auto-detected.')
-group.add_argument('--multi-user', action='store_true', help='Multi-user mode. Chat histories are not saved or automatically loaded. Best suited for small trusted teams.')
+group.add_argument('--multi-user', action='store_true', help='Multi-user mode. Chat histories are not saved or automatically loaded. Warning: this is likely not safe for sharing publicly.')
 group.add_argument('--model', type=str, help='Name of the model to load by default.')
 group.add_argument('--lora', type=str, nargs='+', help='The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces.')
 group.add_argument('--model-dir', type=str, default=str(user_data_dir / 'models'), help='Path to directory with all the models.')
@@ -76,7 +76,7 @@ group.add_argument('--loader', type=str, help='Choose the model loader manually,
 
 # Cache
 group = parser.add_argument_group('Context and cache')
-group.add_argument('--ctx-size', '--n_ctx', '--max_seq_len', type=int, default=0, metavar='N', help='Context size in tokens. 0 = auto for llama.cpp (requires gpu-layers=-1), 8192 for other loaders.')
+group.add_argument('--ctx-size', '--n_ctx', '--max_seq_len', type=int, default=8192, metavar='N', help='Context size in tokens. llama.cpp: 0 = auto if gpu-layers is also -1.')
 group.add_argument('--cache-type', '--cache_type', type=str, default='fp16', metavar='N', help='KV cache type; valid options: llama.cpp - fp16, q8_0, q4_0; ExLlamaV3 - fp16, q2 to q8 (can specify k_bits and v_bits separately, e.g. q4_q8).')
 
 # Speculative decoding
@@ -101,16 +101,15 @@ group.add_argument('--tensor-split', type=str, default=None, help='Split the mod
 group.add_argument('--row-split', action='store_true', help='Split the model by rows across GPUs. This may improve multi-gpu performance.')
 group.add_argument('--no-mmap', action='store_true', help='Prevent mmap from being used.')
 group.add_argument('--mlock', action='store_true', help='Force the system to keep the model in RAM.')
-group.add_argument('--no-kv-offload', action='store_true', help='Do not offload the K, Q, V to the GPU. This saves VRAM but reduces performance.')
+group.add_argument('--no-kv-offload', action='store_true', help='Do not offload the  K, Q, V to the GPU. This saves VRAM but reduces the performance.')
 group.add_argument('--batch-size', type=int, default=1024, help='Maximum number of prompt tokens to batch together when calling llama-server. This is the application level batch size.')
 group.add_argument('--ubatch-size', type=int, default=1024, help='Maximum number of prompt tokens to batch together when calling llama-server. This is the max physical batch size for computation (device level).')
 group.add_argument('--threads', type=int, default=0, help='Number of threads to use.')
 group.add_argument('--threads-batch', type=int, default=0, help='Number of threads to use for batches/prompt processing.')
 group.add_argument('--numa', action='store_true', help='Activate NUMA task allocation for llama.cpp.')
 group.add_argument('--parallel', type=int, default=1, help='Number of parallel request slots. The context size is divided equally among slots. For example, to have 4 slots with 8192 context each, set ctx_size to 32768.')
-group.add_argument('--fit-target', type=str, default='512', help='Target VRAM margin per device for auto GPU layers, comma-separated list of values in MiB. A single value is broadcast across all devices.')
-group.add_argument('--extra-flags', type=str, default=None, help='Extra flags to pass to llama-server. Example: "--jinja --rpc 192.168.1.100:50052"')
-group.add_argument('--ik', action='store_true', help='Use ik_llama.cpp instead of upstream llama.cpp. Requires the ik_llama_cpp_binaries package to be installed.')
+group.add_argument('--fit-target', type=str, default='1024', help='Target VRAM margin per device for auto GPU layers, comma-separated list of values in MiB. A single value is broadcast across all devices. Default: 1024.')
+group.add_argument('--extra-flags', type=str, default=None, help='Extra flags to pass to llama-server. Format: "flag1=value1,flag2,flag3=value3". Example: "override-tensor=exps=CPU"')
 
 # Transformers/Accelerate
 group = parser.add_argument_group('Transformers/Accelerate')
@@ -140,6 +139,12 @@ group.add_argument('--enable-tp', '--enable_tp', action='store_true', help='Enab
 group.add_argument('--tp-backend', type=str, default='native', help='The backend for tensor parallelism. Valid options: native, nccl. Default: native.')
 group.add_argument('--cfg-cache', action='store_true', help='Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader.')
 
+# RoPE
+group = parser.add_argument_group('RoPE')
+group.add_argument('--alpha_value', type=float, default=1, help='Positional embeddings alpha factor for NTK RoPE scaling. Use either this or compress_pos_emb, not both.')
+group.add_argument('--rope_freq_base', type=int, default=0, help='If greater than 0, will be used instead of alpha_value. Those two are related by rope_freq_base = 10000 * alpha_value ^ (64 / 63).')
+group.add_argument('--compress_pos_emb', type=int, default=1, help="Positional embeddings compression factor. Should be set to (context length) / (model\'s original context length). Equal to 1/rope_freq_scale.")
+
 # Gradio
 group = parser.add_argument_group('Gradio')
 group.add_argument('--listen', action='store_true', help='Make the web UI reachable from your local network.')
@@ -157,8 +162,8 @@ group.add_argument('--portable', action='store_true', help='Hide features not av
 
 # API
 group = parser.add_argument_group('API')
-group.add_argument('--api', action='store_true', help='Enable the API server.')
-group.add_argument('--public-api', action='store_true', help='Create a public URL for the API using Cloudflare.')
+group.add_argument('--api', action='store_true', help='Enable the API extension.')
+group.add_argument('--public-api', action='store_true', help='Create a public URL for the API using Cloudfare.')
 group.add_argument('--public-api-id', type=str, help='Tunnel ID for named Cloudflare Tunnel. Use together with public-api option.', default=None)
 group.add_argument('--api-port', type=int, default=5000, help='The listening port for the API.')
 group.add_argument('--api-key', type=str, default='', help='API authentication key.')
@@ -176,10 +181,9 @@ group.add_argument('--dynatemp-high', type=float, default=_d['dynatemp_high'], m
 group.add_argument('--dynatemp-exponent', type=float, default=_d['dynatemp_exponent'], metavar='N', help='Dynamic temperature exponent')
 group.add_argument('--smoothing-factor', type=float, default=_d['smoothing_factor'], metavar='N', help='Smoothing factor')
 group.add_argument('--smoothing-curve', type=float, default=_d['smoothing_curve'], metavar='N', help='Smoothing curve')
-group.add_argument('--top-p', type=float, default=0.95, metavar='N', help='Top P')
-group.add_argument('--top-k', type=int, default=_d['top_k'], metavar='N', help='Top K')
 group.add_argument('--min-p', type=float, default=_d['min_p'], metavar='N', help='Min P')
-group.add_argument('--top-n-sigma', type=float, default=_d['top_n_sigma'], metavar='N', help='Top N Sigma')
+group.add_argument('--top-p', type=float, default=_d['top_p'], metavar='N', help='Top P')
+group.add_argument('--top-k', type=int, default=_d['top_k'], metavar='N', help='Top K')
 group.add_argument('--typical-p', type=float, default=_d['typical_p'], metavar='N', help='Typical P')
 group.add_argument('--xtc-threshold', type=float, default=_d['xtc_threshold'], metavar='N', help='XTC threshold')
 group.add_argument('--xtc-probability', type=float, default=_d['xtc_probability'], metavar='N', help='XTC probability')
@@ -187,6 +191,7 @@ group.add_argument('--epsilon-cutoff', type=float, default=_d['epsilon_cutoff'],
 group.add_argument('--eta-cutoff', type=float, default=_d['eta_cutoff'], metavar='N', help='Eta cutoff')
 group.add_argument('--tfs', type=float, default=_d['tfs'], metavar='N', help='TFS')
 group.add_argument('--top-a', type=float, default=_d['top_a'], metavar='N', help='Top A')
+group.add_argument('--top-n-sigma', type=float, default=_d['top_n_sigma'], metavar='N', help='Top N Sigma')
 group.add_argument('--adaptive-target', type=float, default=_d['adaptive_target'], metavar='N', help='Adaptive target')
 group.add_argument('--adaptive-decay', type=float, default=_d['adaptive_decay'], metavar='N', help='Adaptive decay')
 group.add_argument('--dry-multiplier', type=float, default=_d['dry_multiplier'], metavar='N', help='DRY multiplier')
@@ -258,10 +263,8 @@ settings = {
     'chat-instruct_command': 'Continue the chat dialogue below. Write a single reply for the character "<|character|>". Reply directly, without starting the reply with the character name.\n\n<|prompt|>',
     'enable_web_search': False,
     'web_search_pages': 3,
-    'selected_tools': [],
-    'mcp_servers': '',
     'prompt-notebook': '',
-    'preset': 'Top-P' if (user_data_dir / 'presets/Top-P.yaml').exists() else None,
+    'preset': 'Qwen3 - Thinking' if (user_data_dir / 'presets/Qwen3 - Thinking.yaml').exists() else None,
     'max_new_tokens': 512,
     'max_new_tokens_min': 1,
     'max_new_tokens_max': 4096,
@@ -286,7 +289,7 @@ settings = {
     'include_past_attachments': True,
 
     # Generation parameters - Curve shape
-    'temperature': neutral_samplers['temperature'],
+    'temperature': 0.6,
     'dynatemp_low': neutral_samplers['dynatemp_low'],
     'dynatemp_high': neutral_samplers['dynatemp_high'],
     'dynatemp_exponent': neutral_samplers['dynatemp_exponent'],
@@ -294,10 +297,9 @@ settings = {
     'smoothing_curve': neutral_samplers['smoothing_curve'],
 
     # Generation parameters - Curve cutoff
-    'top_p': 0.95,
-    'top_k': neutral_samplers['top_k'],
     'min_p': neutral_samplers['min_p'],
-    'top_n_sigma': neutral_samplers['top_n_sigma'],
+    'top_p': 0.95,
+    'top_k': 20,
     'typical_p': neutral_samplers['typical_p'],
     'xtc_threshold': neutral_samplers['xtc_threshold'],
     'xtc_probability': neutral_samplers['xtc_probability'],
@@ -305,6 +307,7 @@ settings = {
     'eta_cutoff': neutral_samplers['eta_cutoff'],
     'tfs': neutral_samplers['tfs'],
     'top_a': neutral_samplers['top_a'],
+    'top_n_sigma': neutral_samplers['top_n_sigma'],
     'adaptive_target': neutral_samplers['adaptive_target'],
     'adaptive_decay': neutral_samplers['adaptive_decay'],
 
@@ -344,7 +347,7 @@ settings = {
     'greeting': 'How can I help you today?',
     'custom_system_message': '',
     'instruction_template_str': "{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n    {%- if message['role'] == 'system' -%}\n        {%- set ns.found = true -%}\n    {%- endif -%}\n{%- endfor -%}\n{%- if not ns.found -%}\n    {{- '' + 'Below is an instruction that describes a task. Write a response that appropriately completes the request.' + '\\n\\n' -}}\n{%- endif %}\n{%- for message in messages %}\n    {%- if message['role'] == 'system' -%}\n        {{- '' + message['content'] + '\\n\\n' -}}\n    {%- else -%}\n        {%- if message['role'] == 'user' -%}\n            {{-'### Instruction:\\n' + message['content'] + '\\n\\n'-}}\n        {%- else -%}\n            {{-'### Response:\\n' + message['content'] + '\\n\\n' -}}\n        {%- endif -%}\n    {%- endif -%}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n    {{-'### Response:\\n'-}}\n{%- endif -%}",
-    'chat_template_str': "{%- for message in messages %}\n    {%- if message['role'] == 'system' -%}\n        {%- if message['content'] -%}\n            {{- message['content'] + '\\n\\n' -}}\n        {%- endif -%}\n        {%- if user_bio -%}\n            {{- user_bio + '\\n\\n' -}}\n        {%- endif -%}\n    {%- elif message['role'] == 'tool' -%}\n        {{- '[Tool result: ' + message['content'] + ']\\n' -}}\n    {%- elif message['role'] == 'user' -%}\n        {{- name1 + ': ' + message['content'] + '\\n'-}}\n    {%- elif message['tool_calls'] is defined and message['tool_calls'] -%}\n        {%- for tc in message['tool_calls'] -%}\n            {{- '[Calling: ' + tc['function']['name'] + '(' + tc['function']['arguments'] + ')]\\n' -}}\n        {%- endfor -%}\n    {%- else -%}\n        {{- name2 + ': ' + message['content'] + '\\n' -}}\n    {%- endif -%}\n{%- endfor -%}\n{%- if add_generation_prompt %}\n    {{- name2 + ':' -}}\n{%- endif %}",
+    'chat_template_str': "{%- for message in messages %}\n    {%- if message['role'] == 'system' -%}\n        {%- if message['content'] -%}\n            {{- message['content'] + '\\n\\n' -}}\n        {%- endif -%}\n        {%- if user_bio -%}\n            {{- user_bio + '\\n\\n' -}}\n        {%- endif -%}\n    {%- else -%}\n        {%- if message['role'] == 'user' -%}\n            {{- name1 + ': ' + message['content'] + '\\n'-}}\n        {%- else -%}\n            {{- name2 + ': ' + message['content'] + '\\n' -}}\n        {%- endif -%}\n    {%- endif -%}\n{%- endfor -%}\n{%- if add_generation_prompt %}\n    {{- name2 + ':' -}}\n{%- endif %}",
 
     # Extensions
     'default_extensions': [],
@@ -364,7 +367,7 @@ settings = {
     'image_llm_variations_prompt': 'Write a variation of the image generation prompt above. Consider the intent of the user with that prompt and write something that will likely please them, with added details. Output only the new prompt. Do not add any explanations, prefixes, or additional text.',
     'image_model_menu': 'None',
     'image_dtype': 'bfloat16',
-    'image_attn_backend': 'sdpa',
+    'image_attn_backend': 'flash_attention_2',
     'image_cpu_offload': False,
     'image_compile': False,
     'image_quant': 'none',
@@ -392,16 +395,9 @@ def do_cmd_flags_warnings():
         if args.share:
             logger.warning("The gradio \"share link\" feature uses a proprietary executable to create a reverse tunnel. Use it with care.")
         if any((args.listen, args.share)) and not any((args.gradio_auth, args.gradio_auth_path)):
-            logger.warning("You are potentially exposing the web UI to the entire internet without any access password.\nYou can create one with the \"--gradio-auth\" flag like this:\n\n--gradio-auth username:password\n\nMake sure to replace username:password with your own.")
-    if args.multi_user:
-        logger.warning(
-            'Multi-user mode is enabled. Known limitations:'
-            '\n- The Stop button stops generation for all users, not just you.'
-            '\n- Chat history is not saved and will be lost on page refresh.'
-            '\n- Only one user can generate at a time unless using a parallel-capable backend (e.g. llama.cpp with --parallel N for N > 1, or ExLlamaV3).'
-            '\n\nThis mode works best for small trusted teams.'
-            '\n\nDo not expose publicly. Grayed-out actions can easily be bypassed client-side.\n'
-        )
+            logger.warning("\nYou are potentially exposing the web UI to the entire internet without any access password.\nYou can create one with the \"--gradio-auth\" flag like this:\n\n--gradio-auth username:password\n\nMake sure to replace username:password with your own.")
+            if args.multi_user:
+                logger.warning('\nThe multi-user mode is highly experimental and should not be shared publicly.')
 
 
 def apply_image_model_cli_overrides():
@@ -437,6 +433,16 @@ def fix_loader_name(name):
         return 'TensorRT-LLM'
 
 
+def add_extension(name, last=False):
+    if args.extensions is None:
+        args.extensions = [name]
+    elif last:
+        args.extensions = [x for x in args.extensions if x != name]
+        args.extensions.append(name)
+    elif name not in args.extensions:
+        args.extensions.append(name)
+
+
 def is_chat():
     return True
 
@@ -445,18 +451,36 @@ def load_user_config():
     '''
     Loads custom model-specific settings
     '''
-    user_config = {}
     if Path(f'{args.model_dir}/config-user.yaml').exists():
         file_content = open(f'{args.model_dir}/config-user.yaml', 'r').read().strip()
+
         if file_content:
             user_config = yaml.safe_load(file_content)
+        else:
+            user_config = {}
+    else:
+        user_config = {}
 
     return user_config
 
 
 args.loader = fix_loader_name(args.loader)
 
+# Activate the API extension
+if args.api or args.public_api:
+    add_extension('openai', last=True)
+
+# Load model-specific settings
+p = Path(f'{args.model_dir}/config.yaml')
+if p.exists():
+    model_config = yaml.safe_load(open(p, 'r').read())
+else:
+    model_config = {}
+del p
+
+
 # Load custom model-specific settings
 user_config = load_user_config()
 
+model_config = OrderedDict(model_config)
 user_config = OrderedDict(user_config)

modules/text_generation.py

@@ -4,6 +4,7 @@ import html
 import pprint
 import random
 import time
+import traceback
 
 import numpy as np
 
@@ -17,7 +18,9 @@ from modules.utils import check_model_loaded
 
 
 def generate_reply(*args, **kwargs):
-    models.load_model_if_idle_unloaded()
+    if shared.args.idle_timeout > 0 and shared.model is None and shared.model_name not in [None, 'None']:
+        from modules.models import load_model
+        shared.model, shared.tokenizer = load_model(shared.model_name)
 
     state = args[1] if len(args) > 1 else kwargs.get('state', {})
     use_parallel = (
@@ -29,16 +32,10 @@ def generate_reply(*args, **kwargs):
     if not use_parallel:
         shared.generation_lock.acquire()
 
-    with models._generation_count_lock:
-        models.active_generation_count += 1
-
     try:
         for result in _generate_reply(*args, **kwargs):
             yield result
     finally:
-        with models._generation_count_lock:
-            models.active_generation_count -= 1
-
         models.last_generation_time = time.time()
         if not use_parallel:
             shared.generation_lock.release()
@@ -81,13 +78,10 @@ def _generate_reply(question, state, stopping_strings=None, is_chat=False, escap
     reply = ''
     is_stream = state['stream']
     if len(all_stop_strings) > 0 and not state['stream']:
-        original_logits_processor = state.get('logits_processor')
         stop_event_ref = state.pop('stop_event', None)
         state = copy.deepcopy(state)
         if stop_event_ref is not None:
             state['stop_event'] = stop_event_ref
-        if original_logits_processor is not None:
-            state['logits_processor'] = original_logits_processor
         state['stream'] = True
 
