oobabooga/text-generation-webui
1
0
Fork 0
mirror of https://github.com/oobabooga/text-generation-webui.git synced 2026-04-04 14:17:28 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

llama.cpp: Add speculative decoding (#6891)

Browse source
This commit is contained in:
oobabooga 2025-04-23 20:10:16 -03:00 • committed by GitHub
parent 9424ba17c8
commit e99c20bcb0
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
7 changed files with 61 additions and 2 deletions
Download patch file Download diff file Expand all files Collapse all files

4
modules/training.py
View file

@ -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 requirements and adapter size.\nNOTE: Only works for model_id='llama', other types will retain default training behavior and not use these settings.")
with gr.Row():
with gr.Column():
@ -86,7 +86,7 @@ def create_ui():
with gr.Row():
lr_scheduler_type = gr.Dropdown(label='LR Scheduler', value='linear', 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():
lora_dropout = gr.Slider(label='LoRA Dropout', minimum=0.0, maximum=1.0, step=0.025, value=0.05, info='Percentage probability for dropout of LoRA layers. This can help reduce overfitting. Most users should leave at default.')