Finetuning Llama-3 on your own data

This repo gives you the code to fine-tune Llama-3 on your own data. In this example, we'll be finetuning on 500 pieces of data from the Math Instruct dataset from TIGER-Lab. LLMs are known for not being the best at complex multi-step math problems so we want to fine-tune an LLM on some of these problems and see how well it does.

We'll go through data cleaning, uploading your dataset, fine-tuning LLama-3-8B on it, then running evals to show the accuracy vs the base model. Fine-tuning will happen on Together and costs $5 with the current pricing.

Fine-tuning Llama-3 on MathInstruct

1. Make an account at Together AI and save your API key as an OS variable called TOGETHER_API_KEY.
2. Install the Together AI python library by running pip install together.
3. [Optional] Make an account with Weights and Biases and save your API key as WANDB_API_KEY.
4. Run 1-transform.py to do some data cleaning and get it into a format Together accepts.
5. Run 2-finetune.py to upload the dataset and start the fine-tuning job on Together.
6. Run 3-eval.py to evaluate the fine-tuned model against a base model and get accuracy.
7. [Optional] Run utils/advanced-eval.py to run the model against other models like GPT-4 as well.

Results

Note: This repo contains 500 problems for training but we finetuned our model on 207k problems

After fine-tuning Llama-3-8B on 207k math problems from the MathInstruct dataset, we ran an eval of 1000 new math problems through to compare. Here were the results:

• Base model (Llama-3-8b): 47.2%
• Fine-tuned (Llama-3-8b) model: 65.2%
• Top OSS model (Llama-3-70b): 64.2%
• Top proprietary model (GPT-4o): 71.4%