     # Generate
@@ -130,9 +124,7 @@ def _generate_reply(question, state, stopping_strings=None, is_chat=False, escap
 
 def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_length=None):
     if shared.tokenizer is None:
-        models.load_model_if_idle_unloaded()
-        if shared.tokenizer is None:
-            raise ValueError('No tokenizer is loaded')
+        raise ValueError('No tokenizer is loaded')
 
     # llama.cpp case
     if shared.model.__class__.__name__ == 'LlamaServer':
@@ -182,9 +174,7 @@ def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_lengt
 
 def decode(output_ids, skip_special_tokens=True):
     if shared.tokenizer is None:
-        models.load_model_if_idle_unloaded()
-        if shared.tokenizer is None:
-            raise ValueError('No tokenizer is loaded')
+        raise ValueError('No tokenizer is loaded')
 
     return shared.tokenizer.decode(output_ids, skip_special_tokens=skip_special_tokens)
 
@@ -385,7 +375,7 @@ def generate_reply_HF(question, original_question, state, stopping_strings=None,
         generate_params['sampler_priority'] = [x.strip() for x in state['sampler_priority'].replace('\n', ',').split(',') if x.strip()]
 
     if state['custom_token_bans']:
-        to_ban = [int(x.strip()) for x in state['custom_token_bans'].split(',') if x.strip()]
+        to_ban = [int(x) for x in state['custom_token_bans'].split(',')]
         if len(to_ban) > 0:
             if generate_params.get('suppress_tokens', None):
                 generate_params['suppress_tokens'] += to_ban
@@ -484,7 +474,7 @@ def generate_reply_HF(question, original_question, state, stopping_strings=None,
                     yield cumulative_reply
 
     except Exception:
-        logger.exception("Failed to generate reply (HF)")
+        traceback.print_exc()
     finally:
         t1 = time.time()
         original_tokens = len(original_input_ids[0])
@@ -517,7 +507,7 @@ def generate_reply_custom(question, original_question, state, stopping_strings=N
                 yield reply
 
     except Exception:
-        logger.exception("Failed to generate reply (custom)")
+        traceback.print_exc()
     finally:
         t1 = time.time()
 

modules/tool_parsing.py

@@ -1,724 +0,0 @@
-import json
-import random
-import re
-
-from modules.reasoning import extract_reasoning
-
-
-def _make_tool_call(name, arguments):
-    return {"type": "function", "function": {"name": name, "arguments": arguments}}
-
-
-def get_tool_call_id() -> str:
-    letter_bytes = "abcdefghijklmnopqrstuvwxyz0123456789"
-    b = [random.choice(letter_bytes) for _ in range(8)]
-    return "call_" + "".join(b).lower()
-
-
-# All known opening markers for tool calls across model formats.
-TOOL_CALL_OPENING_MARKERS = [
-    '<tool_call>',
-    '<function_call>',
-    '<minimax:tool_call>',
-    '<|tool_call_begin|>',
-    '<|tool_calls_section_begin|>',
-    '<｜tool▁call▁begin｜>',
-    '<｜tool▁calls▁begin｜>',
-    '[TOOL_CALLS]',
-    'to=functions.',
-    '<|channel|>commentary',
-    '<|tool_call>call:',
-]
-
-
-def streaming_tool_buffer_check(text, markers=None, tool_names=None, check_bare_names=False, partial_match=True):
-    '''
-    Check whether streaming output should be withheld because it may
-    contain tool-call markup.
-
-    Args:
-        text: Full accumulated internal text.
-        markers: Template-specific markers for partial-prefix matching.
-                 If None, falls back to TOOL_CALL_OPENING_MARKERS.
-        tool_names: List of tool function names.
-        check_bare_names: Whether to do partial-prefix matching on tool
-                          names (for models with unknown template format).
-        partial_match: Whether to check partial prefixes of markers/names.
-                       Set to False for end-of-generation checks where a
-                       partial prefix is just normal text, not an incomplete
-                       tool call.
-    '''
-    # Strip thinking blocks so tool-call syntax inside <think> doesn't
-    # trigger false positives.
-    _, text = extract_reasoning(text)
-
-    # Full marker found in text → buffer permanently.
-    # Always checks ALL known markers regardless of template (cheap safety net).
-    for marker in TOOL_CALL_OPENING_MARKERS:
-        if marker in text:
-            return True
-
-    # Bare function-name full match: "get_weather{...}" or "get_weather {...}"
-    if tool_names:
-        for name in tool_names:
-            if name + '{' in text or name + ' {' in text:
-                return True
-
-    if not partial_match:
-        return False
-
-    # Partial-prefix matching: only for template-specific markers.
-    for marker in (markers if markers is not None else TOOL_CALL_OPENING_MARKERS):
-        for prefix_len in range(min(len(marker) - 1, len(text)), 0, -1):
-            if text.endswith(marker[:prefix_len]):
-                return True
-
-    # Bare-name partial matching: only when template format is unknown.
-    if check_bare_names and tool_names:
-        for name in tool_names:
-            if text.endswith(name):
-                return True
-            for prefix_len in range(min(len(name) - 1, len(text)), 0, -1):
-                if text.endswith(name[:prefix_len]):
-                    return True
-
-    return False
-
-
-def check_and_sanitize_tool_call_candidate(candidate_dict: dict, tool_names: list[str]):
-    # check if property 'function' exists and is a dictionary, otherwise adapt dict
-    if 'function' not in candidate_dict and 'name' in candidate_dict and isinstance(candidate_dict['name'], str):
-        candidate_dict = {"type": "function", "function": candidate_dict}
-    if 'function' in candidate_dict and isinstance(candidate_dict['function'], str):
-        candidate_dict['name'] = candidate_dict['function']
-        del candidate_dict['function']
-        candidate_dict = {"type": "function", "function": candidate_dict}
-    if 'function' in candidate_dict and isinstance(candidate_dict['function'], dict):
-        # check if 'name' exists within 'function' and is part of known tools
-        if 'name' in candidate_dict['function'] and candidate_dict['function']['name'] in tool_names:
-            candidate_dict["type"] = "function"  # ensure required property 'type' exists and has the right value
-            # map property 'parameters' used by some older models to 'arguments'
-            if "arguments" not in candidate_dict["function"] and "parameters" in candidate_dict["function"]:
-                candidate_dict["function"]["arguments"] = candidate_dict["function"]["parameters"]
-                del candidate_dict["function"]["parameters"]
-            return candidate_dict
-    return None
-
-
-def _extract_balanced_json(text: str, start: int) -> str | None:
-    """Extract a balanced JSON object from text starting at the given position.
-
-    Walks through the string tracking brace depth and string boundaries
-    to correctly handle arbitrary nesting levels.
-    """
-    if start >= len(text) or text[start] != '{':
-        return None
-    depth = 0
-    in_string = False
-    escape_next = False
-    for i in range(start, len(text)):
-        c = text[i]
-        if escape_next:
-            escape_next = False
-            continue
-        if c == '\\' and in_string:
-            escape_next = True
-            continue
-        if c == '"':
-            in_string = not in_string
-            continue
-        if in_string:
-            continue
-        if c == '{':
-            depth += 1
-        elif c == '}':
-            depth -= 1
-            if depth == 0:
-                return text[start:i + 1]
-    return None
-
-
-def _parse_channel_tool_calls(answer: str, tool_names: list[str]):
-    """Parse channel-based tool calls used by GPT-OSS and similar models.
-
-    Format:
-        <|start|>assistant to=functions.func_name<|channel|>commentary json<|message|>{"arg": "value"}
-    or:
-        <|channel|>commentary to=functions.func_name <|constrain|>json<|message|>{"arg": "value"}
-    """
-    matches = []
-    start_pos = None
-    # Pattern 1: to=functions.NAME before <|channel|> (GPT-OSS primary format)
-    # Pattern 2: to=functions.NAME after <|channel|> (alternative format)
-    patterns = [
-        r'to=functions\.([^<\s]+)\s*<\|channel\|>[^<]*<\|message\|>',
-        r'<\|channel\|>\w+ to=functions\.([^<\s]+).*?<\|message\|>',
-    ]
-    for pattern in patterns:
-        for m in re.finditer(pattern, answer):
-            func_name = m.group(1).strip()
-            if func_name not in tool_names:
-                continue
-            json_str = _extract_balanced_json(answer, m.end())
-            if json_str is None:
-                continue
-            try:
-                arguments = json.loads(json_str)
-                if start_pos is None:
-                    prefix = answer.rfind('<|start|>assistant', 0, m.start())
-                    start_pos = prefix if prefix != -1 else m.start()
-                matches.append(_make_tool_call(func_name, arguments))
-            except json.JSONDecodeError:
-                pass
-        if matches:
-            break
-    return matches, start_pos
-
-
-def _parse_mistral_token_tool_calls(answer: str, tool_names: list[str]):
-    """Parse Mistral/Devstral-style tool calls with [TOOL_CALLS] and [ARGS] special tokens.
-
-    Format:
-        [TOOL_CALLS]func_name[ARGS]{"arg": "value"}
-    """
-    matches = []
-    start_pos = None
-    for m in re.finditer(
-        r'\[TOOL_CALLS\]\s*(\S+?)\s*\[ARGS\]\s*',
-        answer
-    ):
-        func_name = m.group(1).strip()
-        if func_name not in tool_names:
-            continue
-        json_str = _extract_balanced_json(answer, m.end())
-        if json_str is None:
-            continue
-        try:
-            arguments = json.loads(json_str)
-            if start_pos is None:
-                start_pos = m.start()
-            matches.append(_make_tool_call(func_name, arguments))
-        except json.JSONDecodeError:
-            pass
-    return matches, start_pos
-
-
-def _parse_bare_name_tool_calls(answer: str, tool_names: list[str]):
-    """Parse bare function-name style tool calls used by Mistral and similar models.
-
-    Format:
-        functionName{"arg": "value"}
-    Multiple calls are concatenated directly or separated by whitespace.
-    """
-    matches = []
-    start_pos = None
-    # Match tool name followed by opening brace, then extract balanced JSON
-    escaped_names = [re.escape(name) for name in tool_names]
-    pattern = r'(?:' + '|'.join(escaped_names) + r')\s*\{'
-    for match in re.finditer(pattern, answer):
-        text = match.group(0)
-        name = None
-        for n in tool_names:
-            if text.startswith(n):
-                name = n
-                break
-        if not name:
-            continue
-        brace_start = match.end() - 1
-        json_str = _extract_balanced_json(answer, brace_start)
-        if json_str is None:
-            continue
-        try:
-            arguments = json.loads(json_str)
-            if start_pos is None:
-                start_pos = match.start()
-            matches.append(_make_tool_call(name, arguments))
-        except json.JSONDecodeError:
-            pass
-    return matches, start_pos
-
-
-def _parse_xml_param_tool_calls(answer: str, tool_names: list[str]):
-    """Parse XML-parameter style tool calls used by Qwen3.5 and similar models.
-
-    Format:
-        <tool_call>
-        <function=function_name>
-        <parameter=param_name>value</parameter>
-        </function>
-        </tool_call>
-    """
-    matches = []
-    start_pos = None
-    for tc_match in re.finditer(r'<tool_call>\s*(.*?)\s*</tool_call>', answer, re.DOTALL):
-        tc_content = tc_match.group(1)
-        func_match = re.search(r'<function=([^>]+)>', tc_content)
-        if not func_match:
-            continue
-        func_name = func_match.group(1).strip()
-        if func_name not in tool_names:
-            continue
-        arguments = {}
-        for param_match in re.finditer(r'<parameter=([^>]+)>\s*(.*?)\s*</parameter>', tc_content, re.DOTALL):
-            param_name = param_match.group(1).strip()
-            param_value = param_match.group(2).strip()
-            try:
-                param_value = json.loads(param_value)
-            except (json.JSONDecodeError, ValueError):
-                pass  # keep as string
-            arguments[param_name] = param_value
-        if start_pos is None:
-            start_pos = tc_match.start()
-        matches.append(_make_tool_call(func_name, arguments))
-    return matches, start_pos
-
-
-def _parse_kimi_tool_calls(answer: str, tool_names: list[str]):
-    """Parse Kimi-K2-style tool calls using pipe-delimited tokens.
-
-    Format:
-        <|tool_calls_section_begin|>
-        <|tool_call_begin|>functions.func_name:index<|tool_call_argument_begin|>{"arg": "value"}<|tool_call_end|>
-        <|tool_calls_section_end|>
-    """
-    matches = []
-    start_pos = None
-    for m in re.finditer(
-        r'<\|tool_call_begin\|>\s*(?:functions\.)?(\S+?)(?::\d+)?\s*<\|tool_call_argument_begin\|>\s*',
-        answer
-    ):
-        func_name = m.group(1).strip()
-        if func_name not in tool_names:
-            continue
-        json_str = _extract_balanced_json(answer, m.end())
-        if json_str is None:
-            continue
-        try:
-            arguments = json.loads(json_str)
-            if start_pos is None:
-                # Check for section begin marker before the call marker
-                section = answer.rfind('<|tool_calls_section_begin|>', 0, m.start())
-                start_pos = section if section != -1 else m.start()
-            matches.append(_make_tool_call(func_name, arguments))
-        except json.JSONDecodeError:
-            pass
-    return matches, start_pos
-
-
-def _parse_minimax_tool_calls(answer: str, tool_names: list[str]):
-    """Parse MiniMax-style tool calls using invoke/parameter XML tags.
-
-    Format:
-        <minimax:tool_call>
-        <invoke name="function_name">
-        <parameter name="param_name">value</parameter>
-        </invoke>
-        </minimax:tool_call>
-    """
-    matches = []
-    start_pos = None
-    for tc_match in re.finditer(r'<minimax:tool_call>\s*(.*?)\s*</minimax:tool_call>', answer, re.DOTALL):
-        tc_content = tc_match.group(1)
-        # Split on <invoke> to handle multiple parallel calls in one block
-        for invoke_match in re.finditer(r'<invoke\s+name="([^"]+)">(.*?)</invoke>', tc_content, re.DOTALL):
-            func_name = invoke_match.group(1).strip()
-            if func_name not in tool_names:
-                continue
-            invoke_body = invoke_match.group(2)
-            arguments = {}
-            for param_match in re.finditer(r'<parameter\s+name="([^"]+)">\s*(.*?)\s*</parameter>', invoke_body, re.DOTALL):
-                param_name = param_match.group(1).strip()
-                param_value = param_match.group(2).strip()
-                try:
-                    param_value = json.loads(param_value)
-                except (json.JSONDecodeError, ValueError):
-                    pass  # keep as string
-                arguments[param_name] = param_value
-            if start_pos is None:
-                start_pos = tc_match.start()
-            matches.append(_make_tool_call(func_name, arguments))
-    return matches, start_pos
-
-
-def _parse_deep_seek_tool_calls(answer: str, tool_names: list[str]):
-    """Parse DeepSeek-style tool calls using fullwidth Unicode token delimiters.
-
-    Format:
-        <｜tool▁calls▁begin｜><｜tool▁call▁begin｜>func_name<｜tool▁sep｜>{"arg": "value"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>
-    """
-    matches = []
-    start_pos = None
-    for m in re.finditer(
-        r'<｜tool▁call▁begin｜>\s*(\S+?)\s*<｜tool▁sep｜>\s*',
-        answer
-    ):
-        func_name = m.group(1).strip()
-        if func_name not in tool_names:
-            continue
-        json_str = _extract_balanced_json(answer, m.end())
-        if json_str is None:
-            continue
-        try:
-            arguments = json.loads(json_str)
-            if start_pos is None:
-                # Check for section begin marker before the call marker
-                section = answer.rfind('<｜tool▁calls▁begin｜>', 0, m.start())
-                start_pos = section if section != -1 else m.start()
-            matches.append(_make_tool_call(func_name, arguments))
-        except json.JSONDecodeError:
-            pass
-    return matches, start_pos
-
-
-def _parse_glm_tool_calls(answer: str, tool_names: list[str]):
-    """Parse GLM-style tool calls using arg_key/arg_value XML pairs.
-
-    Format:
-        <tool_call>function_name
-        <arg_key>key1</arg_key>
-        <arg_value>value1</arg_value>
-        </tool_call>
-    """
-    matches = []
-    start_pos = None
-    for tc_match in re.finditer(r'<tool_call>\s*(.*?)\s*</tool_call>', answer, re.DOTALL):
-        tc_content = tc_match.group(1)
-        # First non-tag text is the function name
-        name_match = re.match(r'([^<\s]+)', tc_content.strip())
-        if not name_match:
-            continue
-        func_name = name_match.group(1).strip()
-        if func_name not in tool_names:
-            continue
-        # Extract arg_key/arg_value pairs
-        keys = [k.group(1).strip() for k in re.finditer(r'<arg_key>\s*(.*?)\s*</arg_key>', tc_content, re.DOTALL)]
-        vals = [v.group(1).strip() for v in re.finditer(r'<arg_value>\s*(.*?)\s*</arg_value>', tc_content, re.DOTALL)]
-        if len(keys) != len(vals):
-            continue
-        arguments = {}
-        for k, v in zip(keys, vals):
-            try:
-                v = json.loads(v)
-            except (json.JSONDecodeError, ValueError):
-                pass  # keep as string
-            arguments[k] = v
-        if start_pos is None:
-            start_pos = tc_match.start()
-        matches.append(_make_tool_call(func_name, arguments))
-    return matches, start_pos
-
-
-def _extract_gemma4_balanced(text, start):
-    """Extract balanced braces from Gemma 4 format, using <|"|> as string delimiters."""
-    if start >= len(text) or text[start] != '{':
-        return None
-    depth = 0
-    in_string = False
-    quote_token = '<|"|>'
-    quote_len = len(quote_token)
-    i = start
-    while i < len(text):
-        if text[i:i + quote_len] == quote_token:
-            in_string = not in_string
-            i += quote_len
-            continue
-        if in_string:
-            i += 1
-            continue
-        c = text[i]
-        if c == '{':
-            depth += 1
-        elif c == '}':
-            depth -= 1
-            if depth == 0:
-                return text[start:i + 1]
-        i += 1
-    return None
-
-
-def _parse_gemma4_tool_calls(answer: str, tool_names: list[str]):
-    """Parse Gemma 4-style tool calls.
-
-    Format:
-        <|tool_call>call:func_name{key:<|"|>value<|"|>,...}<tool_call|>
-
-    Values use <|"|> tokens instead of standard JSON quotes, and keys are
-    bare identifiers.
-    """
-    matches = []
-    start_pos = None
-
-    for m in re.finditer(r'<\|tool_call>call:([^\s{]+)\s*', answer):
-        func_name = m.group(1).strip()
-        if func_name not in tool_names:
-            continue
-
-        brace_start = m.end()
-        if brace_start >= len(answer) or answer[brace_start] != '{':
-            continue
-
-        content = _extract_gemma4_balanced(answer, brace_start)
-        if content is None:
-            continue
-
-        # Convert to JSON: split on <|"|> tokens so that key quoting
-        # only applies outside string values (even-indexed parts),
-        # then rejoin with real quotes.
-        parts = content.split('<|"|>')
-        for idx in range(0, len(parts), 2):
-            parts[idx] = re.sub(r'(^|[{,\[])\s*(\w+)\s*:', r'\1"\2":', parts[idx])
-        json_str = '"'.join(parts)
-
-        try:
-            arguments = json.loads(json_str)
-            if start_pos is None:
-                start_pos = m.start()
-            matches.append(_make_tool_call(func_name, arguments))
-        except (json.JSONDecodeError, ValueError):
-            pass
-
-    return matches, start_pos
-
-
-def _parse_pythonic_tool_calls(answer: str, tool_names: list[str]):
-    """Parse pythonic-style tool calls used by Llama 4 and similar models.
-
-    Format:
-        [func_name(param1="value1", param2="value2"), func_name2(...)]
-    """
-    matches = []
-    start_pos = None
-    # Match a bracketed list of function calls
-    bracket_match = re.search(r'\[([^\[\]]+)\]', answer)
-    if not bracket_match:
-        return matches, start_pos
-
-    inner = bracket_match.group(1)
-
-    # Build pattern for known tool names
-    escaped_names = [re.escape(name) for name in tool_names]
-    name_pattern = '|'.join(escaped_names)
-
-    for call_match in re.finditer(
-        r'(' + name_pattern + r')\(([^)]*)\)',
-        inner
-    ):
-        func_name = call_match.group(1)
-        params_str = call_match.group(2).strip()
-        arguments = {}
-
-        if params_str:
-            # Parse key="value" pairs, handling commas inside quoted values
-            for param_match in re.finditer(
-                r'(\w+)\s*=\s*("(?:[^"\\]|\\.)*"|\'(?:[^\'\\]|\\.)*\'|[^,\)]+)',
-                params_str
-            ):
-                param_name = param_match.group(1)
-                param_value = param_match.group(2).strip()
-                # Strip surrounding quotes
-                if (param_value.startswith('"') and param_value.endswith('"')) or \
-                   (param_value.startswith("'") and param_value.endswith("'")):
-                    param_value = param_value[1:-1]
-                # Try to parse as JSON for numeric/bool/null values
-                try:
-                    param_value = json.loads(param_value)
-                except (json.JSONDecodeError, ValueError):
-                    pass
-                arguments[param_name] = param_value
-
-        if start_pos is None:
-            start_pos = bracket_match.start()
-        matches.append(_make_tool_call(func_name, arguments))
-
-    return matches, start_pos
-
-
-# Format registry: maps template substrings to the parser and streaming
-# markers for that format.  When a format's hints are NOT found in the
-# template, its parser and markers are excluded.
-TOOL_CALL_FORMATS = [
-    {
-        'template_hints': ['tool▁call▁begin', 'tool▁calls▁begin'],
-        'parser': _parse_deep_seek_tool_calls,
-        'markers': ['<｜tool▁call▁begin｜>', '<｜tool▁calls▁begin｜>'],
-    },
-    {
-        'template_hints': ['<|tool_call_begin|>', 'tool_calls_section'],
-        'parser': _parse_kimi_tool_calls,
-        'markers': ['<|tool_call_begin|>', '<|tool_calls_section_begin|>'],
-    },
-    {
-        'template_hints': ['to=functions.', '<|channel|>'],
-        'parser': _parse_channel_tool_calls,
-        'markers': ['to=functions.', '<|channel|>commentary'],
-    },
-    {
-        'template_hints': ['<|tool_call>call:'],
-        'parser': _parse_gemma4_tool_calls,
-        'markers': ['<|tool_call>call:'],
-    },
-    {
-        'template_hints': ['minimax:tool_call'],
-        'parser': _parse_minimax_tool_calls,
-        'markers': ['<minimax:tool_call>'],
-    },
-    {
-        'template_hints': ['<arg_key>'],
-        'parser': _parse_glm_tool_calls,
-        'markers': ['<tool_call>'],
-    },
-    {
-        'template_hints': ['<tool_call>'],
-        'parser': _parse_xml_param_tool_calls,
-        'markers': ['<tool_call>'],
-    },
-    {
-        'template_hints': ['[TOOL_CALLS]'],
-        'parser': _parse_mistral_token_tool_calls,
-        'markers': ['[TOOL_CALLS]'],
-    },
-    {
-        'template_hints': ['<function_call>'],
-        'parser': None,
-        'markers': ['<function_call>'],
-    },
-]
-
-# Default ordered list of all specialized parsers.
-ALL_PARSERS = [
-    _parse_deep_seek_tool_calls,
-    _parse_kimi_tool_calls,
-    _parse_channel_tool_calls,
-    _parse_gemma4_tool_calls,
-    _parse_minimax_tool_calls,
-    _parse_glm_tool_calls,
-    _parse_xml_param_tool_calls,
-    _parse_mistral_token_tool_calls,
-    _parse_bare_name_tool_calls,
-    _parse_pythonic_tool_calls,
-]
-
-
-def detect_tool_call_format(template_str):
-    """Inspect a chat/instruction template to determine which tool call
-    formats are relevant.
-
-    Uses an exclude-based approach: starts with all parsers/markers,
-    then removes the ones whose hints are not found in the template.
-
-    Returns (parsers, streaming_markers, check_bare_names).
-    """
-    if not template_str:
-        return None, TOOL_CALL_OPENING_MARKERS, True
-
-    matched_any = False
-    exclude_parsers = []
-    exclude_markers = []
-    matched_markers = []
-
-    for fmt in TOOL_CALL_FORMATS:
-        if any(hint in template_str for hint in fmt['template_hints']):
-            matched_any = True
-            matched_markers.extend(fmt['markers'])
-        else:
-            if fmt['parser'] is not None:
-                exclude_parsers.append(fmt['parser'])
-            exclude_markers.extend(fmt['markers'])
-
-    if not matched_any:
-        return None, TOOL_CALL_OPENING_MARKERS, True
-
-    parsers = [p for p in ALL_PARSERS if p not in exclude_parsers]
-    markers = [m for m in TOOL_CALL_OPENING_MARKERS if m not in exclude_markers or m in matched_markers]
-
-    return parsers, markers, False
-
-
-def parse_tool_call(answer: str, tool_names: list[str], return_prefix: bool = False, parsers: list = None):
-    # Strip thinking blocks so tool-call syntax inside <think> is ignored.
-    original_answer = answer
-    _, answer = extract_reasoning(answer)
-    # Reasoning extraction returns empty content when GPT-OSS internal
-    # markup (<|start|>assistant…) follows the thinking block without a
-    # content tag.  Fall back to the full text so tool-call markers can
-    # be found.
-    if not answer.strip():
-        answer = original_answer
-        reasoning_offset = 0
-    else:
-        reasoning_offset = len(original_answer) - len(answer)
-
-    matches = []
-    start_pos = None
-
-    def _return(matches, start_pos):
-        if return_prefix:
-            prefix = original_answer[:start_pos + reasoning_offset] if matches and start_pos is not None else ''
-            return matches, prefix
-        return matches
-
-    # Try specialized parsers.
-    for parser in (parsers if parsers is not None else ALL_PARSERS):
-        matches, start_pos = parser(answer, tool_names)
-        if matches:
-            return _return(matches, start_pos)
-
-    # Generic fallback: regex pattern to find the JSON content wrapped in <function>, <tools>, <tool_call>, and other tags observed from various models
-    patterns = [r"(```[^\n]*)\n(.*?)```", r"<([^>]+)>(.*?)</\1>"]
-
-    for pattern in patterns:
-        for match in re.finditer(pattern, answer, re.DOTALL):
-            if match.group(2) is None:
-                continue
-            # remove backtick wraps if present
-            candidate = re.sub(r"^```(json|xml|python[^\n]*)\n", "", match.group(2).strip())
-            candidate = re.sub(r"```$", "", candidate.strip())
-            # unwrap inner tags
-            candidate = re.sub(pattern, r"\2", candidate.strip(), flags=re.DOTALL)
-            # llm might have generated multiple json objects separated by linebreaks, check for this pattern and try parsing each object individually
-            if re.search(r"\}\s*\n\s*\{", candidate) is not None:
-                candidate = re.sub(r"\}\s*\n\s*\{", "},\n{", candidate)
-            if not candidate.strip().startswith("["):
-                candidate = "[" + candidate + "]"
-
-            candidates = []
-            try:
-                # parse the candidate JSON into a dictionary
-                candidates = json.loads(candidate)
-                if not isinstance(candidates, list):
-                    candidates = [candidates]
-            except json.JSONDecodeError:
-                # Ignore invalid JSON silently
-                continue
-
-            for candidate_dict in candidates:
-                checked_candidate = check_and_sanitize_tool_call_candidate(candidate_dict, tool_names)
-                if checked_candidate is not None:
-                    if start_pos is None:
-                        start_pos = match.start()
-                    matches.append(checked_candidate)
-
-        # last resort if nothing has been mapped: LLM might have produced plain json tool call without xml-like tags
-        if len(matches) == 0:
-            try:
-                candidate = answer
-                # llm might have generated multiple json objects separated by linebreaks, check for this pattern and try parsing each object individually
-                if re.search(r"\}\s*\n\s*\{", candidate) is not None:
-                    candidate = re.sub(r"\}\s*\n\s*\{", "},\n{", candidate)
-                if not candidate.strip().startswith("["):
-                    candidate = "[" + candidate + "]"
-                # parse the candidate JSON into a dictionary
-                candidates = json.loads(candidate)
-                if not isinstance(candidates, list):
-                    candidates = [candidates]
-                for candidate_dict in candidates:
-                    if not isinstance(candidate_dict, dict):
-                        continue
-                    checked_candidate = check_and_sanitize_tool_call_candidate(candidate_dict, tool_names)
-                    if checked_candidate is not None:
-                        matches.append(checked_candidate)
-            except json.JSONDecodeError:
-                # Ignore invalid JSON silently
-                pass
-
-    return _return(matches, start_pos)

