This project explores the fine-tuning of large language models (LLMs) using the LoRA (Low-Rank Adaptation) and QLoRA (Quantized Low-Rank Adaptation) techniques. These methods are designed to improve the model's performance and efficiency, making them suitable for deployment in resource-constrained environments. The notebook provides an in-depth analysis and implementation of these fine-tuning approaches.
- LoRA and QLoRA Techniques: Implements fine-tuning of LLMs using state-of-the-art methods that optimize model size and compute resources.
- Detailed Analysis: Includes comprehensive evaluations of performance improvements and resource utilization.
- Extensible Framework: The code can be adapted to various LLMs and fine-tuning scenarios.
To run this notebook, you will need:
- Python 3.7 or higher
- PyTorch 1.8 or newer
- Transformers library by Hugging Face
Clone the repository to your local machine:
git clone https://github.com/Apoorva-Udupa/Fine-Tuning_LLMs_with_LoRA_and_QLoRA.git
cd fine-tuning-llm-lora