Agentic LLM Inference Parameters Reference for Qwen and Gemma
The article provides a reference for tuning agentic LLM inference parameters for models Qwen and Gemma. It emphasizes the importance of specific configurations for optimal performance in coding and reasoning tasks. The guide includes recommended settings and highlights differences in behavior between dense and mixture of experts (MoE) models.
- ▪The reference focuses on tuning parameters such as temperature, top_p, top_k, and presence penalties for Qwen 3.6 and Gemma 4 models.
- ▪It outlines the differences in tuning priorities for agentic systems compared to traditional chat models, emphasizing multi-step reasoning and consistent outputs.
- ▪Gemma 4 is noted to perform better with higher temperatures, which contradicts standard advice for coding agents.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3544400) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Rost Posted on May 17 • Originally published at glukhov.org Agentic LLM Inference Parameters Reference for Qwen and Gemma #hermes #openclaw #opencode #cheatsheet This page is a practical reference for agentic LLM inference tuning (temperature, top_p, top_k, penalties, and how they interact in multi-step and tool-heavy workflows).
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