modules/tool_use.py

@@ -1,185 +0,0 @@
-import asyncio
-import importlib.util
-import json
-
-from modules import shared
-from modules.logging_colors import logger
-from modules.utils import natural_keys, sanitize_filename
-
-
-def get_available_tools():
-    """Return sorted list of tool script names from user_data/tools/*.py."""
-    tools_dir = shared.user_data_dir / 'tools'
-    tools_dir.mkdir(parents=True, exist_ok=True)
-    return sorted((p.stem for p in tools_dir.glob('*.py')), key=natural_keys)
-
-
-def load_tools(selected_names):
-    """
-    Import selected tool scripts and return their definitions and executors.
-    Returns (tool_defs, executors) where:
-      - tool_defs: list of OpenAI-format tool dicts
-      - executors: dict mapping function_name -> execute callable
-    """
-    tool_defs = []
-    executors = {}
-    for name in selected_names:
-        name = sanitize_filename(name)
-        if not name:
-            continue
-
-        path = shared.user_data_dir / 'tools' / f'{name}.py'
-        if not path.exists():
-            continue
-
-        try:
-            spec = importlib.util.spec_from_file_location(f"tool_{name}", str(path))
-            module = importlib.util.module_from_spec(spec)
-            spec.loader.exec_module(module)
-        except Exception:
-            logger.exception(f'Failed to load tool script "{name}"')
-            continue
-
-        tool_def = getattr(module, 'tool', None)
-        execute_fn = getattr(module, 'execute', None)
-        if tool_def is None or execute_fn is None:
-            logger.warning(f'Tool "{name}" is missing a "tool" dict or "execute" function.')
-            continue
-
-        func_name = tool_def.get('function', {}).get('name', name)
-        if func_name in executors:
-            logger.warning(f'Tool "{name}" declares function name "{func_name}" which conflicts with an already loaded tool. Skipping.')
-            continue
-        tool_defs.append(tool_def)
-        executors[func_name] = execute_fn
-
-    return tool_defs, executors
-
-
-def _parse_mcp_servers(servers_str):
-    """Parse MCP servers textbox: one server per line, format 'url' or 'url,Header: value,Header2: value2'."""
-    servers = []
-    for line in servers_str.strip().splitlines():
-        line = line.strip()
-        if not line:
-            continue
-        parts = line.split(',')
-        url = parts[0].strip()
-        headers = {}
-        for part in parts[1:]:
-            part = part.strip()
-            if ':' in part:
-                key, val = part.split(':', 1)
-                headers[key.strip()] = val.strip()
-        servers.append((url, headers))
-    return servers
-
-
-def _mcp_tool_to_openai(tool):
-    """Convert an MCP Tool object to OpenAI-format tool dict."""
-    return {
-        "type": "function",
-        "function": {
-            "name": tool.name,
-            "description": tool.description or "",
-            "parameters": tool.inputSchema or {"type": "object", "properties": {}}
-        }
-    }
-
-
-async def _mcp_session(url, headers, callback):
-    """Open an MCP session and pass it to the callback."""
-    from mcp.client.streamable_http import streamablehttp_client
-    from mcp import ClientSession
-
-    async with streamablehttp_client(url, headers=headers or None) as (read_stream, write_stream, _):
-        async with ClientSession(read_stream, write_stream) as session:
-            await session.initialize()
-            return await callback(session)
-
-
-def _make_mcp_executor(name, url, headers):
-    def executor(arguments):
-        return asyncio.run(_call_mcp_tool(name, arguments, url, headers))
-    return executor
-
-
-async def _connect_mcp_server(url, headers):
-    """Connect to one MCP server and return (tool_defs, executors)."""
-
-    async def _discover(session):
-        result = await session.list_tools()
-        tool_defs = []
-        executors = {}
-        for tool in result.tools:
-            tool_defs.append(_mcp_tool_to_openai(tool))
-            executors[tool.name] = _make_mcp_executor(tool.name, url, headers)
-        return tool_defs, executors
-
-    return await _mcp_session(url, headers, _discover)
-
-
-async def _call_mcp_tool(name, arguments, url, headers):
-    """Connect to an MCP server and call a single tool."""
-
-    async def _invoke(session):
-        result = await session.call_tool(name, arguments)
-        parts = []
-        for content in result.content:
-            if hasattr(content, 'text'):
-                parts.append(content.text)
-            else:
-                parts.append(str(content))
-        return '\n'.join(parts) if parts else ''
-
-    return await _mcp_session(url, headers, _invoke)
-
-
-async def _connect_all_mcp_servers(servers):
-    """Connect to all MCP servers concurrently."""
-    results = await asyncio.gather(
-        *(_connect_mcp_server(url, headers) for url, headers in servers),
-        return_exceptions=True
-    )
-    all_defs = []
-    all_executors = {}
-    for (url, _), result in zip(servers, results):
-        if isinstance(result, Exception):
-            logger.exception(f'Failed to connect to MCP server "{url}"', exc_info=result)
-            continue
-        defs, execs = result
-        for td, (fn, ex) in zip(defs, execs.items()):
-            if fn in all_executors:
-                logger.warning(f'MCP tool "{fn}" from {url} conflicts with an already loaded tool. Skipping.')
-                continue
-            all_defs.append(td)
-            all_executors[fn] = ex
-    return all_defs, all_executors
-
-
-def load_mcp_tools(servers_str):
-    """
-    Parse MCP servers string and discover tools from each server.
-    Returns (tool_defs, executors) in the same format as load_tools.
-    """
-    servers = _parse_mcp_servers(servers_str)
-    if not servers:
-        return [], {}
-
-    return asyncio.run(_connect_all_mcp_servers(servers))
-
-
-def execute_tool(func_name, arguments, executors):
-    """Execute a tool by function name. Returns result as a JSON string."""
-    fn = executors.get(func_name)
-    if fn is None:
-        return json.dumps({"error": f"Unknown tool: {func_name}"})
-
-    try:
-        if isinstance(arguments, str):
-            arguments = json.loads(arguments)
-        result = fn(arguments)
-        return json.dumps(result) if not isinstance(result, str) else result
-    except Exception as e:
-        logger.exception(f'Tool "{func_name}" execution failed')
-        return json.dumps({"error": str(e)})

modules/training.py

@@ -26,7 +26,7 @@ from modules.evaluate import (
 from modules.logging_colors import logger
 from modules.models import reload_model
 
-PARAMETERS = ["lora_name", "always_override", "all_linear", "q_proj_en", "v_proj_en", "k_proj_en", "o_proj_en", "gate_proj_en", "down_proj_en", "up_proj_en", "save_steps", "micro_batch_size", "batch_size", "epochs", "learning_rate", "lr_scheduler_type", "lora_rank", "lora_alpha", "lora_dropout", "cutoff_len", "dataset", "eval_dataset", "format", "eval_steps", "text_dataset", "warmup_steps", "optimizer", "stride_length", "stop_at_loss", "add_eos_token", "excess_length", "report_to", "gradient_checkpointing"]
+PARAMETERS = ["lora_name", "always_override", "all_linear", "q_proj_en", "v_proj_en", "k_proj_en", "o_proj_en", "gate_proj_en", "down_proj_en", "up_proj_en", "save_steps", "micro_batch_size", "batch_size", "epochs", "learning_rate", "lr_scheduler_type", "lora_rank", "lora_alpha", "lora_dropout", "cutoff_len", "dataset", "eval_dataset", "format", "eval_steps", "text_dataset", "higher_rank_limit", "warmup_steps", "optimizer", "stride_length", "stop_at_loss", "add_eos_token", "excess_length", "report_to"]
 WANT_INTERRUPT = False
 
 train_log = {}
@@ -52,7 +52,7 @@ def create_ui():
                         with gr.Column():
                             always_override = gr.Checkbox(label='Override Existing Files', value=False, info='If the name is the same, checking will replace the existing file, and unchecking will load and continue from it (the rank must be the same).', elem_classes=['no-background'])
 
