{"data":{"slug":"tloen-alpaca-lora","name":"alpaca-lora","tagline":"Instruct-tune LLaMA on consumer hardware","github_url":"https://github.com/tloen/alpaca-lora","owner":"tloen","repo":"alpaca-lora","owner_avatar_url":"https://avatars.githubusercontent.com/u/4811103?v=4","primary_language":"Jupyter Notebook","stars":18913,"forks":2185,"topics":[],"archived":false,"github_pushed_at":"2024-07-29T13:37:49+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/tloen-alpaca-lora","markdown_url":"https://www.graphcanon.com/tools/tloen-alpaca-lora.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/tloen-alpaca-lora","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=tloen-alpaca-lora","description":"Instruct-tune LLaMA on consumer hardware","homepage_url":null,"license":"Apache-2.0","open_issues":366,"watchers":150,"ai_summary":null,"readme_excerpt":"### Docker Setup & Inference\n\n1. Build the container image:\n\n```bash\ndocker build -t alpaca-lora .\n```\n\n2. Run the container (you can also use `finetune.py` and all of its parameters as shown above for training):\n\n```bash\ndocker run --gpus=all --shm-size 64g -p 7860:7860 -v ${HOME}/.cache:/root/.cache --rm alpaca-lora generate.py \\\n    --load_8bit \\\n    --base_model 'decapoda-research/llama-7b-hf' \\\n    --lora_weights 'tloen/alpaca-lora-7b'\n```\n\n3. Open `https://localhost:7860` in the browser\n\n---\n\n### Docker Compose Setup & Inference\n\n1. (optional) Change desired model and weights under `environment` in the `docker-compose.yml`\n\n2. Build and run the container\n\n```bash\ndocker-compose up -d --build\n```\n\n3. Open `https://localhost:7860` in the browser\n\n4. See logs:\n\n```bash\ndocker-compose logs -f\n```\n\n5. Clean everything up:\n\n```bash\ndocker-compose down --volumes --rmi all\n```","github_created_at":"2023-03-13T21:52:36+00:00","created_at":"2026-07-11T23:21:46.150976+00:00","updated_at":"2026-07-11T23:22:01.714762+00:00","categories":[{"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":"computer-vision","name":"Computer Vision","url":"https://www.graphcanon.com/categories/computer-vision","markdown_url":"https://www.graphcanon.com/categories/computer-vision.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/computer-vision"},{"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":613591358,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-11T23:21:55.583Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":712,"last_release_at":null},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":28,"high_count":5,"last_scan_at":"2026-07-11T23:21:56.017Z","medium_count":12,"scan_profile":"deps","critical_count":1}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-11T23:21:55.295Z"},"deploy":{"source":"dockerfile:Dockerfile","self_host":true,"observed_at":"2026-07-11T23:21:55.295Z","managed_saas":false},"languages":{"value":["jupyter notebook","python"],"source":"github.language+pyproject.toml","observed_at":"2026-07-11T23:21:55.295Z"},"has_docker":{"value":true,"source":"dockerfile:Dockerfile","observed_at":"2026-07-11T23:21:55.295Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-07-11T23:21:55.295Z"}}}}