Project Title
qlora — Efficient Finetuning of Quantized Large Language Models (LLMs) for Research
Overview
QLoRA is an innovative approach to finetuning large language models (LLMs) that significantly reduces memory usage, allowing for the training of massive models on a single GPU. It leverages 4-bit quantization and Low Rank Adapters (LoRA) to maintain performance while democratizing access to LLM research. QLoRA stands out for its ability to achieve near ChatGPT-level performance with substantially less computational resources.
Key Features
- 4-bit NormalFloat (NF4) quantization for optimal memory efficiency
- Double Quantization to further reduce memory footprint
- Paged Optimizers to manage memory spikes during training
- Integration with Hugging Face's PEFT and transformers libraries
Use Cases
- Researchers and developers needing to finetune large language models with limited hardware resources
- Academic institutions and startups looking to perform LLM research without extensive infrastructure
- Enterprises seeking to deploy high-performance chatbots and instruction-following models with reduced costs
Advantages
- Enables finetuning of 65B parameter models on a single 48GB GPU
- Preserves full 16-bit finetuning task performance with 4-bit quantization
- Outperforms previous openly released models on the Vicuna benchmark
- Provides detailed analysis and models for various instruction datasets and model types
Limitations / Considerations
- QLoRA is designed for research purposes and may produce problematic outputs in certain applications
- Requires access to the LLaMA models for the Guanaco model family
- The project is relatively new, and long-term community support and updates are yet to be established
Similar / Related Projects
- bitsandbytes: A library for quantization used by QLoRA, offering different quantization techniques.
- transformers: A library by Hugging Face for state-of-the-art NLP models, which QLoRA integrates with.
- PEFT: A library by Hugging Face for parameter-efficient fine-tuning, which QLoRA also leverages.
Basic Information
- GitHub: https://github.com/artidoro/qlora
- Stars: 10,666
- License: MIT (as of the last update)
- Last Commit: 2025-09-19
📊 Project Information
- Project Name: qlora
- GitHub URL: https://github.com/artidoro/qlora
- Programming Language: Jupyter Notebook
- ⭐ Stars: 10,666
- 🍴 Forks: 862
- 📅 Created: 2023-05-11
- 🔄 Last Updated: 2025-09-19
🏷️ Project Topics
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