/Fine-Tune-LLAMA-2-With-Custom-Dataset-Using-LoRA-And-QLoRA-Techniques

This repository provides a comprehensive guide and implementation for fine-tuning the LLAMA 2 language model using custom datasets. By using Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA), it enables efficient and scalable model fine-tuning, making it suitable for resource-limited environments.

Primary LanguageJupyter NotebookMIT LicenseMIT

Fine-Tuning LLAMA 2 with Custom Dataset Using LoRA and QLoRA Techniques

Introduction

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.

Features

  • 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.

Requirements

  • Python 3.8+
  • CUDA-enabled GPU for training

Contributions

Contributions are welcome! If you have ideas for improvements or new features, feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.