-                    with gr.Accordion(label='Target Modules', open=False):
+                    with gr.Accordion(label='Target Modules', open=False, elem_classes='tgw-accordion'):
                         gr.Markdown("Selects which modules to target in training. Targeting more modules is closer to a full fine-tune at the cost of increased VRAM and adapter size.")
                         all_linear = gr.Checkbox(label='Target all linear layers', value=True, info='Targets every nn.Linear layer except lm_head. Works for any model architecture. When checked, the individual module checkboxes below are ignored.', elem_classes=['no-background'])
                         with gr.Row():
@@ -73,8 +73,8 @@ def create_ui():
 
                     with gr.Row():
                         with gr.Column():
-                            lora_rank = gr.Slider(label='LoRA Rank', value=8, minimum=0, maximum=2048, step=4, info='Also called dimension count. Use 4–8 for style/format, 128–256 to teach factual knowledge, 1024+ for comprehensive fine-tuning. Very high ranks require significant VRAM.')
-                            lora_alpha = gr.Slider(label='LoRA Alpha', value=16, minimum=0, maximum=4096, step=4, info='This divided by the rank becomes the scaling of the LoRA. Higher means stronger. A good standard value is twice your Rank.')
+                            lora_rank = gr.Slider(label='LoRA Rank', value=8, minimum=0, maximum=1024, step=4, info='Also called dimension count. Higher values = larger file, more content control. Smaller values = smaller file, less control. Use 4 or 8 for style, 128 or 256 to teach, 1024+ for fine-detail on big data. More VRAM is needed for higher ranks.')
+                            lora_alpha = gr.Slider(label='LoRA Alpha', value=16, minimum=0, maximum=2048, step=4, info='This divided by the rank becomes the scaling of the LoRA. Higher means stronger. A good standard value is twice your Rank.')
                             batch_size = gr.Slider(label='Batch Size', value=32, minimum=0, maximum=1024, step=4, info='Global batch size. The two batch sizes together determine gradient accumulation (gradientAccum = batch / microBatch). Higher gradient accum values lead to better quality training.')
                             micro_batch_size = gr.Slider(label='Micro Batch Size', value=4, minimum=1, maximum=128, step=1, info='Per-device batch size (NOTE: multiple devices not yet implemented). Increasing this will increase VRAM usage.')
                             cutoff_len = gr.Slider(label='Cutoff Length', minimum=0, maximum=4096, value=512, step=32, info='Maximum sequence length in tokens. For instruction datasets, conversations longer than this are dropped. For text datasets, documents are split into chunks of this size. Higher values require more VRAM.')
@@ -87,18 +87,21 @@ def create_ui():
                             with gr.Row():
                                 lr_scheduler_type = gr.Dropdown(label='LR Scheduler', value='cosine', choices=['linear', 'constant', 'constant_with_warmup', 'cosine', 'cosine_with_restarts', 'polynomial', 'inverse_sqrt'], info='Learning rate scheduler - defines how the learning rate changes over time. "Constant" means never change, "linear" means to go in a straight line from the learning rate down to 0, cosine follows a curve, etc.', elem_classes=['slim-dropdown'])
 
-                    with gr.Accordion(label='Advanced Options', open=False):
+                    with gr.Accordion(label='Advanced Options', open=False, elem_classes='tgw-accordion'):
                         with gr.Row():
                             with gr.Column():
-                                optimizer = gr.Dropdown(label='Optimizer', value='adamw_torch', choices=['adamw_hf', 'adamw_torch', 'adamw_torch_fused', 'adamw_torch_xla', 'adamw_apex_fused', 'adafactor', 'adamw_bnb_8bit', 'adamw_anyprecision', 'sgd', 'adagrad'], info='Optimizer algorithm. adamw_torch is the standard choice. adamw_bnb_8bit uses less VRAM. adafactor is memory-efficient for large models.', elem_classes=['slim-dropdown'])
-                                warmup_steps = gr.Number(label='Warmup Steps', value=100, info='For this many steps at the start, the learning rate is gradually ramped up from 0 to the target value. This prevents unstable updates early in training.')
                                 lora_dropout = gr.Slider(label='LoRA Dropout', minimum=0.0, maximum=1.0, step=0.025, value=0.0, info='Percentage probability for dropout of LoRA layers. This can help reduce overfitting. Most users should leave at default.')
                                 stop_at_loss = gr.Slider(label='Stop at loss', minimum=0.0, maximum=3.0, step=0.1, value=0.00, info='The process will automatically stop once the desired loss value is reached. (reasonable numbers are 1.5-1.8)')
+                                with gr.Row():
+                                    optimizer = gr.Dropdown(label='Optimizer', value='adamw_torch', choices=['adamw_hf', 'adamw_torch', 'adamw_torch_fused', 'adamw_torch_xla', 'adamw_apex_fused', 'adafactor', 'adamw_bnb_8bit', 'adamw_anyprecision', 'sgd', 'adagrad'], info='Optimizer algorithm. adamw_torch is the standard choice. adamw_bnb_8bit uses less VRAM. adafactor is memory-efficient for large models.', elem_classes=['slim-dropdown'])
 
                             with gr.Column():
-                                gradient_checkpointing = gr.Checkbox(label='Gradient checkpointing', value=True, info='Trades ~20-30% training speed for reduced VRAM usage by recomputing activations during the backward pass instead of storing them. No impact on accuracy.')
+                                warmup_steps = gr.Number(label='Warmup Steps', value=100, info='For this many steps at the start, the learning rate is gradually ramped up from 0 to the target value. This prevents unstable updates early in training.')
+
                                 add_eos_token = gr.Checkbox(label='Add EOS token', value=True, info="Adds EOS token for each document in text datasets.")
                                 excess_length = gr.Dropdown(label='Excess length', value='drop', choices=['drop', 'truncate'], info='What to do with conversations that exceed the cutoff length. "Drop" removes them entirely (recommended). "Truncate" cuts from the right, which may produce incomplete responses.', elem_classes=['slim-dropdown'])
+
+                                higher_rank_limit = gr.Checkbox(label='Enable higher ranks', value=False, info='If checked, changes Rank/Alpha slider above to go much higher. This will not work without a datacenter-class GPU.')
                                 report_to = gr.Radio(label="Save detailed logs with", value="None", choices=["None", "wandb", "tensorboard"], interactive=True)
 
                 with gr.Column():
@@ -156,12 +159,12 @@ def create_ui():
                 refresh_table = gr.Button('Refresh the table', elem_classes="small-button", interactive=not mu)
 
     # Training events
-    all_params = [lora_name, always_override, all_linear, q_proj_en, v_proj_en, k_proj_en, o_proj_en, gate_proj_en, down_proj_en, up_proj_en, save_steps, micro_batch_size, batch_size, epochs, learning_rate, lr_scheduler_type, lora_rank, lora_alpha, lora_dropout, cutoff_len, dataset, eval_dataset, format, eval_steps, text_dataset, warmup_steps, optimizer, stride_length, stop_at_loss, add_eos_token, excess_length, report_to, gradient_checkpointing]
+    all_params = [lora_name, always_override, all_linear, q_proj_en, v_proj_en, k_proj_en, o_proj_en, gate_proj_en, down_proj_en, up_proj_en, save_steps, micro_batch_size, batch_size, epochs, learning_rate, lr_scheduler_type, lora_rank, lora_alpha, lora_dropout, cutoff_len, dataset, eval_dataset, format, eval_steps, text_dataset, higher_rank_limit, warmup_steps, optimizer, stride_length, stop_at_loss, add_eos_token, excess_length, report_to]
 
     copy_from.change(do_copy_params, [copy_from] + all_params, all_params)
     start_button.click(do_train, all_params, output)
     stop_button.click(do_interrupt, None, None, queue=False)
-
+    higher_rank_limit.change(change_rank_limit, [higher_rank_limit], [lora_rank, lora_alpha])
 
     # Evaluation events. For some reason, the interrupt event
     # doesn't work with the .then() syntax, so I write them one
@@ -206,6 +209,10 @@ def do_copy_params(lora_name: str, *args):
     return result
 
 
+def change_rank_limit(use_higher_ranks: bool):
+    mult = 2 if use_higher_ranks else 1
+    return {"maximum": 1024 * mult, "__type__": "update"}, {"maximum": 2048 * mult, "__type__": "update"}
+
 
 def clean_path(base_path: str, path: str):
     """Strips unusual symbols and forcibly builds a path as relative to the intended directory."""
@@ -286,7 +293,7 @@ def calc_trainable_parameters(model):
     return trainable_params, all_param
 
 
-def do_train(lora_name: str, always_override: bool, all_linear: bool, q_proj_en: bool, v_proj_en: bool, k_proj_en: bool, o_proj_en: bool, gate_proj_en: bool, down_proj_en: bool, up_proj_en: bool, save_steps: int, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: str, lr_scheduler_type: str, lora_rank: int, lora_alpha: int, lora_dropout: float, cutoff_len: int, dataset: str, eval_dataset: str, format: str, eval_steps: int, text_dataset: str, warmup_steps: int, optimizer: str, stride_length: int, stop_at_loss: float, add_eos_token: bool, excess_length: str, report_to: str, gradient_checkpointing: bool = True):
+def do_train(lora_name: str, always_override: bool, all_linear: bool, q_proj_en: bool, v_proj_en: bool, k_proj_en: bool, o_proj_en: bool, gate_proj_en: bool, down_proj_en: bool, up_proj_en: bool, save_steps: int, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: str, lr_scheduler_type: str, lora_rank: int, lora_alpha: int, lora_dropout: float, cutoff_len: int, dataset: str, eval_dataset: str, format: str, eval_steps: int, text_dataset: str, higher_rank_limit: bool, warmup_steps: int, optimizer: str, stride_length: int, stop_at_loss: float, add_eos_token: bool, excess_length: str, report_to: str):
 
     import torch
     import transformers
@@ -303,11 +310,6 @@ def do_train(lora_name: str, always_override: bool, all_linear: bool, q_proj_en:
 
     # == Input validation / processing ==
     yield "Preparing the input..."
-
-    if shared.args.loader == 'llama.cpp':
-        yield "Error: LoRA training requires a model loaded with the Transformers loader. GGUF models are not supported for training."
-        return
-
     lora_file_path = clean_path(None, lora_name)
     if lora_file_path.strip() == '':
         yield "Missing or invalid LoRA file name input."
@@ -546,8 +548,10 @@ def do_train(lora_name: str, always_override: bool, all_linear: bool, q_proj_en:
                     yield f"Failed to load {selected_model}."
                     return
             except Exception:
-                logger.exception('Failed to reload the model.')
-                yield traceback.format_exc().replace('\n', '\n\n')
+                exc = traceback.format_exc()
+                logger.error('Failed to reload the model.')
+                print(exc)
+                yield exc.replace('\n', '\n\n')
                 return
 
     # == Start prepping the model itself ==
@@ -699,7 +703,6 @@ def do_train(lora_name: str, always_override: bool, all_linear: bool, q_proj_en:
             load_best_model_at_end=eval_data is not None,
             # TODO: Enable multi-device support
             ddp_find_unused_parameters=None,
-            gradient_checkpointing=gradient_checkpointing,
             use_cpu=shared.args.cpu,
             remove_unused_columns=False,
         ),
@@ -732,13 +735,11 @@ def do_train(lora_name: str, always_override: bool, all_linear: bool, q_proj_en:
     if lora_all_param > 0:
         print(f"Trainable params: {lora_trainable_param:,d} ({100 * lora_trainable_param / lora_all_param:.4f} %), All params: {lora_all_param:,d} (Model: {model_all_params:,d})")
 
-    train_log.update({
-        "base_model_name": shared.model_name,
-        "base_model_class": shared.model.__class__.__name__,
-        "base_loaded_in_4bit": getattr(lora_model, "is_loaded_in_4bit", False),
-        "base_loaded_in_8bit": getattr(lora_model, "is_loaded_in_8bit", False),
-        "projections": projections_string,
-    })
+    train_log.update({"base_model_name": shared.model_name})
+    train_log.update({"base_model_class": shared.model.__class__.__name__})
+    train_log.update({"base_loaded_in_4bit": getattr(lora_model, "is_loaded_in_4bit", False)})
+    train_log.update({"base_loaded_in_8bit": getattr(lora_model, "is_loaded_in_8bit", False)})
+    train_log.update({"projections": projections_string})
 
     if stop_at_loss > 0:
         print(f"Monitoring loss \033[1;31;1m(Auto-Stop at: {stop_at_loss})\033[0;37;0m")

modules/transformers_loader.py

@@ -44,8 +44,8 @@ class Stream(transformers.StoppingCriteria):
 
 
 class LogitsBiasProcessor(LogitsProcessor):
-    def __init__(self, logit_bias=None):
-        self.logit_bias = logit_bias if logit_bias is not None else {}
+    def __init__(self, logit_bias={}):
+        self.logit_bias = logit_bias
         if self.logit_bias:
             self.keys = list([int(key) for key in self.logit_bias.keys()])
             values = [self.logit_bias[str(key)] for key in self.keys]
@@ -65,16 +65,14 @@ class LogprobProcessor(LogitsProcessor):
     def __init__(self, logprobs=None):
         self.logprobs = logprobs
         self.token_alternatives = {}
-        self.token_alternatives_history = []
 
     def __call__(self, input_ids: torch.LongTensor, logits: torch.FloatTensor) -> torch.FloatTensor:
         if self.logprobs is not None:  # 0-5
             log_e_probabilities = F.log_softmax(logits, dim=1)
-            top_values, top_indices = torch.topk(log_e_probabilities, k=self.logprobs)
+            top_values, top_indices = torch.topk(log_e_probabilities, k=self.logprobs + 1)
             top_tokens = [get_reply_from_output_ids([tok]) for tok in top_indices[0]]
             top_probs = [float(x) for x in top_values[0]]
             self.token_alternatives = dict(zip(top_tokens, top_probs))
-            self.token_alternatives_history.append(self.token_alternatives)
 
         return logits
 
@@ -109,6 +107,7 @@ def load_model_HF(model_name):
     params = {
         'low_cpu_mem_usage': True,
         'attn_implementation': shared.args.attn_implementation,
+        'torch_dtype': torch.bfloat16 if shared.args.bf16 else torch.float16,
     }
 
     if shared.original_args.trust_remote_code:
@@ -119,17 +118,6 @@ def load_model_HF(model_name):
 
     config = AutoConfig.from_pretrained(path_to_model, trust_remote_code=shared.original_args.trust_remote_code)
 
-    # Determine torch_dtype: respect --bf16 flag, otherwise autodetect
-    # from model config, but never allow float32.
-    if shared.args.bf16:
-        params['torch_dtype'] = torch.bfloat16
-    else:
-        dtype = getattr(config, 'torch_dtype', None) or getattr(getattr(config, 'text_config', None), 'torch_dtype', None)
-        if dtype in (torch.float16, torch.bfloat16):
-            params['torch_dtype'] = dtype
-        else:
-            params['torch_dtype'] = torch.float16
-
     if 'chatglm' in model_name.lower():
         LoaderClass = AutoModel
     else:
@@ -146,6 +134,8 @@ def load_model_HF(model_name):
         shared.args.load_in_4bit,
         shared.args.disk,
         shared.args.cpu_memory is not None,
+        shared.args.compress_pos_emb > 1,
+        shared.args.alpha_value > 1,
     ])
 
     # Load the model without any special settings
@@ -208,6 +198,11 @@ def load_model_HF(model_name):
             if shared.args.disk:
                 params['offload_folder'] = str(Path(shared.args.disk_cache_dir))
 
+        if shared.args.compress_pos_emb > 1:
+            params['rope_scaling'] = {'type': 'linear', 'factor': shared.args.compress_pos_emb}
+        elif shared.args.alpha_value > 1:
+            params['rope_scaling'] = {'type': 'dynamic', 'factor': shared.args.alpha_value}
+
         logger.info("TRANSFORMERS_PARAMS=")
         pprint.PrettyPrinter(indent=4, sort_dicts=False).pprint(params)
         print()

modules/ui.py

@@ -66,8 +66,7 @@ theme = gr.themes.Default(
 if not shared.args.old_colors:
     theme = theme.set(
         # General Colors
-        border_color_primary='#d2d2d8',
-        block_border_color='transparent',
+        border_color_primary='#c5c5d2',
         body_text_color_subdued='#484848',
         background_fill_secondary='#eaeaea',
         background_fill_secondary_dark='var(--selected-item-color-dark, #282930)',
@@ -75,15 +74,9 @@ if not shared.args.old_colors:
         background_fill_primary_dark='var(--darker-gray, #1C1C1D)',
         body_background_fill="white",
         block_background_fill="transparent",
-        body_text_color='#1a1a1a',
+        body_text_color='rgb(64, 64, 64)',
         button_secondary_background_fill="white",
         button_secondary_border_color="var(--border-color-primary)",
-        block_title_text_color='*body_text_color',
-        button_primary_background_fill='#374151',
-        button_primary_background_fill_hover='#4b5563',
-        button_primary_background_fill_hover_dark='rgba(255, 255, 255, 0.05)',
-        button_primary_border_color='#374151',
-        button_primary_text_color='white',
         input_shadow="none",
         button_shadow_hover="none",
 
