https://github.com/mltrev23/fine-tuning-llama-2
Fine tuning LLaMA-2 model on provided text data
https://github.com/mltrev23/fine-tuning-llama-2
ai fine-tune llama2 modeling
Last synced: 23 days ago
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Fine tuning LLaMA-2 model on provided text data
- Host: GitHub
- URL: https://github.com/mltrev23/fine-tuning-llama-2
- Owner: mltrev23
- Created: 2024-07-02T13:35:28.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-20T11:53:55.000Z (about 1 year ago)
- Last Synced: 2024-10-30T00:52:01.116Z (12 months ago)
- Topics: ai, fine-tune, llama2, modeling
- Language: Python
- Homepage:
- Size: 20.5 KB
- Stars: 12
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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.
```bash
git clone https://github.com/mltrev23/Fine-Tuning-LLaMA-2/
```
2. Install the required packages using the following command:
```bash
pip install -r requirements.txt
```
---
## Fine-tuning Llama-2
To fine-tune Llama-2, run the following command:
```shell
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:
```python
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:
```python
prompt = "What is bittensor?"
result = pipe(f"[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.