Best Way to fine tune Llama 3?

Beginners
1

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:

  1. Dataset Size: How large does the dataset need to be to notice a meaningful difference in performance?
  2. Dataset Format: Is the format of the dataset important? Is a CSV file fine?
  3. Fine-Tuning Methods: What’s the best way to fine-tune LLaMA 3? Has anyone used the Unsloth framework, and would you recommend it?
2

Hello, Biren here
Answers:

  1. I would recommend having around 10k+ entries to get a good impact of learning
  2. I personally recommend JSON format
[
   {
        "instruction": "Question",
        "response": "Answer"
    },
]
  1. I would recommend training it using auto train on a large AWS instance for better accuracy.
    OR
    Below is the link for unsloth you can used there refrences to train it on colab or desktop
    GitHub - unslothai/unsloth: Finetune Llama 3, Mistral, Phi & Gemma LLMs 2-5x faster with 80% less memory

Hoping this helps…