HuggingFaceH4

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starchat-beta

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I have recently tried to fine-tune the model (using the QLoRA method). While the fine-tuning process concluded without issue, the resulting output responses did not meet the anticipated standards since the generated contents were quite oddly. I suspect the root of this issue may stem from the training dataset, which may not have been adequately prepared to facilitate effective learning for the model. The model I have previously finetuned was trained usin gonly 'text' column from https://huggingface.co/datasets/heegyu/namuwiki-extracted, lacking any question-answering format. So, to enhance the finetuned model's performance, I am wondering whether the following format should be like this:

<|system|>\n{system prompt sample}<|end|>\n<|user|>\n{user prompt sample}<|end|>\n<|assistant|>\n{assistant prompt sample}<|end|>\n

As each training session for my model incurs considerable costs due to the GPU rental fees, I am hesitant to proceed with further training without advice. Therefore, I'm reaching out for opinions from professionals before training again. I greatly appreciate any guidance or recommendations that can help me optimize my process, my data and potentially save on these significant costs.