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Fine-tuning Llama-2

This project aims to fine-tune the Llama-2 language model using Hugging Face's Transformers library. By following these steps, you can fine-tune the model and use it for inference.

Prerequisites

Before getting started, make sure you have the following:

  • Hugging Face API token (HF token)
  • Python installed on your system

Setup

  1. Clone this repository to your local machine.

    git clone https://github.com/mltrev23/Fine-Tuning-LLaMA-2/

  2. Install the required packages using the following command:

    pip install -r requirements.txt

Fine-tuning Llama-2

To fine-tune Llama-2, run the following command:

python main.py

This command will fine-tune the model using the specified dataset and save the fine-tuned model to the llama-2-7b-chat-guanaco folder.

Custom Data Ingestion

To ingest your own data for fine-tuning, you'll need to modify the code in your script. Here's an example of how to load a custom dataset:

from datasets import load_dataset

# Load your dataset from a text file
dataset = load_dataset('text', data_files='datasets/bittensor.txt', split='train')

Make sure to replace 'datasets/bittensor.txt' with the path to your dataset file.

Happy fine-tuning!

Inference

To perform inference using the fine-tuned Llama-2 model, follow these steps:

  1. Ensure you've successfully fine-tuned Llama-2 as explained in the Fine-tuning Llama-2 section.

  2. After training, you can run inference directly in the main.py script. The fine-tuned model will be used for generating text based on prompts.

Example Inference

You can modify the prompt in the main.py file to test the model:

prompt = "What is bittensor?"
result = pipe(f"<s>[INST] {prompt} [/INST]")
print(result[0]['generated_text'])

This will output the generated text based on the prompt provided.

Additional Notes

  • Ensure you have all necessary dependencies installed as listed in requirements.txt.
  • Adjust the training parameters in the TrainingArguments section of main.py as needed to fit your requirements.

Summary of Changes

  • The command to run the fine-tuning process is simplified to just python main.py.
  • The data ingestion section is updated to reflect how to load a dataset using the datasets library.
  • Inference instructions are included to show how to modify prompts for testing the model.
  • Additional notes encourage users to check dependencies and adjust training parameters.