@@ -92,11 +85,11 @@ if not shared.args.old_colors:
         checkbox_background_color_dark='var(--darker-gray, #1C1C1D)',
         block_background_fill_dark='transparent',
         block_border_color_dark='transparent',
-        input_border_color_dark='var(--border-color-dark)',
-        input_border_color_focus_dark='var(--border-color-dark)',
-        checkbox_border_color_dark='rgba(255, 255, 255, 0.2)',
-        border_color_primary_dark='var(--border-color-dark)',
-        button_secondary_border_color_dark='var(--border-color-dark)',
+        input_border_color_dark='var(--border-color-dark, #525252)',
+        input_border_color_focus_dark='var(--border-color-dark, #525252)',
+        checkbox_border_color_dark='var(--border-color-dark, #525252)',
+        border_color_primary_dark='var(--border-color-dark, #525252)',
+        button_secondary_border_color_dark='var(--border-color-dark, #525252)',
         body_background_fill_dark='var(--dark-gray, #212125)',
         button_primary_background_fill_dark='transparent',
         button_secondary_background_fill_dark='transparent',
@@ -114,12 +107,10 @@ if not shared.args.old_colors:
         block_shadow_dark='none',
         input_shadow_focus='none',
         input_shadow_focus_dark='none',
-        button_large_radius='0.75rem',
-        button_small_radius='0.75rem',
+        button_large_radius='0.375rem',
         button_large_padding='6px 12px',
-        input_radius='0.5rem',
-        block_radius='0.375rem',
-        button_transition='background-color 0.15s ease, border-color 0.15s ease, color 0.15s ease',
+        input_radius='0.375rem',
+        block_radius='0',
     )
 
 if (shared.user_data_dir / "notification.mp3").exists():
@@ -129,8 +120,58 @@ else:
 
 
 def list_model_elements():
-    from modules.loaders import list_model_elements
-    return list_model_elements()
+    elements = [
+        'filter_by_loader',
+        'loader',
+        'cpu_memory',
+        'gpu_layers',
+        'fit_target',
+        'cpu_moe',
+        'threads',
+        'threads_batch',
+        'batch_size',
+        'ubatch_size',
+        'ctx_size',
+        'cache_type',
+        'tensor_split',
+        'extra_flags',
+        'streaming_llm',
+        'gpu_split',
+        'alpha_value',
+        'rope_freq_base',
+        'compress_pos_emb',
+        'compute_dtype',
+        'quant_type',
+        'load_in_8bit',
+        'load_in_4bit',
+        'attn_implementation',
+        'cpu',
+        'disk',
+        'row_split',
+        'no_kv_offload',
+        'no_mmap',
+        'mlock',
+        'numa',
+        'parallel',
+        'use_double_quant',
+        'bf16',
+        'enable_tp',
+        'tp_backend',
+        'cfg_cache',
+        'no_use_fast',
+        'model_draft',
+        'draft_max',
+        'gpu_layers_draft',
+        'device_draft',
+        'ctx_size_draft',
+        'spec_type',
+        'spec_ngram_size_n',
+        'spec_ngram_size_m',
+        'spec_ngram_min_hits',
+        'mmproj',
+    ]
+
+    return elements
 
 
 def list_interface_input_elements():
@@ -208,8 +249,6 @@ def list_interface_input_elements():
         'unique_id',
         'textbox',
         'start_with',
-        'selected_tools',
-        'mcp_servers',
         'mode',
         'chat_style',
         'chat-instruct_command',
@@ -301,7 +340,7 @@ def apply_interface_values(state, use_persistent=False):
 
     elements = list_interface_input_elements()
 
-    if not state:
+    if len(state) == 0:
         return [gr.update() for k in elements]  # Dummy, do nothing
     else:
         return [state[k] if k in state else gr.update() for k in elements]
@@ -309,22 +348,19 @@ def apply_interface_values(state, use_persistent=False):
 
 def save_settings(state, preset, extensions_list, show_controls, theme_state, manual_save=False):
     output = copy.deepcopy(shared.settings)
+    exclude = []
     for k in state:
-        if k in shared.settings:
+        if k in shared.settings and k not in exclude:
             output[k] = state[k]
 
-    if preset:
-        output['preset'] = preset
+    output['preset'] = preset
     output['prompt-notebook'] = state['prompt_menu-default'] if state['show_two_notebook_columns'] else state['prompt_menu-notebook']
-    if state.get('character_menu'):
-        output['character'] = state['character_menu']
-    if state.get('user_menu'):
+    output['character'] = state['character_menu']
+    if 'user_menu' in state and state['user_menu']:
         output['user'] = state['user_menu']
     output['seed'] = int(output['seed'])
-    output['custom_stopping_strings'] = output.get('custom_stopping_strings') or ''
-    output['custom_token_bans'] = output.get('custom_token_bans') or ''
     output['show_controls'] = show_controls
-    output['dark_theme'] = theme_state == 'dark'
+    output['dark_theme'] = True if theme_state == 'dark' else False
     output.pop('instruction_template_str')
     output.pop('truncation_length')
 
@@ -434,8 +470,6 @@ def setup_auto_save():
         'user_bio',
         'custom_system_message',
         'chat_template_str',
-        'selected_tools',
-        'mcp_servers',
 
         # Parameters tab (ui_parameters.py) - Generation parameters
         'preset_menu',
@@ -486,6 +520,7 @@ def setup_auto_save():
         'skip_special_tokens',
         'stream',
         'static_cache',
+        'truncation_length',
         'seed',
         'sampler_priority',
         'custom_stopping_strings',

modules/ui_chat.py

@@ -28,8 +28,7 @@ def create_ui():
                     shared.gradio['branch_chat'] = gr.Button('Branch', elem_classes=['refresh-button', 'refresh-button-medium'], elem_id='Branch', interactive=not mu)
                     shared.gradio['rename_chat'] = gr.Button('Rename', elem_classes=['refresh-button', 'refresh-button-medium'], interactive=not mu)
                     shared.gradio['delete_chat'] = gr.Button('🗑️', visible=False, elem_classes='refresh-button', interactive=not mu, elem_id='delete_chat')
-                    shared.gradio['Start new chat'] = gr.Button('New chat', elem_classes=['refresh-button', 'refresh-button-medium', 'focus-on-chat-input'], elem_id='new-chat-btn')
-                    shared.gradio['Start incognito chat'] = gr.Button('Incognito chat', visible=False, elem_id='incognito-chat-btn')
+                    shared.gradio['Start new chat'] = gr.Button('New chat', elem_classes=['refresh-button', 'refresh-button-medium', 'focus-on-chat-input'])
                     shared.gradio['branch_index'] = gr.Number(value=-1, precision=0, visible=False, elem_id="Branch-index", interactive=True)
 
                 shared.gradio['search_chat'] = gr.Textbox(placeholder='Search chats...', max_lines=1, elem_id='search_chat')
@@ -52,7 +51,7 @@ def create_ui():
                 shared.gradio['html_display'] = gr.HTML(value=chat_html_wrapper({'internal': [], 'visible': [], 'metadata': {}}, '', '', 'chat', 'cai-chat', '')['html'], visible=True)
                 with gr.Row(elem_id="chat-input-row"):
                     with gr.Column(scale=1, elem_id='gr-hover-container'):
-                        gr.HTML(value='<div class="hover-element" onclick="void(0)"><span id="hover-element-button"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><line x1="4" y1="6" x2="20" y2="6"></line><line x1="4" y1="12" x2="20" y2="12"></line><line x1="4" y1="18" x2="20" y2="18"></line></svg></span><div class="hover-menu" id="hover-menu"></div></div>', elem_id='gr-hover')
+                        gr.HTML(value='<div class="hover-element" onclick="void(0)"><span style="width: 100px; display: block" id="hover-element-button">&#9776;</span><div class="hover-menu" id="hover-menu"></div>', elem_id='gr-hover')
 
                     with gr.Column(scale=10, elem_id='chat-input-container'):
                         shared.gradio['textbox'] = gr.MultimodalTextbox(label='', placeholder='Send a message', file_types=['text', '.pdf', 'image'], file_count="multiple", elem_id='chat-input', elem_classes=['add_scrollbar'])
@@ -82,7 +81,7 @@ def create_ui():
                 gr.HTML("<div class='sidebar-vertical-separator'></div>")
 
                 shared.gradio['reasoning_effort'] = gr.Dropdown(value=shared.settings['reasoning_effort'], choices=['low', 'medium', 'high'], label='Reasoning effort', info='Used by GPT-OSS.')
-                shared.gradio['enable_thinking'] = gr.Checkbox(value=shared.settings['enable_thinking'], label='Enable thinking', info='For models with thinking support.')
+                shared.gradio['enable_thinking'] = gr.Checkbox(value=shared.settings['enable_thinking'], label='Enable thinking', info='Used by Seed-OSS and pre-2507 Qwen3.')
 
                 gr.HTML("<div class='sidebar-vertical-separator'></div>")
 
@@ -92,24 +91,6 @@ def create_ui():
 
                 gr.HTML("<div class='sidebar-vertical-separator'></div>")
 
-                from modules.tool_use import get_available_tools
-                shared.gradio['selected_tools'] = gr.CheckboxGroup(choices=get_available_tools(), value=shared.settings.get('selected_tools', []), label='Tools', info='Functions the model can call during generation.', elem_id='tools-group')
-                shared.gradio['tools_refresh'] = gr.Button('Refresh list', elem_id='tools-refresh-btn', visible=False)
-                shared.gradio['tools_refresh'].click(fn=lambda: gr.update(choices=get_available_tools()), inputs=[], outputs=[shared.gradio['selected_tools']])
-
-                def sync_web_tools(selected):
-                    if 'web_search' in selected and 'fetch_webpage' not in selected and 'fetch_webpage' in get_available_tools():
-                        selected.append('fetch_webpage')
-
-                    return gr.update(value=selected)
-
-                shared.gradio['selected_tools'].change(fn=sync_web_tools, inputs=[shared.gradio['selected_tools']], outputs=[shared.gradio['selected_tools']], show_progress=False)
-
-                with gr.Accordion('MCP servers', open=False):
-                    shared.gradio['mcp_servers'] = gr.Textbox(value=shared.settings.get('mcp_servers', ''), lines=3, max_lines=3, label='', info='One url per line. For headers, write url,Header: value,Header2: value2', elem_classes=['add_scrollbar'])
-
-                gr.HTML("<div class='sidebar-vertical-separator'></div>")
-
                 with gr.Row():
                     shared.gradio['mode'] = gr.Radio(choices=['instruct', 'chat-instruct', 'chat'], value=None, label='Mode', info='In instruct and chat-instruct modes, the template under Parameters > Instruction template is used.', elem_id='chat-mode')
 
@@ -294,10 +275,6 @@ def create_event_handlers():
         ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
         chat.handle_start_new_chat_click, gradio('interface_state'), gradio('history', 'display', 'unique_id'), show_progress=False)
 
-    shared.gradio['Start incognito chat'].click(
-        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        chat.handle_start_incognito_chat_click, gradio('interface_state'), gradio('history', 'display', 'unique_id'), show_progress=False)
-
     shared.gradio['delete_chat-confirm'].click(
         ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
         chat.handle_delete_chat_confirm_click, gradio('interface_state'), gradio('history', 'display', 'unique_id'), show_progress=False)
@@ -353,13 +330,13 @@ def create_event_handlers():
     shared.gradio['load_template'].click(chat.handle_load_template_click, gradio('instruction_template'), gradio('instruction_template_str', 'instruction_template'), show_progress=False)
     shared.gradio['save_template'].click(
         ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        chat.handle_save_template_click, gradio('instruction_template_str'), gradio('save_filename', 'save_root', 'save_contents', 'save_root_state', 'file_saver'), show_progress=False)
+        chat.handle_save_template_click, gradio('instruction_template_str'), gradio('save_filename', 'save_root', 'save_contents', 'file_saver'), show_progress=False)
 
     shared.gradio['restore_character'].click(
         ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
         chat.restore_character_for_ui, gradio('interface_state'), gradio('interface_state', 'name2', 'context', 'greeting', 'character_picture'), show_progress=False)
 
-    shared.gradio['delete_template'].click(chat.handle_delete_template_click, gradio('instruction_template'), gradio('delete_filename', 'delete_root', 'delete_root_state', 'file_deleter'), show_progress=False)
+    shared.gradio['delete_template'].click(chat.handle_delete_template_click, gradio('instruction_template'), gradio('delete_filename', 'delete_root', 'file_deleter'), show_progress=False)
     shared.gradio['save_chat_history'].click(
         lambda x: json.dumps(x, indent=4), gradio('history'), gradio('temporary_text')).then(
         None, gradio('temporary_text', 'character_menu', 'mode'), None, js=f'(hist, char, mode) => {{{ui.save_files_js}; saveHistory(hist, char, mode)}}')

modules/ui_default.py

@@ -10,7 +10,7 @@ from modules.text_generation import (
     stop_everything_event
 )
 from modules.ui_notebook import store_notebook_state_and_debounce
-from modules.utils import gradio, sanitize_filename
+from modules.utils import gradio
 
 inputs = ('textbox-default', 'interface_state')
 outputs = ('output_textbox', 'html-default')
@@ -167,7 +167,6 @@ def handle_new_prompt():
 
 
 def handle_delete_prompt_confirm_default(prompt_name):
-    prompt_name = sanitize_filename(prompt_name)
     available_prompts = utils.get_available_prompts()
     current_index = available_prompts.index(prompt_name) if prompt_name in available_prompts else 0
 
@@ -200,8 +199,6 @@ def handle_rename_prompt_click_default(current_name):
 
 
 def handle_rename_prompt_confirm_default(new_name, current_name):
-    new_name = sanitize_filename(new_name)
-    current_name = sanitize_filename(current_name)
     old_path = shared.user_data_dir / "logs" / "notebook" / f"{current_name}.txt"
     new_path = shared.user_data_dir / "logs" / "notebook" / f"{new_name}.txt"
 

modules/ui_file_saving.py

@@ -1,19 +1,14 @@
+import traceback
+
 import gradio as gr
 
 from modules import chat, presets, shared, ui, utils
-from modules.logging_colors import logger
 from modules.utils import gradio, sanitize_filename
 
 
 def create_ui():
     mu = shared.args.multi_user
 
-    # Server-side per-session root paths for the generic file saver/deleter.
-    # Set by the handler that opens the dialog, read by the confirm handler.
-    # Using gr.State so they are session-scoped and safe for multi-user.
-    shared.gradio['save_root_state'] = gr.State(None)
-    shared.gradio['delete_root_state'] = gr.State(None)
-
     # Text file saver
     with gr.Group(visible=False, elem_classes='file-saver') as shared.gradio['file_saver']:
         shared.gradio['save_filename'] = gr.Textbox(lines=1, label='File name')
@@ -71,13 +66,13 @@ def create_event_handlers():
         ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
         handle_save_preset_click, gradio('interface_state'), gradio('save_preset_contents', 'save_preset_filename', 'preset_saver'), show_progress=False)
 
-    shared.gradio['delete_preset'].click(handle_delete_preset_click, gradio('preset_menu'), gradio('delete_filename', 'delete_root', 'delete_root_state', 'file_deleter'), show_progress=False)
-    shared.gradio['save_grammar'].click(handle_save_grammar_click, gradio('grammar_string'), gradio('save_contents', 'save_filename', 'save_root', 'save_root_state', 'file_saver'), show_progress=False)
-    shared.gradio['delete_grammar'].click(handle_delete_grammar_click, gradio('grammar_file'), gradio('delete_filename', 'delete_root', 'delete_root_state', 'file_deleter'), show_progress=False)
+    shared.gradio['delete_preset'].click(handle_delete_preset_click, gradio('preset_menu'), gradio('delete_filename', 'delete_root', 'file_deleter'), show_progress=False)
+    shared.gradio['save_grammar'].click(handle_save_grammar_click, gradio('grammar_string'), gradio('save_contents', 'save_filename', 'save_root', 'file_saver'), show_progress=False)
+    shared.gradio['delete_grammar'].click(handle_delete_grammar_click, gradio('grammar_file'), gradio('delete_filename', 'delete_root', 'file_deleter'), show_progress=False)
 
     shared.gradio['save_preset_confirm'].click(handle_save_preset_confirm_click, gradio('save_preset_filename', 'save_preset_contents'), gradio('preset_menu', 'preset_saver'), show_progress=False)
-    shared.gradio['save_confirm'].click(handle_save_confirm_click, gradio('save_root_state', 'save_filename', 'save_contents'), gradio('save_root_state', 'file_saver'), show_progress=False)
-    shared.gradio['delete_confirm'].click(handle_delete_confirm_click, gradio('delete_root_state', 'delete_filename'), gradio('delete_root_state', 'file_deleter'), show_progress=False)
+    shared.gradio['save_confirm'].click(handle_save_confirm_click, gradio('save_root', 'save_filename', 'save_contents'), gradio('file_saver'), show_progress=False)
+    shared.gradio['delete_confirm'].click(handle_delete_confirm_click, gradio('delete_root', 'delete_filename'), gradio('file_deleter'), show_progress=False)
     shared.gradio['save_character_confirm'].click(handle_save_character_confirm_click, gradio('name2', 'greeting', 'context', 'character_picture', 'save_character_filename'), gradio('character_menu', 'character_saver'), show_progress=False)
     shared.gradio['delete_character_confirm'].click(handle_delete_character_confirm_click, gradio('character_menu'), gradio('character_menu', 'character_deleter'), show_progress=False)
 
@@ -102,7 +97,7 @@ def handle_save_preset_confirm_click(filename, contents):
         output = gr.update(choices=available_presets, value=filename)
     except Exception:
         output = gr.update()
-        logger.exception("Failed to save preset")
+        traceback.print_exc()
 
     return [
         output,
@@ -110,30 +105,24 @@ def handle_save_preset_confirm_click(filename, contents):
     ]
 
 
-def handle_save_confirm_click(root_state, filename, contents):
+def handle_save_confirm_click(root, filename, contents):
     try:
-        if root_state is None:
-            return None, gr.update(visible=False)
-
         filename = sanitize_filename(filename)
-        utils.save_file(root_state + filename, contents)
+        utils.save_file(root + filename, contents)
     except Exception:
-        logger.exception("Failed to save file")
+        traceback.print_exc()
 
-    return None, gr.update(visible=False)
+    return gr.update(visible=False)
 
 
-def handle_delete_confirm_click(root_state, filename):
+def handle_delete_confirm_click(root, filename):
     try:
-        if root_state is None:
-            return None, gr.update(visible=False)
-
         filename = sanitize_filename(filename)
-        utils.delete_file(root_state + filename)
+        utils.delete_file(root + filename)
     except Exception:
-        logger.exception("Failed to delete file")
+        traceback.print_exc()
 
-    return None, gr.update(visible=False)
+    return gr.update(visible=False)
 
 
 def handle_save_character_confirm_click(name2, greeting, context, character_picture, filename):
@@ -143,7 +132,7 @@ def handle_save_character_confirm_click(name2, greeting, context, character_pict
         output = gr.update(choices=available_characters, value=filename)
     except Exception:
         output = gr.update()
-        logger.exception("Failed to save character")
+        traceback.print_exc()
 
     return [
         output,
@@ -158,7 +147,7 @@ def handle_delete_character_confirm_click(character):
         output = chat.update_character_menu_after_deletion(index)
     except Exception:
         output = gr.update()
-        logger.exception("Failed to delete character")
+        traceback.print_exc()
 
     return [
         output,
@@ -176,32 +165,26 @@ def handle_save_preset_click(state):
 
 
 def handle_delete_preset_click(preset):
-    root = str(shared.user_data_dir / "presets") + "/"
     return [
         f"{preset}.yaml",
-        root,
-        root,
+        str(shared.user_data_dir / "presets") + "/",
         gr.update(visible=True)
     ]
 
 
 def handle_save_grammar_click(grammar_string):
-    root = str(shared.user_data_dir / "grammars") + "/"
     return [
         grammar_string,
         "My Fancy Grammar.gbnf",
-        root,
-        root,
+        str(shared.user_data_dir / "grammars") + "/",
         gr.update(visible=True)
     ]
 
 
 def handle_delete_grammar_click(grammar_file):
-    root = str(shared.user_data_dir / "grammars") + "/"
     return [
         grammar_file,
-        root,
-        root,
+        str(shared.user_data_dir / "grammars") + "/",
         gr.update(visible=True)
     ]
 
