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ashishpatel26/LLM-Finetuning

Provides a collection of Colab notebooks for efficiently fine-tuning large language models using PEFT methods like LoRA and Hugging Face transformers.

Core Features

Efficiently fine-tune large language models using LoRA.

Step-by-step guides for popular models like Llama 2 and Falcon-7b.

Leverages Hugging Face transformers and PEFT library.

Demonstrates various fine-tuning techniques including QLoRa.

Detailed Introduction

This project offers a practical collection of Google Colab notebooks designed to guide users through the process of efficiently fine-tuning large language models (LLMs). By leveraging techniques like PEFT (Parameter-Efficient Fine-Tuning) and LoRA (Low-Rank Adaptation) in conjunction with Hugging Face's transformers library, it enables developers and researchers to adapt powerful LLMs such as Llama 2, Falcon, Bloom, and OPT for specific tasks without extensive computational resources. It serves as an invaluable educational and development resource for anyone looking to customize pre-trained LLMs.