Welcome to this repository! 🎉 Here, we explore fine-tuning Large Language Models (LLMs) using QLoRA (Quantized Low-Rank Adaptation). This approach allows you to efficiently adapt large models with lower memory and compute requirements, making advanced AI more accessible. 💡
This repo provides example code and workflows to help you get started with building custom LLM applications using Mistral-7B-Instruct.
You’ll find everything you need to fine-tune LLMs effectively:
- 🛠️ Complete QLoRA Fine-Tuning Workflow – Step-by-step guide to adapt large models efficiently
- 📄 Training Scripts & Configuration Files – Ready-to-use scripts to streamline your training
- 🗂️ Dataset Preparation Guidance – Tips and templates to structure your data for optimal results
- 🤖 End-to-End Example – Demonstrates how to build an automated response system using Mistral-7B-Instruct
This project is perfect for:
- AI/ML enthusiasts exploring efficient LLM fine-tuning
- Developers looking to implement low-resource, production-ready LLM solutions
- Anyone interested in practical AI application development
By the end, you’ll have a solid foundation for training custom LLMs while keeping compute and memory requirements manageable. ⚡
- Clone the repository:
git clone https://github.com/nisargpatel28/QLoRA-LLMs.git