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Regarding the issue of training templates in Qwen2VLDataCollator #57
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I found the same problem as you mention. So I directly edit the code: |
Yes, I have adopted the same strategy as you, but is it correct to do so? |
I think it is ok to edit like this, I have trained model using this repository (edit this few lines of code) and inference with qwen2vl official code (using apply_chat_template) and get the correct result. |
Thank you, this is very helpful to me. |
@Asunatan @Elenore1997 Yes I think the proposed fix looks good. Sorry for not being able to respond earlier; I was moving the past few days. Tagging @linyueqian to be aware of this. |
Yes, as mentioned in #56, we should directly use the text as the content value. Just updated in the latest commit. |
Hello, I am a beginner in the field of VLM and have a question regarding the training template issue. In the Qwen2VLDataCollator you provided, I noticed there are some additional fields.
This differs from directly applying
apply_chat_template_text= self.processor.apply_chat_template(cur_text, tokenize=False, add_generation_prompt=True,)
which seems to result in some differences. Could this lead to discrepancies during prediction?Below is the result obtained directly from applying apply_chat_template:
The gpt_response obtained from apply_chat_template appears to lack the fields such as [ {"type":"text", "text":. I found that the source of the issue seems to be:
I am curious whether the differences between these two could lead to training biases.
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