Follow-up to #116. MiniMax exposed only TARGET_DEPLOYMENT, and
set_optimizer_deployment() never touched MiniMax, so a configured
optimizer_model was ignored: chat_optimizer and the optimizer message path
both sent TARGET_DEPLOYMENT. In mixed optimizer/target setups this could send
the wrong model name to the optimizer endpoint.
Adds OPTIMIZER_DEPLOYMENT and set_optimizer_deployment() to minimax_backend,
wired into the model facade. chat_optimizer and a new chat_optimizer_messages
use it, falling back to TARGET_DEPLOYMENT when unset. Also fixes dict[int]
return annotations to dict[str, int]. Adds routing tests.
Co-Authored-By: Claude <noreply@anthropic.com>
Two missing pieces in the minimax_chat dispatch chain:
1. minimax_backend.py did not define chat_optimizer or chat_optimizer_messages;
only chat_target / chat_target_messages existed. Any caller using
optimizer_backend='minimax_chat' would hit AttributeError or fall through
to _openai (Azure).
2. skillopt/model/__init__.py's chat_optimizer and chat_optimizer_messages
dispatchers checked claude_chat and qwen_chat but not minimax_chat,
so minimax_chat callers would silently fall through to _openai.chat_optimizer
(the Azure path), which fails with 'Azure OpenAI endpoint is not configured'
on any setup without AZURE_OPENAI_* env vars.
Adds chat_optimizer to minimax_backend.py (mirrors chat_target via
_chat_messages_impl) and minimax_chat branches to both
chat_optimizer / chat_optimizer_messages dispatchers.
Verified locally: 1-epoch training on a 4-item SearchQA-format dataset
went from '[skip] no usable patches — skill unchanged' (baseline fallback)
to a successful accept_new_best with success_patches=1 per step.
Co-authored-by: Mavis (MiniMax) <Mavis@MiniMax.local>
Co-authored-by: jc <jc@users.noreply.github.com>
The qwen_chat backend (the generic OpenAI-compatible client) hardcoded
max_tokens and always sent temperature, so reasoning models behind
OpenAI-compatible gateways (GPT-5.x, Claude Opus 4.8 via Azure/LiteLLM)
would 400.
- Add opt-in QWEN_CHAT_USE_MAX_COMPLETION_TOKENS (+ role variants) that
swaps the payload key max_tokens -> max_completion_tokens.
- Treat an explicit empty / none / off temperature as "omit" instead of
collapsing to the 0.7 default (via _resolve_temperature).
- Thread both through configure_qwen_chat / _update_config.
- Defaults unchanged; fully backward compatible. Adds 6 tests.
Fixes#127
Co-authored-by: Chirag Singhal <chirag127@users.noreply.github.com>
Two bugs made local vLLM targets score acc=0.000: the router did not
forward 'timeout' to the Qwen backend (so runs used the 300s default),
and qwen_backend always injected chat_template_kwargs.enable_thinking,
which non-Qwen vLLM servers reject or answer with <think> output and no
<answer> tag. Forward timeout and only set the field when enabled.
Closes#28
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- Skill optimization framework with training loop analogy
- 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen)
- WebUI for browser-based training control
- Pluggable architecture for extending benchmarks and backends
Yif Yang