# artidoro/qlora

QLoRA: Efficient Finetuning of Quantized LLMs

Repository: https://github.com/artidoro/qlora
Canonical: https://ross.abutalabs.com/products/qlora
Homepage: https://arxiv.org/abs/2305.14314
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2024-06-10T19:20:16+00:00

## Health v2 (maintenance only)
Score: 29/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 86
- inputs: {"age_days": 1210, "days_push": 814, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 10998, forks 876 (observed 2026-08-28T04:10:44.987290+00:00)

## What it is
QLoRA is the official implementation of the QLoRA paper, an efficient finetuning approach that backpropagates through a frozen 4-bit quantized LLM into Low Rank Adapters, enabling finetuning of a 65B model on a single 48GB GPU. It integrates bitsandbytes quantization with Hugging Face PEFT and transformers, and includes the Guanaco model family and training scripts.

## Use cases
- finetune a large language model on a single GPU
- run 4-bit quantized LoRA finetuning of LLaMA models
- train an instruction-following chatbot cheaply
- reproduce the Guanaco models from the QLoRA paper
- reduce memory usage when finetuning 33B or 65B parameter models
- create LoRA adapter weights for a quantized LLM

## When to choose
- you need to finetune very large LLMs with limited GPU memory
- you want the reference implementation of the QLoRA/NF4 method
- you want to train LoRA adapters on quantized models with bitsandbytes and PEFT

## When to avoid
- you need a maintained, general-purpose finetuning framework (the repo is a research artifact with infrequent updates)
- you want full 16-bit finetuning or non-LoRA training methods
- you need multi-GPU or production training pipelines beyond the provided scripts

## Facets
- artifact type: library
- maturity: maintenance
- function: llm-training, machine-learning, deep-learning
- domain: large-language-models, machine-learning, deep-learning
- platform: python
- tags: qlora, lora, quantization, 4-bit, finetuning, bitsandbytes, peft, guanaco, instruction-tuning, gpu, linux

## Member repositories
- artidoro/qlora (main) score 29

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:44.987290+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:17:14.450996+00:00, confidence not recorded.
  - readme: https://github.com/artidoro/qlora (fetched 2026-08-28T04:10:44.987290+00:00, sha dc2c59085140)
  - homepage: https://arxiv.org/abs/2305.14314 (fetched 2026-08-29T08:15:51.007500+00:00, sha 5a2c65e4ca1c)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T08:15:51.017450+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T08:15:51.021708+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T08:15:51.024077+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T08:15:51.019834+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.