@@ -213,7 +196,7 @@ def handle_save_user_confirm_click(name1, user_bio, your_picture, filename):
         output = gr.update(choices=available_users, value=filename)
     except Exception:
         output = gr.update()
-        logger.exception("Failed to save user")
+        traceback.print_exc()
 
     return [
         output,
@@ -228,7 +211,7 @@ def handle_delete_user_confirm_click(user):
         output = chat.update_user_menu_after_deletion(index)
     except Exception:
         output = gr.update()
-        logger.exception("Failed to delete user")
+        traceback.print_exc()
 
     return [
         output,

modules/ui_image_generation.py

@@ -728,8 +728,6 @@ def generate_prompt_variation(state):
         variation = variation.rsplit("</think>", 1)[1]
     elif "<|start|>assistant<|channel|>final<|message|>" in variation:
         variation = variation.rsplit("<|start|>assistant<|channel|>final<|message|>", 1)[1]
-    elif "<|channel|>final<|message|>" in variation:
-        variation = variation.rsplit("<|channel|>final<|message|>", 1)[1]
     elif "</seed:think>" in variation:
         variation = variation.rsplit("</seed:think>", 1)[1]
 
@@ -798,9 +796,6 @@ def generate(state, save_images=True):
         if seed == -1:
             seed = random.randint(0, 2**32 - 1)
 
-        # Store resolved seed back so callers (e.g. API) can access it
-        state['image_seed_resolved'] = seed
-
         device = get_device()
         if device is None:
             device = "cpu"
@@ -919,8 +914,9 @@ def generate(state, save_images=True):
         yield all_images, progress_bar_html()
         clear_torch_cache()
 
-    except Exception:
-        logger.exception("Image generation failed")
+    except Exception as e:
+        logger.error(f"Image generation failed: {e}")
+        traceback.print_exc()
         yield [], progress_bar_html()
         clear_torch_cache()
 

modules/ui_model_menu.py

@@ -42,18 +42,16 @@ def create_ui():
                     with gr.Row():
                         with gr.Column():
                             shared.gradio['gpu_layers'] = gr.Slider(label="gpu-layers", minimum=-1, maximum=get_initial_gpu_layers_max(), step=1, value=shared.args.gpu_layers, info='Number of layers to offload to the GPU. -1 = auto.')
-                            shared.gradio['ctx_size'] = gr.Slider(label='ctx-size', minimum=0, maximum=1048576, step=1024, value=shared.args.ctx_size, info='Context length. 0 = auto for llama.cpp (requires gpu-layers=-1), 8192 for other loaders. Common values: 4096, 8192, 16384, 32768, 65536, 131072.')
+                            shared.gradio['ctx_size'] = gr.Slider(label='ctx-size', minimum=0, maximum=131072, step=256, value=shared.args.ctx_size, info='Context length. llama.cpp: 0 = auto if gpu-layers is also -1. Common values: 4096, 8192, 16384, 32768, 65536, 131072.')
                             shared.gradio['gpu_split'] = gr.Textbox(label='gpu-split', info='Comma-separated list of VRAM (in GB) to use per GPU. Example: 20,7,7')
                             shared.gradio['attn_implementation'] = gr.Dropdown(label="attn-implementation", choices=['sdpa', 'eager', 'flash_attention_2'], value=shared.args.attn_implementation, info='Attention implementation.')
                             shared.gradio['cache_type'] = gr.Dropdown(label="cache-type", choices=['fp16', 'q8_0', 'q4_0', 'fp8', 'q8', 'q7', 'q6', 'q5', 'q4', 'q3', 'q2'], value=shared.args.cache_type, allow_custom_value=True, info='Valid options: llama.cpp - fp16, q8_0, q4_0; ExLlamaV3 - fp16, q2 to q8. For ExLlamaV3, you can type custom combinations for separate k/v bits (e.g. q4_q8).')
-                            shared.gradio['fit_target'] = gr.Textbox(label='fit-target', value=shared.args.fit_target, info='Target VRAM margin per device for auto GPU layers (MiB). Comma-separated list for multiple devices.')
+                            shared.gradio['fit_target'] = gr.Textbox(label='fit-target', value=shared.args.fit_target, info='Target VRAM margin per device for auto GPU layers (MiB). Comma-separated list for multiple devices. Default: 1024.')
                             shared.gradio['tp_backend'] = gr.Dropdown(label="tp-backend", choices=['native', 'nccl'], value=shared.args.tp_backend, info='The backend for tensor parallelism.')
 
                         with gr.Column():
                             shared.gradio['vram_info'] = gr.HTML(value=get_initial_vram_info())
-                            if not shared.args.portable:
-                                shared.gradio['ik'] = gr.Checkbox(label="ik", value=shared.args.ik, info='Use ik_llama.cpp instead of upstream llama.cpp.')
-
+                            shared.gradio['cpu_moe'] = gr.Checkbox(label="cpu-moe", value=shared.args.cpu_moe, info='Move the experts to the CPU. Saves VRAM on MoE models.')
                             shared.gradio['streaming_llm'] = gr.Checkbox(label="streaming-llm", value=shared.args.streaming_llm, info='Activate StreamingLLM to avoid re-evaluating the entire prompt when old messages are removed.')
                             shared.gradio['load_in_8bit'] = gr.Checkbox(label="load-in-8bit", value=shared.args.load_in_8bit)
                             shared.gradio['load_in_4bit'] = gr.Checkbox(label="load-in-4bit", value=shared.args.load_in_4bit)
@@ -66,13 +64,13 @@ def create_ui():
                             )
 
                             # Multimodal
-                            with gr.Accordion("Multimodal (vision)", open=False) as shared.gradio['mmproj_accordion']:
+                            with gr.Accordion("Multimodal (vision)", open=False, elem_classes='tgw-accordion') as shared.gradio['mmproj_accordion']:
                                 with gr.Row():
                                     shared.gradio['mmproj'] = gr.Dropdown(label="mmproj file", choices=utils.get_available_mmproj(), value=lambda: shared.args.mmproj or 'None', elem_classes='slim-dropdown', info=f'Select a file that matches your model. Must be placed in {shared.user_data_dir}/mmproj/', interactive=not mu)
                                     ui.create_refresh_button(shared.gradio['mmproj'], lambda: None, lambda: {'choices': utils.get_available_mmproj()}, 'refresh-button', interactive=not mu)
 
                             # Speculative decoding
-                            with gr.Accordion("Speculative decoding", open=False) as shared.gradio['speculative_decoding_accordion']:
+                            with gr.Accordion("Speculative decoding", open=False, elem_classes='tgw-accordion') as shared.gradio['speculative_decoding_accordion']:
                                 shared.gradio['draft_max'] = gr.Number(label="draft-max", precision=0, step=1, value=shared.args.draft_max, info='Maximum number of tokens to draft for speculative decoding. Recommended: 4 for draft model, 64 for n-gram.')
 
                                 gr.Markdown('#### Draft model')
@@ -91,7 +89,7 @@ def create_ui():
                                 shared.gradio['spec_ngram_min_hits'] = gr.Number(label="spec-ngram-min-hits", precision=0, step=1, value=shared.args.spec_ngram_min_hits, info='Minimum n-gram hits for ngram-map speculative decoding.', visible=shared.args.spec_type != 'none')
 
                     gr.Markdown("## Other options")
-                    with gr.Accordion("See more options", open=False):
+                    with gr.Accordion("See more options", open=False, elem_classes='tgw-accordion'):
                         with gr.Row():
                             with gr.Column():
                                 shared.gradio['parallel'] = gr.Slider(label="parallel", minimum=1, step=1, maximum=64, value=shared.args.parallel, info='Number of parallel request slots for the API. The context size is divided equally among slots. For example, to have 4 slots with 8192 context each, set ctx_size to 32768.')
@@ -100,17 +98,19 @@ def create_ui():
                                 shared.gradio['batch_size'] = gr.Slider(label="batch_size", minimum=1, maximum=4096, step=1, value=shared.args.batch_size)
                                 shared.gradio['ubatch_size'] = gr.Slider(label="ubatch_size", minimum=1, maximum=4096, step=1, value=shared.args.ubatch_size)
                                 shared.gradio['tensor_split'] = gr.Textbox(label='tensor_split', info='List of proportions to split the model across multiple GPUs. Example: 60,40')
-                                shared.gradio['extra_flags'] = gr.Textbox(label='extra-flags', info='Extra flags to pass to llama-server. Example: --jinja --rpc 192.168.1.100:50052', value=shared.args.extra_flags)
+                                shared.gradio['extra_flags'] = gr.Textbox(label='extra-flags', info='Additional flags to pass to llama-server. Format: "flag1=value1,flag2,flag3=value3". Example: "override-tensor=exps=CPU"', value=shared.args.extra_flags)
                                 shared.gradio['cpu_memory'] = gr.Number(label="Maximum CPU memory in GiB. Use this for CPU offloading.", value=shared.args.cpu_memory)
+                                shared.gradio['alpha_value'] = gr.Number(label='alpha_value', value=shared.args.alpha_value, precision=2, info='Positional embeddings alpha factor for NTK RoPE scaling. Recommended values (NTKv1): 1.75 for 1.5x context, 2.5 for 2x context. Use either this or compress_pos_emb, not both.')
+                                shared.gradio['rope_freq_base'] = gr.Number(label='rope_freq_base', value=shared.args.rope_freq_base, precision=0, info='Positional embeddings frequency base for NTK RoPE scaling. Related to alpha_value by rope_freq_base = 10000 * alpha_value ^ (64 / 63). 0 = from model.')
+                                shared.gradio['compress_pos_emb'] = gr.Number(label='compress_pos_emb', value=shared.args.compress_pos_emb, precision=2, info='Positional embeddings compression factor. Should be set to (context length) / (model\'s original context length). Equal to 1/rope_freq_scale.')
                                 shared.gradio['compute_dtype'] = gr.Dropdown(label="compute_dtype", choices=["bfloat16", "float16", "float32"], value=shared.args.compute_dtype, info='Used by load-in-4bit.')
                                 shared.gradio['quant_type'] = gr.Dropdown(label="quant_type", choices=["nf4", "fp4"], value=shared.args.quant_type, info='Used by load-in-4bit.')
 
                             with gr.Column():
                                 shared.gradio['cpu'] = gr.Checkbox(label="cpu", value=shared.args.cpu, info='Use PyTorch in CPU mode.')
                                 shared.gradio['disk'] = gr.Checkbox(label="disk", value=shared.args.disk)
-                                shared.gradio['cpu_moe'] = gr.Checkbox(label="cpu-moe", value=shared.args.cpu_moe, info='Move the experts to the CPU. Saves VRAM on MoE models.')
                                 shared.gradio['row_split'] = gr.Checkbox(label="row_split", value=shared.args.row_split, info='Split the model by rows across GPUs. This may improve multi-gpu performance.')
-                                shared.gradio['no_kv_offload'] = gr.Checkbox(label="no_kv_offload", value=shared.args.no_kv_offload, info='Do not offload the K, Q, V to the GPU. This saves VRAM but reduces performance.')
+                                shared.gradio['no_kv_offload'] = gr.Checkbox(label="no_kv_offload", value=shared.args.no_kv_offload, info='Do not offload the  K, Q, V to the GPU. This saves VRAM but reduces the performance.')
                                 shared.gradio['no_mmap'] = gr.Checkbox(label="no-mmap", value=shared.args.no_mmap)
                                 shared.gradio['mlock'] = gr.Checkbox(label="mlock", value=shared.args.mlock)
                                 shared.gradio['numa'] = gr.Checkbox(label="numa", value=shared.args.numa, info='NUMA support can help on some systems with non-uniform memory access.')
@@ -137,7 +137,7 @@ def create_ui():
                         ui.create_refresh_button(shared.gradio['customized_template'], lambda: None, lambda: {'choices': utils.get_available_instruction_templates()}, 'refresh-button', interactive=not mu)
 
                     shared.gradio['customized_template_submit'] = gr.Button("Submit", variant="primary", interactive=not mu)
-                    gr.Markdown("This allows you to set a customized template for the model currently selected in the \"Model loader\" menu. Whenever the model gets loaded, this template will be used in place of the template specified in the model's metadata, which sometimes is wrong.")
+                    gr.Markdown("This allows you to set a customized template for the model currently selected in the \"Model loader\" menu. Whenever the model gets loaded, this template will be used in place of the template specified in the model's medatada, which sometimes is wrong.")
 
                 with gr.Row():
                     shared.gradio['model_status'] = gr.Markdown('No model is loaded' if shared.model_name == 'None' else 'Ready')
@@ -225,14 +225,16 @@ def load_model_wrapper(selected_model, loader, autoload=False):
             else:
                 yield f"Failed to load `{selected_model}`."
         except Exception:
-            logger.exception('Failed to load the model.')
-            yield traceback.format_exc().replace('\n', '\n\n')
+            exc = traceback.format_exc()
+            logger.error('Failed to load the model.')
+            print(exc)
+            yield exc.replace('\n', '\n\n')
 
 
 def load_lora_wrapper(selected_loras):
     yield ("Applying the following LoRAs to {}:\n\n{}".format(shared.model_name, '\n'.join(selected_loras)))
     add_lora_to_model(selected_loras)
-    yield ("Successfully applied the LoRAs")
+    yield ("Successfuly applied the LoRAs")
 
 
 def download_model_wrapper(repo_id, specific_file, progress=gr.Progress(), return_links=False, check=False):
@@ -386,11 +388,7 @@ def download_model_wrapper(repo_id, specific_file, progress=gr.Progress(), retur
 def update_truncation_length(current_length, state):
     if 'loader' in state:
         if state['loader'].lower().startswith('exllama') or state['loader'] == 'llama.cpp':
-            if state['ctx_size'] > 0:
-                return state['ctx_size']
-
-            # ctx_size == 0 means auto: use the actual value from the server
-            return shared.settings['truncation_length']
+            return state['ctx_size']
 
     return current_length
 

modules/ui_notebook.py

@@ -11,7 +11,7 @@ from modules.text_generation import (
     get_token_ids,
     stop_everything_event
 )
-from modules.utils import gradio, sanitize_filename
+from modules.utils import gradio
 
 _notebook_file_lock = threading.Lock()
 _notebook_auto_save_timer = None
@@ -202,7 +202,6 @@ def handle_new_prompt():
 
 
 def handle_delete_prompt_confirm_notebook(prompt_name):
-    prompt_name = sanitize_filename(prompt_name)
     available_prompts = utils.get_available_prompts()
     current_index = available_prompts.index(prompt_name) if prompt_name in available_prompts else 0
 
@@ -234,8 +233,6 @@ def handle_rename_prompt_click_notebook(current_name):
 
 
 def handle_rename_prompt_confirm_notebook(new_name, current_name):
-    new_name = sanitize_filename(new_name)
-    current_name = sanitize_filename(current_name)
     old_path = shared.user_data_dir / "logs" / "notebook" / f"{current_name}.txt"
     new_path = shared.user_data_dir / "logs" / "notebook" / f"{new_name}.txt"
 
@@ -252,7 +249,6 @@ def handle_rename_prompt_confirm_notebook(new_name, current_name):
 
 def autosave_prompt(text, prompt_name):
     """Automatically save the text to the selected prompt file"""
-    prompt_name = sanitize_filename(prompt_name)
     if prompt_name and text.strip():
         prompt_path = shared.user_data_dir / "logs" / "notebook" / f"{prompt_name}.txt"
         prompt_path.parent.mkdir(parents=True, exist_ok=True)

modules/ui_parameters.py

@@ -37,10 +37,10 @@ def create_ui():
                             shared.gradio['dynamic_temperature'] = gr.Checkbox(value=shared.settings['dynamic_temperature'], label='dynamic_temperature')
 
                             gr.Markdown('## Curve cutoff')
-                            shared.gradio['top_p'] = gr.Slider(0.0, 1.0, value=shared.settings['top_p'], step=0.01, label='top_p')
-                            shared.gradio['top_k'] = gr.Slider(0, 200, value=shared.settings['top_k'], step=1, label='top_k')
                             shared.gradio['min_p'] = gr.Slider(0.0, 1.0, value=shared.settings['min_p'], step=0.01, label='min_p')
                             shared.gradio['top_n_sigma'] = gr.Slider(0.0, 5.0, value=shared.settings['top_n_sigma'], step=0.01, label='top_n_sigma')
+                            shared.gradio['top_p'] = gr.Slider(0.0, 1.0, value=shared.settings['top_p'], step=0.01, label='top_p')
+                            shared.gradio['top_k'] = gr.Slider(0, 200, value=shared.settings['top_k'], step=1, label='top_k')
                             shared.gradio['typical_p'] = gr.Slider(0.0, 1.0, value=shared.settings['typical_p'], step=0.01, label='typical_p')
                             shared.gradio['xtc_threshold'] = gr.Slider(0, 0.5, value=shared.settings['xtc_threshold'], step=0.01, label='xtc_threshold', info='If 2 or more tokens have probability above this threshold, consider removing all but the last one.')
                             shared.gradio['xtc_probability'] = gr.Slider(0, 1, value=shared.settings['xtc_probability'], step=0.01, label='xtc_probability', info='Probability that the removal will actually happen. 0 disables the sampler. 1 makes it always happen.')
@@ -73,7 +73,7 @@ def create_ui():
                             gr.Markdown('## Other options')
                             shared.gradio['do_sample'] = gr.Checkbox(value=shared.settings['do_sample'], label='do_sample')
                             shared.gradio['temperature_last'] = gr.Checkbox(value=shared.settings['temperature_last'], label='temperature_last', info='Moves temperature/dynamic temperature/quadratic sampling to the end of the sampler stack, ignoring their positions in "Sampler priority".')
-                            shared.gradio['sampler_priority'] = gr.DragDrop(value=shared.settings['sampler_priority'], label='Sampler priority', info='Parameter names separated by new lines or commas.', elem_classes=['add_scrollbar'])
+                            shared.gradio['sampler_priority'] = gr.Textbox(value=shared.settings['sampler_priority'], lines=10, label='Sampler priority', info='Parameter names separated by new lines or commas.', elem_classes=['add_scrollbar'])
                             shared.gradio['dry_sequence_breakers'] = gr.Textbox(value=shared.settings['dry_sequence_breakers'], label='dry_sequence_breakers', info='Tokens across which sequence matching is not continued. Specified as a comma-separated list of quoted strings.')
 
