GraphCanon updated 3w · GitHub synced 3w
Decision brief
QLoRA specializes in accelerating the fine-tuning process of quantized large language models like those in the Guanaco family.
Good fit when
- Need efficient fine-tuning for quantized LLaMA-based models
- Working with limited computational resources, aiming to maintain performance
Avoid when
- Require native full-precision model tuning without efficiency constraints
- Focusing on non-LLaMA-based language models where specific adaptations may not apply
- Pricing:
- freemium - Open source under MIT License; requires access to LLaMA base models
- Requirements:
- Installation involves installing PyTorch and specific packages from source; Works with model sizes ranging from 7B to 65B, includes recommendations for tuning different sizes
Observed Jul 17, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Dormant (783d since push)
- As of 3w
- Provenance
- Not a fork · Personal account
- As of 3w
- Security (OSV)
- 48 low (48 low)
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/artidoro/qloraSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A repository for QLoRA, which provides tools for efficient fine-tuning of quantized large language models including the Guanaco model family.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Aug 3, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 3, 2026)
python qlora.py --model_name_or_path <path_or_name>Source link
Tags
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.
For agents
This page has a .md twin and JSON over the API.