Best Way to fine tune Llama 3?
I’m planning to create a fine-tuned version of LLaMA 3 and need some advice on the best approach. I have a few questions:Dataset Size: How large does the dataset need to be to notice a meaningful difference in performance?Dataset Format: Is the format of the dataset important? Is a CSV file fine?Fine-Tuning Methods: What’s the best way to fine-tune LLaMA 3? Has anyone used the Unsloth framework, and would you recommend it?
2024-06-14T12:25:50Z
Hello, Biren hereAnswers:I would recommend having around 10k+ entries to get a good impact of learningI personally recommend JSON format[
   {
        "instruction": "Question",
        "response": "Answer"
    },
]I would recommend training it using auto train on a large AWS instance for better accuracy.ORBelow is the link for unsloth you can used there refrences to train it on colab or desktopGitHub - unslothai/unsloth: Finetune Llama 3, Mistral, Phi & Gemma LLMs 2-5x faster with 80% less memoryHoping this helps…