A developer discovered that their self-hosted coding model, Ornith-1.0-35B, was performing poorly due to incorrect serving configurations rather than inherent model flaws. By analyzing LiteLLM spend logs, they found that requests were being sent with maximum randomness parameters (temperature and top_p at 1.0) and that the model's reasoning capabilities were disabled. Additionally, the use of fp8 precision for the KV cache with extremely large contexts was causing degradation. Adjusting the serving configuration to include temperature and enable thinking, and switching to fp16 for the KV cache, resolved these issues without additional cost. AI
IMPACT Highlights the critical role of proper LLM serving configuration for optimal performance, even with advanced models.
RANK_REASON Developer's self-hosted LLM configuration troubleshooting and fix.
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