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Overview
QLoRA: Efficient Finetuning of Quantized LLMs
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- Languages
- jupyter notebook
Source: github.language · Jul 11, 2026
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Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 11, 2026)
python qlora.py --model_name_or_path <path_or_name>Source link
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README
License and Intended Use
We release the resources associated with QLoRA finetuning in this repository under MIT license. In addition, we release the Guanaco model family for base LLaMA model sizes of 7B, 13B, 33B, and 65B. These models are intended for purposes in line with the LLaMA license and require access to the LLaMA models.
Installation
To load models in 4bits with transformers and bitsandbytes, you have to install accelerate and transformers from source and make sure you have the latest version of the bitsandbytes library. After installing PyTorch (follow instructions here), you can achieve the above with the following command:
pip install -U -r requirements.txt
Getting Started
The qlora.py code is a starting point for finetuning and inference on various datasets.
Basic command for finetuning a baseline model on the Alpaca dataset:
python qlora.py --model_name_or_path <path_or_name>
For models larger than 13B, we recommend adjusting the learning rate:
python qlora.py –learning_rate 0.0001 --model_name_or_path <path_or_name>
To replicate our Guanaco models see below.