                 with gr.Column():

modules/ui_session.py

@@ -17,7 +17,7 @@ def create_ui():
 
             with gr.Column():
                 gr.Markdown("## Extensions & flags")
-                shared.gradio['save_settings'] = gr.Button(f'Save extensions settings to {shared.user_data_dir}/settings.yaml', interactive=not mu)
+                shared.gradio['save_settings'] = gr.Button(f'Save extensions settings to {shared.user_data_dir}/settings.yaml', elem_classes='refresh-button', interactive=not mu)
                 shared.gradio['reset_interface'] = gr.Button("Apply flags/extensions and restart", interactive=not mu)
                 with gr.Row():
                     with gr.Column():
@@ -30,7 +30,7 @@ def create_ui():
         if not mu:
             shared.gradio['save_settings'].click(
                 ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-                handle_save_settings, gradio('interface_state', 'preset_menu', 'extensions_menu', 'show_controls', 'theme_state'), gradio('save_contents', 'save_filename', 'save_root', 'save_root_state', 'file_saver'), show_progress=False)
+                handle_save_settings, gradio('interface_state', 'preset_menu', 'extensions_menu', 'show_controls', 'theme_state'), gradio('save_contents', 'save_filename', 'save_root', 'file_saver'), show_progress=False)
 
         shared.gradio['toggle_dark_mode'].click(
             lambda x: 'dark' if x == 'light' else 'light', gradio('theme_state'), gradio('theme_state')).then(
@@ -51,12 +51,10 @@ def create_ui():
 
 def handle_save_settings(state, preset, extensions, show_controls, theme):
     contents = ui.save_settings(state, preset, extensions, show_controls, theme, manual_save=True)
-    root = str(shared.user_data_dir) + "/"
     return [
         contents,
         "settings.yaml",
-        root,
-        root,
+        str(shared.user_data_dir) + "/",
         gr.update(visible=True)
     ]
 
@@ -95,6 +93,8 @@ def set_interface_arguments(extensions, bool_active):
         setattr(shared.args, k, False)
     for k in bool_active:
         setattr(shared.args, k, True)
+        if k == 'api':
+            shared.add_extension('openai', last=True)
 
     shared.need_restart = True
 

modules/utils.py

@@ -47,10 +47,6 @@ def save_file(fname, contents):
         logger.error(f'Invalid file path: \"{fname}\"')
         return
 
-    if Path(abs_path_str).suffix.lower() not in ('.yaml', '.yml', '.json', '.txt', '.gbnf'):
-        logger.error(f'Refusing to save file with disallowed extension: \"{fname}\"')
-        return
-
     with open(abs_path_str, 'w', encoding='utf-8') as f:
         f.write(contents)
 
@@ -81,6 +77,14 @@ def atoi(text):
     return int(text) if text.isdigit() else text.lower()
 
 
+# Replace multiple string pairs in a string
+def replace_all(text, dic):
+    for i, j in dic.items():
+        text = text.replace(i, j)
+
+    return text
+
+
 def natural_keys(text):
     return [atoi(c) for c in re.split(r'(\d+)', text)]
 
@@ -105,9 +109,6 @@ def resolve_model_path(model_name_or_path, image_model=False):
     before the default models directory.
     """
 
-    if model_name_or_path is None:
-        raise FileNotFoundError("No model specified.")
-
     path_candidate = Path(model_name_or_path)
     if path_candidate.exists():
         return path_candidate

modules/web_search.py

@@ -1,12 +1,11 @@
 import concurrent.futures
 import html
-import ipaddress
 import random
 import re
-import socket
+import urllib.request
 from concurrent.futures import as_completed
 from datetime import datetime
-from urllib.parse import parse_qs, quote_plus, urljoin, urlparse
+from urllib.parse import quote_plus
 
 import requests
 
@@ -14,60 +13,34 @@ from modules import shared
 from modules.logging_colors import logger
 
 
-def _validate_url(url):
-    """Validate that a URL is safe to fetch (not targeting private/internal networks)."""
-    parsed = urlparse(url)
-    if parsed.scheme not in ('http', 'https'):
-        raise ValueError(f"Unsupported URL scheme: {parsed.scheme}")
-
-    hostname = parsed.hostname
-    if not hostname:
-        raise ValueError("No hostname in URL")
-
-    # Resolve hostname and check all returned addresses
-    try:
-        for family, _, _, _, sockaddr in socket.getaddrinfo(hostname, None):
-            ip = ipaddress.ip_address(sockaddr[0])
-            if not ip.is_global:
-                raise ValueError(f"Access to non-public address {ip} is blocked")
-    except socket.gaierror:
-        raise ValueError(f"Could not resolve hostname: {hostname}")
-
-
 def get_current_timestamp():
     """Returns the current time in 24-hour format"""
     return datetime.now().strftime('%b %d, %Y %H:%M')
 
 
-def download_web_page(url, timeout=10, include_links=False):
+def download_web_page(url, timeout=10):
     """
-    Download a web page and extract its main content as Markdown text.
+    Download a web page and convert its HTML content to structured Markdown text.
     """
-    import trafilatura
+    import html2text
 
     try:
-        _validate_url(url)
         headers = {
-            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36'
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
         }
-        max_redirects = 5
-        for _ in range(max_redirects):
-            response = requests.get(url, headers=headers, timeout=timeout, allow_redirects=False)
-            if response.is_redirect and 'Location' in response.headers:
-                url = urljoin(url, response.headers['Location'])
-                _validate_url(url)
-            else:
-                break
+        response = requests.get(url, headers=headers, timeout=timeout)
+        response.raise_for_status()  # Raise an exception for bad status codes
 
-        response.raise_for_status()
+        # Initialize the HTML to Markdown converter
+        h = html2text.HTML2Text()
+        h.body_width = 0
+        h.ignore_images = True
+        h.ignore_links = True
 
-        result = trafilatura.extract(
-            response.text,
-            include_links=include_links,
-            output_format='markdown',
-            url=url
-        )
-        return result or ""
+        # Convert the HTML to Markdown
+        markdown_text = h.handle(response.text)
+
+        return markdown_text
     except requests.exceptions.RequestException as e:
         logger.error(f"Error downloading {url}: {e}")
         return ""
@@ -76,51 +49,35 @@ def download_web_page(url, timeout=10, include_links=False):
         return ""
 
 
-def perform_web_search(query, num_pages=3, max_workers=5, timeout=10, fetch_content=True):
-    """Perform web search and return results, optionally with page content"""
+def perform_web_search(query, num_pages=3, max_workers=5, timeout=10):
+    """Perform web search and return results with content"""
     try:
         search_url = f"https://html.duckduckgo.com/html/?q={quote_plus(query)}"
 
         agents = [
-            "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36",
-            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36"
+            "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
+            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36"
         ]
 
-        response = requests.get(search_url, headers={'User-Agent': random.choice(agents)}, timeout=timeout)
-        response.raise_for_status()
-        response_text = response.text
+        response_text = ""
+        req = urllib.request.Request(search_url, headers={'User-Agent': random.choice(agents)})
+        with urllib.request.urlopen(req, timeout=timeout) as response:
+            response_text = response.read().decode('utf-8')
 
-        # Extract results - title and URL come from the same <a class="result__a"> element
-        result_links = re.findall(r'<a[^>]*class="[^"]*result__a[^"]*"[^>]*>(.*?)</a>', response_text, re.DOTALL)
-        result_tags = re.findall(r'<a([^>]*class="[^"]*result__a[^"]*"[^>]*)>', response_text, re.DOTALL)
+        # Extract results with regex
+        titles = re.findall(r'<a[^>]*class="[^"]*result__a[^"]*"[^>]*>(.*?)</a>', response_text, re.DOTALL)
+        urls = re.findall(r'<a[^>]*class="[^"]*result__url[^"]*"[^>]*>(.*?)</a>', response_text, re.DOTALL)
 
         # Prepare download tasks
         download_tasks = []
-        for i, (tag_attrs, raw_title) in enumerate(zip(result_tags, result_links)):
-            if num_pages is not None and i >= num_pages:
-                break
-            # Extract href and resolve the actual URL from DuckDuckGo's redirect link
-            href_match = re.search(r'href="([^"]*)"', tag_attrs)
-            if not href_match:
-                continue
-            uddg = parse_qs(urlparse(html.unescape(href_match.group(1))).query).get('uddg', [''])[0]
-            if not uddg:
-                continue
-            title = html.unescape(re.sub(r'<[^>]+>', '', raw_title).strip())
-            download_tasks.append((uddg, title, len(download_tasks)))
+        for i in range(min(len(titles), len(urls), num_pages)):
+            url = f"https://{urls[i].strip()}"
+            title = re.sub(r'<[^>]+>', '', titles[i]).strip()
+            title = html.unescape(title)
+            download_tasks.append((url, title, i))
 
         search_results = [None] * len(download_tasks)  # Pre-allocate to maintain order
 
-        if not fetch_content:
-            for url, title, index in download_tasks:
-                search_results[index] = {
-                    'title': title,
-                    'url': url,
-                    'content': ''
-                }
-
-            return search_results
-
         # Download pages in parallel
         with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
             # Submit all download tasks

one_click.py

@@ -91,7 +91,7 @@ def get_gpu_choice():
                 "What is your GPU?",
                 {
                     'A': 'NVIDIA',
-                    'B': 'AMD - Linux only, ROCm 7.2',
+                    'B': 'AMD - Linux/macOS only, requires ROCm 6.4',
                     'C': 'Apple M Series',
                     'D': 'Intel Arc (beta)',
                     'N': 'CPU mode'
@@ -111,17 +111,18 @@ def get_gpu_choice():
 def get_pytorch_install_command(gpu_choice):
     """Get PyTorch installation command based on GPU choice"""
     base_cmd = f"python -m pip install torch=={TORCH_VERSION} "
-    pypi_fallback = " --extra-index-url https://pypi.org/simple/"
 
     if gpu_choice == "NVIDIA_CUDA128":
-        return base_cmd + "--index-url https://download.pytorch.org/whl/cu128" + pypi_fallback
+        return base_cmd + "--index-url https://download.pytorch.org/whl/cu128"
     elif gpu_choice == "AMD":
-        py_tag = f"cp{PYTHON_VERSION.replace('.', '')}"
-        return f"python -m pip install https://repo.radeon.com/rocm/manylinux/rocm-rel-7.2/torch-{TORCH_VERSION}%2Brocm7.2.0.lw.git7e1940d4-{py_tag}-{py_tag}-linux_x86_64.whl --find-links https://repo.radeon.com/rocm/manylinux/rocm-rel-7.2/"
+        return base_cmd + "--index-url https://download.pytorch.org/whl/rocm6.4"
     elif gpu_choice in ["APPLE", "NONE"]:
-        return base_cmd + "--index-url https://download.pytorch.org/whl/cpu" + pypi_fallback
+        return base_cmd + "--index-url https://download.pytorch.org/whl/cpu"
     elif gpu_choice == "INTEL":
-        return base_cmd + "--index-url https://download.pytorch.org/whl/xpu"
+        if is_linux():
+            return "python -m pip install torch==2.1.0a0 intel-extension-for-pytorch==2.1.10+xpu --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/"
+        else:
+            return "python -m pip install torch==2.1.0a0 intel-extension-for-pytorch==2.1.10 --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/"
     else:
         return base_cmd
 
@@ -129,17 +130,16 @@ def get_pytorch_install_command(gpu_choice):
 def get_pytorch_update_command(gpu_choice):
     """Get PyTorch update command based on GPU choice"""
     base_cmd = f"python -m pip install --upgrade torch=={TORCH_VERSION} "
-    pypi_fallback = " --extra-index-url https://pypi.org/simple/"
 
     if gpu_choice == "NVIDIA_CUDA128":
-        return f"{base_cmd}--index-url https://download.pytorch.org/whl/cu128" + pypi_fallback
+        return f"{base_cmd} --index-url https://download.pytorch.org/whl/cu128"
     elif gpu_choice == "AMD":
-        py_tag = f"cp{PYTHON_VERSION.replace('.', '')}"
-        return f"python -m pip install --upgrade https://repo.radeon.com/rocm/manylinux/rocm-rel-7.2/torch-{TORCH_VERSION}%2Brocm7.2.0.lw.git7e1940d4-{py_tag}-{py_tag}-linux_x86_64.whl --find-links https://repo.radeon.com/rocm/manylinux/rocm-rel-7.2/"
+        return f"{base_cmd} --index-url https://download.pytorch.org/whl/rocm6.4"
     elif gpu_choice in ["APPLE", "NONE"]:
-        return f"{base_cmd}--index-url https://download.pytorch.org/whl/cpu" + pypi_fallback
+        return f"{base_cmd} --index-url https://download.pytorch.org/whl/cpu"
     elif gpu_choice == "INTEL":
-        return f"{base_cmd}--index-url https://download.pytorch.org/whl/xpu"
+        intel_extension = "intel-extension-for-pytorch==2.1.10+xpu" if is_linux() else "intel-extension-for-pytorch==2.1.10"
+        return f"{base_cmd} {intel_extension} --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/"
     else:
         return base_cmd
 
@@ -194,8 +194,6 @@ def run_cmd(cmd, assert_success=False, environment=False, capture_output=False,
     if environment:
         if is_windows():
             conda_bat_path = os.path.join(script_dir, "installer_files", "conda", "condabin", "conda.bat")
-            python_path = os.path.join(conda_env_path, "python.exe")
-            cmd = cmd.replace("python ", f'"{python_path}" ')
             cmd = f'"{conda_bat_path}" activate "{conda_env_path}" >nul && {cmd}'
         else:
             conda_sh_path = os.path.join(script_dir, "installer_files", "conda", "etc", "profile.d", "conda.sh")
@@ -270,7 +268,7 @@ def update_pytorch_and_python():
 
 
 def clean_outdated_pytorch_cuda_dependencies():
-    patterns = ["cu121", "cu122", "rocm6", "torch2.4", "torch2.6", "torch2.7", "torchvision", "torchaudio"]
+    patterns = ["cu121", "cu122", "torch2.4", "torch2.6", "torch2.7", "torchvision", "torchaudio"]
     result = run_cmd("python -m pip list --format=freeze", capture_output=True, environment=True)
     matching_packages = []
 
@@ -316,6 +314,13 @@ def install_webui():
     install_pytorch = get_pytorch_install_command(gpu_choice)
     run_cmd(f"conda install -y ninja git && {install_pytorch}", assert_success=True, environment=True)
 
+    if gpu_choice == "INTEL":
+        # Install oneAPI dependencies via conda
+        print_big_message("Installing Intel oneAPI runtime libraries.")
+        run_cmd("conda install -y -c https://software.repos.intel.com/python/conda/ -c conda-forge dpcpp-cpp-rt=2024.0 mkl-dpcpp=2024.0", environment=True)
+        # Install libuv required by Intel-patched torch
+        run_cmd("conda install -y libuv", environment=True)
+
     # Install the webui requirements
     update_requirements(initial_installation=True, pull=False)
 
@@ -358,10 +363,8 @@ def update_requirements(initial_installation=False, pull=True):
 
     current_commit = get_current_commit()
     wheels_changed = not os.path.exists(state_file)
-    installed_wheels = set()
     if not wheels_changed:
         state = load_state()
-        installed_wheels = set(state.get('installed_wheels', []))
         if 'wheels_changed' in state or state.get('last_installed_commit') != current_commit:
             wheels_changed = True
 
@@ -426,17 +429,9 @@ def update_requirements(initial_installation=False, pull=True):
 
     # Prepare the requirements file
     textgen_requirements = open(requirements_file).read().splitlines()
-    all_whl_lines = [line.strip() for line in textgen_requirements if '.whl' in line]
 
-    if not initial_installation:
-        if installed_wheels:
-            # Per-wheel comparison: only re-download wheels that changed
-            textgen_requirements = [
-                line for line in textgen_requirements
-                if '.whl' not in line or line.strip() not in installed_wheels
-            ]
-        elif not wheels_changed:
-            textgen_requirements = [line for line in textgen_requirements if '.whl' not in line]
+    if not initial_installation and not wheels_changed:
+        textgen_requirements = [line for line in textgen_requirements if '.whl' not in line]
 
     with open('temp_requirements.txt', 'w') as file:
         file.write('\n'.join(textgen_requirements))
@@ -455,7 +450,6 @@ def update_requirements(initial_installation=False, pull=True):
     # Save state after successful installation
     state = load_state()
     state['last_installed_commit'] = current_commit
-    state['installed_wheels'] = all_whl_lines
     state.pop('wheels_changed', None)
     save_state(state)
 

requirements/full/requirements.txt

@@ -1,21 +1,21 @@
-accelerate==1.13.*
+accelerate==1.12.*
 audioop-lts<1.0; python_version >= "3.13"
 bitsandbytes==0.49.*
 datasets
-diffusers==0.37.*
+diffusers==0.36.*
 einops
 fastapi==0.112.4
 flash-linear-attention==0.4.*
+html2text==2025.4.15
 huggingface-hub==1.5.*
 jinja2==3.1.6
 markdown
-mcp==1.27.0
 numpy==2.2.*
 pandas
 peft==0.18.*
 Pillow>=9.5.0
 pydantic==2.11.0
-pymupdf==1.27.*
+pymupdf==1.27.1
 python-docx==1.1.2
 pyyaml
 requests
@@ -25,15 +25,14 @@ scipy
 sentencepiece
 tensorboard
 torchao==0.15.*
-trafilatura==2.0.0
-transformers==5.5.*
+transformers==5.3.*
 triton-windows==3.5.1.post24; platform_system == "Windows"
 tqdm
 wandb
 
 # Gradio
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio-4.37.2+custom.19-py3-none-any.whl
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio_client-1.0.2+custom.19-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio-4.37.2+custom.9-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio_client-1.0.2+custom.9-py3-none-any.whl
 
