{"data":{"slug":"artidoro-qlora","name":"qlora","tagline":"QLoRA: Efficient Finetuning of Quantized LLMs","github_url":"https://github.com/artidoro/qlora","owner":"artidoro","repo":"qlora","owner_avatar_url":"https://avatars.githubusercontent.com/u/11949572?v=4","primary_language":"Jupyter Notebook","stars":10952,"forks":876,"topics":[],"archived":false,"github_pushed_at":"2024-06-10T19:20:16+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/artidoro-qlora","markdown_url":"https://www.graphcanon.com/tools/artidoro-qlora.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/artidoro-qlora","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=artidoro-qlora","description":"QLoRA: Efficient Finetuning of Quantized LLMs","homepage_url":"https://arxiv.org/abs/2305.14314","license":"MIT","open_issues":207,"watchers":81,"ai_summary":null,"readme_excerpt":"## License and Intended Use\nWe release the resources associated with QLoRA finetuning in this repository under MIT license.\nIn 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.\n\n---\n\n## Installation\nTo 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](https://pytorch.org/get-started/locally/)), you can achieve the above with the following command:\n```bash\npip install -U -r requirements.txt\n```\n\n---\n\n## Getting Started\nThe `qlora.py` code is a starting point for finetuning and inference on various datasets.\nBasic command for finetuning a baseline model on the Alpaca dataset:\n```bash\npython qlora.py --model_name_or_path <path_or_name>\n```\n\nFor models larger than 13B, we recommend adjusting the learning rate:\n```bash\npython qlora.py –learning_rate 0.0001 --model_name_or_path <path_or_name>\n```\n\nTo replicate our Guanaco models see below.","github_created_at":"2023-05-11T09:30:23+00:00","created_at":"2026-07-11T23:22:17.364535+00:00","updated_at":"2026-07-11T23:22:30.571702+00:00","categories":[{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"},{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"}],"tags":[{"slug":"jupyter-notebook","name":"jupyter notebook"}],"trust":{"provenance":{"is_fork":false,"github_id":639346169,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-11T23:22:23.494Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":761,"last_release_at":null},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":48,"high_count":0,"last_scan_at":"2026-07-11T23:22:24.112Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-11T23:22:23.119Z"},"languages":{"value":["jupyter notebook"],"source":"github.language","observed_at":"2026-07-11T23:22:23.119Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-07-11T23:22:23.119Z"}}}}