Chat Templates：終結默默蠶食模型效能的隱形殺手
Original: Chat Templates: An End to the Silent Performance Killer
As large language models (LLMs) shift toward conversational (Chat/Instruct) applications, correctly formatting and feeding a user's…
在開發 LLM 對話應用時，不同模型（如 LLaMA、Vicuna）要求不同的特殊標記與格式。過去手動拼接字串極易出錯，微小的空格或換行差異都會嚴重影響模型輸出品質。Hugging Face 推出「Chat Templates」功能，將格式化邏輯以 Jinja 模板儲存於 tokenizer 設定中，開發者只需調用 `apply_chat_template()` 即可自動套用正確格式，徹底解決此痛點。
As large language models (LLMs) shift toward conversational (Chat/Instruct) applications, correctly formatting and feeding a user's conversation history — alternating between User and Assistant turns — into the model has long been a thorny and error-prone problem. Different model families (such as Meta's LLaMA, Alpaca, Vicuna, ChatML, and others) each have their own unique special tokens and structural requirements. For instance, some models use `[INST]` and `[/INST]`, while others use `<|im_start|>` and `<|im_end|>`.
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