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Glm4 Invalid Conversation Format Tokenizer.apply_Chat_Template

Glm4 Invalid Conversation Format Tokenizer.apply_Chat_Template - Union[list[dict[str, str]], list[list[dict[str, str]]], conversation], #. Hi @philipamadasun, the most likely cause is that you're loading the base gemma. You can use that model and tokenizer in conversationpipeline, or you can call tokenizer.apply_chat_template() to format chats for inference or. My data contains two key. As of transformers v4.44, default. If you have any chat models, you should set their tokenizer.chat_template attribute and test it. For information about writing templates and setting the. Cannot use apply_chat_template () because.

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Cannot use apply_chat_template () because. Hi @philipamadasun, the most likely cause is that you're loading the base gemma. Union[list[dict[str, str]], list[list[dict[str, str]]], conversation], #. As of transformers v4.44, default. You can use that model and tokenizer in conversationpipeline, or you can call tokenizer.apply_chat_template() to format chats for inference or. If you have any chat models, you should set their tokenizer.chat_template attribute and test it. For information about writing templates and setting the. My data contains two key.

Hi @Philipamadasun, The Most Likely Cause Is That You're Loading The Base Gemma.

Cannot use apply_chat_template () because. My data contains two key. As of transformers v4.44, default. You can use that model and tokenizer in conversationpipeline, or you can call tokenizer.apply_chat_template() to format chats for inference or.

Union[List[Dict[Str, Str]], List[List[Dict[Str, Str]]], Conversation], #.

If you have any chat models, you should set their tokenizer.chat_template attribute and test it. For information about writing templates and setting the.

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