- Stop llama-server subprocess on model unload instead of relying on GC
- Fix tool_calls[].index being string instead of int in API responses
- Omit tool_calls key from API response when empty per OpenAI spec
- Prevent division by zero when micro_batch_size > batch_size in training
- Copy sampler_priority list before mutating in ExLlamaV3
- Normalize presence/frequency_penalty names for ExLlamaV3 sampler sorting
- Restore original chat_template after training instead of leaving it mutated
The /v1/internal/model/load endpoint previously allowed setting any
shared.args attribute, including security-sensitive flags like
trust_remote_code. Now only keys from list_model_elements() are accepted.
The tool call response parser only handled JSON-based formats, causing
tool_calls to always be empty for models that use non-JSON formats.
Add parsers for three additional tool call formats:
- Qwen3.5: <tool_call><function=name><parameter=key>value</parameter>
- Mistral/Devstral: functionName{"arg": "value"}
- GPT-OSS: <|channel|>commentary to=functions.name<|message|>{...}
Also fix multi-turn tool conversations crashing with Jinja2
UndefinedError on tool_call_id by preserving tool_calls and
tool_call_id metadata through the chat history conversion.
If --user-data-dir is not set, auto-detect: use ../user_data when
./user_data doesn't exist, making it easy to share user data across
portable builds by placing it one folder up.
Instead of two separate paths (format files vs Chat Template), all
instruction training now uses apply_chat_template() with assistant-only
label masking. Users pick a Jinja2 template from the dropdown or use the
model's built-in chat template — both work identically.
The built-in training tab now covers its essential functionality
with a more modern and correct implementation (apply_chat_template,
dynamic padding, JSONL datasets, stride overlap).
