Fine-Tune LLM Framework

This repository provides a framework for fine-tuning large language models (LLMs) such as GPT-2, LLAMA, and Mistral. The framework is designed to be flexible and easy to use, allowing users to specify the pretrained model, dataset, and optimization options via command-line arguments.

Features

Support for popular LLMs: GPT-2, LLAMA, Mistral
Command-line interface for easy configuration
Support for optimization methods: LoRA, QLORA

Installation

Clone this repository and install the required dependencies:

git clone https://github.com/yourusername/finetune-llm-framework.git
cd finetune-llm-framework
pip install -r requirements.txt

Usage

Fine-tune a pretrained model with a specified dataset:

python src/main.py --model_name gpt2 --dataset_name wikitext --output_dir ./results --epochs 3 --batch_size 8 --learning_rate 5e-5 --optimization lora

Run inference on the fine-tuned model:

python src/inference.py --model_dir ./results --input_text "Once upon a time"

Arguments for Training

model_name: Pretrained model name (e.g., gpt2, llama, mistral)
dataset_name: Dataset name (e.g., wikitext, glue)
output_dir: Directory to save the results
epochs: Number of training epochs
batch_size: Batch size for training
learning_rate: Learning rate
optimization: Optimization method (lora, qlora, none)

Arguments for Inference

model_dir: Directory of the fine-tuned model
input_text: Input text for inference

Example

To fine-tune the GPT-2 model on the Wikitext dataset using LoRA optimization:

python src/main.py --model_name gpt2 --dataset_name wikitext --output_dir ./results --epochs 3 --batch_size 8 --learning_rate 5e-5 --optimization lora

To run inference on the fine-tuned model:

python src/inference.py --model_dir ./results --input_text "Once upon a time"

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License

This project is licensed under the MIT License.

m-peker/Simple-LLM-Fine-Tuning-Framework