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ashishpatel26/LLM-Finetuning

A collection of guides and code for efficiently fine-tuning large language models using PEFT (LoRA) and Hugging Face transformers.

Core Features

Efficient LLM fine-tuning with LoRA and QLoRa methods

Seamless integration with Hugging Face transformers library

Guides for fine-tuning various LLMs (Llama 2, LLaMA-7B, Bloom, Falcon)

Interactive Colab notebooks for practical learning and execution

Demonstrates BNB self-supervised training for Falcon-7b

Detailed Introduction

This project provides a practical repository for developers and researchers looking to efficiently fine-tune large language models. Leveraging advanced techniques like PEFT (Parameter-Efficient Fine-Tuning) and LoRA (Low-Rank Adaptation), it significantly reduces computational costs and resource requirements compared to full fine-tuning, making LLM customization more accessible. The project integrates seamlessly with Hugging Face's transformers library, offering a collection of interactive Colab notebooks. These notebooks guide users through fine-tuning popular models such as Llama 2, LLaMA-7B, Bloom, and Falcon, demonstrating various methods including QLoRa and BNB self-supervised training, thereby democratizing advanced LLM customization.