A new handbook aims to guide developers in mastering chat templates, which are crucial for ensuring Large Language Models (LLMs) perform as intended. The guide covers Jinja templating, the applychattemplate function, and rendering model-ready prompts, addressing common issues like silent performance degradation due to incorrect tokenization. It also delves into advanced topics such as tool-calling, multimodal templates, debugging with golden-token tests, and the security implications of chat templates as an inference-time attack surface. AI
IMPACT Provides developers with essential tools and knowledge to improve LLM performance and security.
RANK_REASON The cluster discusses a handbook and related tools for LLM prompt engineering, not a new model release or core research.
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