在prompt_config.py里加内容后模型有时不能准确执行prompt about langchain-chatchat HOT 2 CLOSED

The inconsistency in model behavior after updating prompt_config.py could be attributed to several key factors:

1. Complexity and Structure of Prompts: Different models, like ChatGLM3-6B and Yi-34B-int4, may interpret the same prompts differently due to their unique training and architecture. This can lead to variations in how closely they adhere to the intended structure of the prompts.

2. Dynamic Content Handling: If the dynamic content (e.g., variables or placeholders within your prompts) isn't correctly parsed or rendered, it might cause inconsistent responses. Ensuring that this content is accurately inserted into the prompts is crucial.

3. Temperature Settings: The randomness in response generation is influenced by temperature settings. A higher temperature can result in more varied responses, while a lower temperature tends to produce more consistent but potentially less creative outputs. Finding the right balance for your specific use case might require some experimentation.

4. Prompt Directiveness: The clarity and directiveness of your prompts play a significant role. More directive prompts tend to yield more consistent results, whereas open-ended prompts can lead to a wider range of responses due to the model's interpretation.

To address these issues, consider simplifying your prompt structures, ensuring dynamic content is correctly rendered, experimenting with temperature settings to find an optimal balance, and tailoring prompts to the specific strengths and characteristics of each model. This approach can help mitigate inconsistencies and improve the predictability of model responses.

To continue the conversation, mention @dosu.

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你的模板没对吧， system 你要填入什么呢
input是会被替换成你的问题的

这个框架没做system角色

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