Fine-tuning large language models can be resource-intensive. LoRA and QLoRA offer efficient methods to adapt these models by training only a small subset of parameters or by leveraging quantization techniques. This repository demonstrates how to fine-tune the LLAMA 2 model using these techniques.
- LoRA and QLoRA Integration: Efficient fine-tuning by training a subset of parameters or using quantization.
- Custom Dataset Compatibility: Adapt LLAMA 2 to specific datasets for tailored performance.
- Detailed Configuration: Customize training parameters to suit your needs.
- End-to-End Pipeline: Complete process from dataset loading to model training and inference.
- Python 3.8+
- CUDA-enabled GPU for training
Contributions are welcome! If you have ideas for improvements or new features, feel free to open an issue or submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.