 # API
 flask_cloudflared==0.0.15
@@ -41,11 +40,9 @@ sse-starlette==1.6.5
 tiktoken
 
 # CUDA wheels
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/llama_cpp_binaries-0.110.0+cu124-py3-none-win_amd64.whl; platform_system == "Windows"
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/llama_cpp_binaries-0.110.0+cu124-py3-none-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/ik_llama_cpp_binaries-0.110.0+cu124-py3-none-win_amd64.whl; platform_system == "Windows"
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/ik_llama_cpp_binaries-0.110.0+cu124-py3-none-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
-https://github.com/turboderp-org/exllamav3/releases/download/v0.0.28/exllamav3-0.0.28+cu128.torch2.9.0-cp313-cp313-win_amd64.whl; platform_system == "Windows" and python_version == "3.13"
-https://github.com/turboderp-org/exllamav3/releases/download/v0.0.28/exllamav3-0.0.28+cu128.torch2.9.0-cp313-cp313-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.13"
+https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.87.0/llama_cpp_binaries-0.87.0+cu124-py3-none-win_amd64.whl; platform_system == "Windows"
+https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.87.0/llama_cpp_binaries-0.87.0+cu124-py3-none-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
+https://github.com/turboderp-org/exllamav3/releases/download/v0.0.23/exllamav3-0.0.23+cu128.torch2.9.0-cp313-cp313-win_amd64.whl; platform_system == "Windows" and python_version == "3.13"
+https://github.com/turboderp-org/exllamav3/releases/download/v0.0.23/exllamav3-0.0.23+cu128.torch2.9.0-cp313-cp313-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.13"
 https://github.com/kingbri1/flash-attention/releases/download/v2.8.3/flash_attn-2.8.3+cu128torch2.9.0cxx11abiFALSE-cp313-cp313-win_amd64.whl; platform_system == "Windows" and python_version == "3.13"
 https://github.com/kingbri1/flash-attention/releases/download/v2.8.3/flash_attn-2.8.3+cu128torch2.9.0cxx11abiFALSE-cp313-cp313-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.13"

requirements/full/requirements_amd.txt

@@ -1,19 +1,19 @@
-accelerate==1.13.*
+accelerate==1.12.*
 audioop-lts<1.0; python_version >= "3.13"
 datasets
-diffusers==0.37.*
+diffusers==0.36.*
 einops
 fastapi==0.112.4
+html2text==2025.4.15
 huggingface-hub==1.5.*
 jinja2==3.1.6
 markdown
-mcp==1.27.0
 numpy==2.2.*
 pandas
 peft==0.18.*
 Pillow>=9.5.0
 pydantic==2.11.0
-pymupdf==1.27.*
+pymupdf==1.27.1
 python-docx==1.1.2
 pyyaml
 requests
@@ -23,14 +23,13 @@ scipy
 sentencepiece
 tensorboard
 torchao==0.15.*
-transformers==5.5.*
+transformers==5.3.*
 tqdm
-trafilatura==2.0.0
 wandb
 
 # Gradio
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio-4.37.2+custom.19-py3-none-any.whl
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio_client-1.0.2+custom.19-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio-4.37.2+custom.9-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio_client-1.0.2+custom.9-py3-none-any.whl
 
 # API
 flask_cloudflared==0.0.15
@@ -38,5 +37,5 @@ sse-starlette==1.6.5
 tiktoken
 
 # AMD wheels
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/llama_cpp_binaries-0.110.0+rocm7.2-py3-none-win_amd64.whl; platform_system == "Windows"
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/llama_cpp_binaries-0.110.0+rocm7.2-py3-none-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
+https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.87.0/llama_cpp_binaries-0.87.0+rocm6.4-py3-none-win_amd64.whl; platform_system == "Windows"
+https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.87.0/llama_cpp_binaries-0.87.0+rocm6.4-py3-none-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"

requirements/full/requirements_apple_intel.txt

@@ -1,19 +1,19 @@
-accelerate==1.13.*
+accelerate==1.12.*
 audioop-lts<1.0; python_version >= "3.13"
 datasets
-diffusers==0.37.*
+diffusers==0.36.*
 einops
 fastapi==0.112.4
+html2text==2025.4.15
 huggingface-hub==1.5.*
 jinja2==3.1.6
 markdown
-mcp==1.27.0
 numpy==2.2.*
 pandas
 peft==0.18.*
 Pillow>=9.5.0
 pydantic==2.11.0
-pymupdf==1.27.*
+pymupdf==1.27.1
 python-docx==1.1.2
 pyyaml
 requests
@@ -23,14 +23,13 @@ scipy
 sentencepiece
 tensorboard
 torchao==0.15.*
-transformers==5.5.*
+transformers==5.3.*
 tqdm
-trafilatura==2.0.0
 wandb
 
 # Gradio
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio-4.37.2+custom.19-py3-none-any.whl
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio_client-1.0.2+custom.19-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio-4.37.2+custom.9-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio_client-1.0.2+custom.9-py3-none-any.whl
 
 # API
 flask_cloudflared==0.0.15
@@ -38,4 +37,4 @@ sse-starlette==1.6.5
 tiktoken
 
 # Mac wheels
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/llama_cpp_binaries-0.110.0-py3-none-macosx_13_0_x86_64.whl; platform_system == "Darwin"
+https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.87.0/llama_cpp_binaries-0.87.0-py3-none-macosx_13_0_x86_64.whl; platform_system == "Darwin"

requirements/full/requirements_apple_silicon.txt

@@ -1,19 +1,19 @@
-accelerate==1.13.*
+accelerate==1.12.*
 audioop-lts<1.0; python_version >= "3.13"
 datasets
-diffusers==0.37.*
+diffusers==0.36.*
 einops
 fastapi==0.112.4
+html2text==2025.4.15
 huggingface-hub==1.5.*
 jinja2==3.1.6
 markdown
-mcp==1.27.0
 numpy==2.2.*
 pandas
 peft==0.18.*
 Pillow>=9.5.0
 pydantic==2.11.0
-pymupdf==1.27.*
+pymupdf==1.27.1
 python-docx==1.1.2
 pyyaml
 requests
@@ -23,14 +23,13 @@ scipy
 sentencepiece
 tensorboard
 torchao==0.15.*
-transformers==5.5.*
+transformers==5.3.*
 tqdm
-trafilatura==2.0.0
 wandb
 
 # Gradio
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio-4.37.2+custom.19-py3-none-any.whl
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio_client-1.0.2+custom.19-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio-4.37.2+custom.9-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio_client-1.0.2+custom.9-py3-none-any.whl
 
 # API
 flask_cloudflared==0.0.15
@@ -38,4 +37,4 @@ sse-starlette==1.6.5
 tiktoken
 
 # Mac wheels
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/llama_cpp_binaries-0.110.0-py3-none-macosx_13_0_arm64.whl; platform_system == "Darwin"
+https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.87.0/llama_cpp_binaries-0.87.0-py3-none-macosx_13_0_arm64.whl; platform_system == "Darwin"

requirements/full/requirements_cpu_only.txt

@@ -1,19 +1,19 @@
-accelerate==1.13.*
+accelerate==1.12.*
 audioop-lts<1.0; python_version >= "3.13"
 datasets
-diffusers==0.37.*
+diffusers==0.36.*
 einops
 fastapi==0.112.4
+html2text==2025.4.15
 huggingface-hub==1.5.*
 jinja2==3.1.6
 markdown
-mcp==1.27.0
 numpy==2.2.*
 pandas
 peft==0.18.*
 Pillow>=9.5.0
 pydantic==2.11.0
-pymupdf==1.27.*
+pymupdf==1.27.1
 python-docx==1.1.2
 pyyaml
 requests
@@ -23,14 +23,13 @@ scipy
 sentencepiece
 tensorboard
 torchao==0.15.*
-transformers==5.5.*
+transformers==5.3.*
 tqdm
-trafilatura==2.0.0
 wandb
 
 # Gradio
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio-4.37.2+custom.19-py3-none-any.whl
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio_client-1.0.2+custom.19-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio-4.37.2+custom.9-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio_client-1.0.2+custom.9-py3-none-any.whl
 
 # API
 flask_cloudflared==0.0.15
@@ -38,7 +37,5 @@ sse-starlette==1.6.5
 tiktoken
 
 # llama.cpp (CPU only)
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/llama_cpp_binaries-0.110.0+cpu-py3-none-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/llama_cpp_binaries-0.110.0+cpu-py3-none-win_amd64.whl; platform_system == "Windows"
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/ik_llama_cpp_binaries-0.110.0+cpu-py3-none-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/ik_llama_cpp_binaries-0.110.0+cpu-py3-none-win_amd64.whl; platform_system == "Windows"
+https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.87.0/llama_cpp_binaries-0.87.0+cpu-py3-none-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
+https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.87.0/llama_cpp_binaries-0.87.0+cpu-py3-none-win_amd64.whl; platform_system == "Windows"

requirements/full/requirements_nowheels.txt

@@ -1,19 +1,19 @@
-accelerate==1.13.*
+accelerate==1.12.*
 audioop-lts<1.0; python_version >= "3.13"
 datasets
-diffusers==0.37.*
+diffusers==0.36.*
 einops
 fastapi==0.112.4
+html2text==2025.4.15
 huggingface-hub==1.5.*
 jinja2==3.1.6
 markdown
-mcp==1.27.0
 numpy==2.2.*
 pandas
 peft==0.18.*
 Pillow>=9.5.0
 pydantic==2.11.0
-pymupdf==1.27.*
+pymupdf==1.27.1
 python-docx==1.1.2
 pyyaml
 requests
@@ -23,14 +23,13 @@ scipy
 sentencepiece
 tensorboard
 torchao==0.15.*
-transformers==5.5.*
+transformers==5.3.*
 tqdm
-trafilatura==2.0.0
 wandb
 
 # Gradio
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio-4.37.2+custom.19-py3-none-any.whl
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio_client-1.0.2+custom.19-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio-4.37.2+custom.9-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio_client-1.0.2+custom.9-py3-none-any.whl
 
 # API
 flask_cloudflared==0.0.15

requirements/portable/requirements.txt

@@ -1,22 +1,21 @@
 audioop-lts<1.0; python_version >= "3.13"
 fastapi==0.112.4
+html2text==2025.4.15
 huggingface-hub==1.5.*
 jinja2==3.1.6
 markdown
-mcp==1.27.0
 numpy==2.2.*
 pydantic==2.11.0
-pymupdf==1.27.*
+pymupdf==1.27.1
 python-docx==1.1.2
 pyyaml
 requests
 rich
-trafilatura==2.0.0
 tqdm
 
 # Gradio
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio-4.37.2+custom.19-py3-none-any.whl
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio_client-1.0.2+custom.19-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio-4.37.2+custom.9-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio_client-1.0.2+custom.9-py3-none-any.whl
 
 # API
 flask_cloudflared==0.0.15
@@ -24,5 +23,5 @@ sse-starlette==1.6.5
 tiktoken
 
 # CUDA wheels
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/llama_cpp_binaries-0.110.0+cu124-py3-none-win_amd64.whl; platform_system == "Windows"
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/llama_cpp_binaries-0.110.0+cu124-py3-none-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
+https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.87.0/llama_cpp_binaries-0.87.0+cu124-py3-none-win_amd64.whl; platform_system == "Windows"
+https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.87.0/llama_cpp_binaries-0.87.0+cu124-py3-none-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"

requirements/portable/requirements_amd.txt

@@ -1,22 +1,21 @@
 audioop-lts<1.0; python_version >= "3.13"
 fastapi==0.112.4
+html2text==2025.4.15
 huggingface-hub==1.5.*
 jinja2==3.1.6
 markdown
-mcp==1.27.0
 numpy==2.2.*
 pydantic==2.11.0
-pymupdf==1.27.*
+pymupdf==1.27.1
 python-docx==1.1.2
 pyyaml
 requests
 rich
-trafilatura==2.0.0
 tqdm
 
 # Gradio
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio-4.37.2+custom.19-py3-none-any.whl
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio_client-1.0.2+custom.19-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio-4.37.2+custom.9-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio_client-1.0.2+custom.9-py3-none-any.whl
 
 # API
 flask_cloudflared==0.0.15
@@ -24,5 +23,5 @@ sse-starlette==1.6.5
 tiktoken
 
 # AMD wheels
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/llama_cpp_binaries-0.110.0+rocm7.2-py3-none-win_amd64.whl; platform_system == "Windows"
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/llama_cpp_binaries-0.110.0+rocm7.2-py3-none-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
+https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.87.0/llama_cpp_binaries-0.87.0+rocm6.4-py3-none-win_amd64.whl; platform_system == "Windows"
+https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.87.0/llama_cpp_binaries-0.87.0+rocm6.4-py3-none-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"

requirements/portable/requirements_apple_intel.txt

@@ -1,22 +1,21 @@
 audioop-lts<1.0; python_version >= "3.13"
 fastapi==0.112.4
+html2text==2025.4.15
 huggingface-hub==1.5.*
 jinja2==3.1.6
 markdown
-mcp==1.27.0
 numpy==2.2.*
 pydantic==2.11.0
-pymupdf==1.27.*
+pymupdf==1.27.1
 python-docx==1.1.2
 pyyaml
 requests
 rich
-trafilatura==2.0.0
 tqdm
 
 # Gradio
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio-4.37.2+custom.19-py3-none-any.whl
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio_client-1.0.2+custom.19-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio-4.37.2+custom.9-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio_client-1.0.2+custom.9-py3-none-any.whl
 
 # API
 flask_cloudflared==0.0.15
@@ -24,4 +23,4 @@ sse-starlette==1.6.5
 tiktoken
 
 # Mac wheels
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/llama_cpp_binaries-0.110.0-py3-none-macosx_13_0_x86_64.whl; platform_system == "Darwin"
+https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.87.0/llama_cpp_binaries-0.87.0-py3-none-macosx_13_0_x86_64.whl; platform_system == "Darwin"

requirements/portable/requirements_apple_silicon.txt

@@ -1,22 +1,21 @@
 audioop-lts<1.0; python_version >= "3.13"
 fastapi==0.112.4
+html2text==2025.4.15
 huggingface-hub==1.5.*
 jinja2==3.1.6
 markdown
-mcp==1.27.0
 numpy==2.2.*
 pydantic==2.11.0
-pymupdf==1.27.*
+pymupdf==1.27.1
 python-docx==1.1.2
 pyyaml
 requests
 rich
-trafilatura==2.0.0
 tqdm
 
 # Gradio
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio-4.37.2+custom.19-py3-none-any.whl
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio_client-1.0.2+custom.19-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio-4.37.2+custom.9-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio_client-1.0.2+custom.9-py3-none-any.whl
 
 # API
 flask_cloudflared==0.0.15
@@ -24,4 +23,4 @@ sse-starlette==1.6.5
 tiktoken
 
 # Mac wheels
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/llama_cpp_binaries-0.110.0-py3-none-macosx_13_0_arm64.whl; platform_system == "Darwin"
+https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.87.0/llama_cpp_binaries-0.87.0-py3-none-macosx_13_0_arm64.whl; platform_system == "Darwin"

requirements/portable/requirements_cpu_only.txt

@@ -1,22 +1,21 @@
 audioop-lts<1.0; python_version >= "3.13"
 fastapi==0.112.4
+html2text==2025.4.15
 huggingface-hub==1.5.*
 jinja2==3.1.6
 markdown
-mcp==1.27.0
 numpy==2.2.*
 pydantic==2.11.0
-pymupdf==1.27.*
+pymupdf==1.27.1
 python-docx==1.1.2
 pyyaml
 requests
 rich
-trafilatura==2.0.0
 tqdm
 
 # Gradio
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio-4.37.2+custom.19-py3-none-any.whl
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio_client-1.0.2+custom.19-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio-4.37.2+custom.9-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio_client-1.0.2+custom.9-py3-none-any.whl
 
 # API
 flask_cloudflared==0.0.15
@@ -24,5 +23,5 @@ sse-starlette==1.6.5
 tiktoken
 
 # llama.cpp (CPU only)
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/llama_cpp_binaries-0.110.0+cpu-py3-none-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/llama_cpp_binaries-0.110.0+cpu-py3-none-win_amd64.whl; platform_system == "Windows"
+https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.87.0/llama_cpp_binaries-0.87.0+cpu-py3-none-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
+https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.87.0/llama_cpp_binaries-0.87.0+cpu-py3-none-win_amd64.whl; platform_system == "Windows"

requirements/portable/requirements_cuda131.txt

@@ -1,22 +1,21 @@
 audioop-lts<1.0; python_version >= "3.13"
 fastapi==0.112.4
+html2text==2025.4.15
 huggingface-hub==1.5.*
 jinja2==3.1.6
 markdown
-mcp==1.27.0
 numpy==2.2.*
 pydantic==2.11.0
-pymupdf==1.27.*
+pymupdf==1.27.1
 python-docx==1.1.2
 pyyaml
 requests
 rich
-trafilatura==2.0.0
 tqdm
 
 # Gradio
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio-4.37.2+custom.19-py3-none-any.whl
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio_client-1.0.2+custom.19-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio-4.37.2+custom.9-py3-none-any.whl
+https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.9/gradio_client-1.0.2+custom.9-py3-none-any.whl
 
 # API
 flask_cloudflared==0.0.15
@@ -24,5 +23,5 @@ sse-starlette==1.6.5
 tiktoken
 
 # CUDA wheels
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/llama_cpp_binaries-0.110.0+cu131-py3-none-win_amd64.whl; platform_system == "Windows"
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/llama_cpp_binaries-0.110.0+cu131-py3-none-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
+https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.87.0/llama_cpp_binaries-0.87.0+cu131-py3-none-win_amd64.whl; platform_system == "Windows"
+https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.87.0/llama_cpp_binaries-0.87.0+cu131-py3-none-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"

requirements/portable/requirements_ik.txt

@@ -1,28 +0,0 @@
-audioop-lts<1.0; python_version >= "3.13"
-fastapi==0.112.4
-huggingface-hub==1.5.*
-jinja2==3.1.6
-markdown
-mcp==1.27.0
-numpy==2.2.*
-pydantic==2.11.0
-pymupdf==1.27.*
-python-docx==1.1.2
-pyyaml
-requests
-rich
-trafilatura==2.0.0
-tqdm
-
-# Gradio
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio-4.37.2+custom.19-py3-none-any.whl
-https://github.com/oobabooga/gradio/releases/download/4.37.2-custom.19/gradio_client-1.0.2+custom.19-py3-none-any.whl
-
-# API
-flask_cloudflared==0.0.15
-sse-starlette==1.6.5
-tiktoken
-
-# CUDA wheels
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/ik_llama_cpp_binaries-0.110.0+cu124-py3-none-win_amd64.whl; platform_system == "Windows"
-https://github.com/oobabooga/llama-cpp-binaries/releases/download/v0.110.0/ik_llama_cpp_binaries-0.110.0+cu124-py3-none-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"