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…anity check - Add MODEL_FAMILIES config table with per-model API params and server flags - Add getModelApiParams() helper to inject reasoning_effort:none for Mistral - Add delta.thinking fallback in streaming loop to capture thinking tokens - Add streaming sanity check before benchmark run (detects empty-token loops) - Add test-model-config.cjs with 17 unit tests for model detection logic
Feature/mistral thinking fix
Add Mistral Small 4 (119B, IQ1_M + Q2_K_XL), NVIDIA Nemotron-3-Nano (4B + 30B), Liquid LFM2 (1.2B + 24B), Qwen3.5-9B BF16, and Qwen3.5-27B Q8_K_XL to the benchmark paper. Key updates: - Abstract: 7→16 models, 5 families, best local now 95.8% - Models Under Test table: grouped by family, 16 rows - Overall Scorecard: full 16-model ranking - Key Finding 3: quantization precision > parameter count - Conclusion: Qwen3.5-27B Q8 at 95.8%, Mistral-119B at 89.6%
Add Nemotron and LFM2 model families to MODEL_FAMILIES with minTemperature: 1.0 — these models reject temperature < 1.0 with HTTP 400. The benchmark now clamps temperature to the family minimum before sending the request. - Refactor getModelApiParams → getModelFamily (returns full config) - Add resolveTemperature logic in llmCall params builder - Update test-model-config.cjs: 27 tests including temperature clamp - Fix Mistral serverFlags to match current llm-server-manager.cjs
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