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I am currently following the fine-tuning methods for the Hugging Face model Zephyr 7B. They have implemented two fine-tuning methods, namely SFT and DPO, on a public dataset. Currently, I am fine-tuning a 7B model using SFT, which is progressing well. However, I have a question regarding whether it is acceptable to fine-tune the model on a DPO dataset generated synthetically by GPT-3.5.

From my understanding, DPO should be trained on the answers produced by the same model. I want to confirm this and inquire if anyone has attempted such fine-tuning before.

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Using GPT-3.5-generated data for Domain-Adaptive Pretraining (DAPT) on a 7B model is an interesting idea, but it's typically best to use data generated by the same model you're fine-tuning. This ensures consistency and relevance. While there's no rule against using synthetic data from GPT-3.5, the success of such an approach would be experimental. If you decide to try it, make sure to thoroughly evaluate the quality of the synthetic data and compare the fine-tuning results with a baseline. It's also worth checking with the community to see if anyone else has shared experiences with a similar setup.

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