{"data":{"slug":"bigcode-project-starcoder","name":"starcoder","tagline":"Home of StarCoder: fine-tuning & inference!","github_url":"https://github.com/bigcode-project/starcoder","owner":"bigcode-project","repo":"starcoder","owner_avatar_url":"https://avatars.githubusercontent.com/u/110470554?v=4","primary_language":"Python","stars":7503,"forks":525,"topics":[],"archived":false,"github_pushed_at":"2024-02-27T02:05:57+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/bigcode-project-starcoder","markdown_url":"https://www.graphcanon.com/tools/bigcode-project-starcoder.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/bigcode-project-starcoder","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=bigcode-project-starcoder","description":"Home of StarCoder: fine-tuning & inference!","homepage_url":null,"license":"Apache-2.0","open_issues":103,"watchers":65,"ai_summary":"A repository for handling the installation and usage of StarCoder, including setup instructions with conda and pip, requirements for fine-tuning and inference, and hardware guidelines.","readme_excerpt":"## Installation\nFirst, we have to install all the libraries listed in `requirements.txt`\n```bash\npip install -r requirements.txt\n```\n\n---\n\n## Step by step installation with conda \n\nCreate a new conda environment and activate it\n```bash\nconda create -n env\nconda activate env\n```\nInstall the `pytorch` version compatible with your version of cuda [here](https://pytorch.org/get-started/previous-versions/), for example the following command works with cuda 11.6\n```bash\nconda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia\n```\nInstall `transformers` and `peft`\n```bash\nconda install -c huggingface transformers \npip install git+https://github.com/huggingface/peft.git\n```\nNote that you can install the latest stable version of transformers by using\n\n```bash\npip install git+https://github.com/huggingface/transformers\n```\n\nInstall `datasets`, `accelerate` and `huggingface_hub`\n\n```bash\nconda install -c huggingface -c conda-forge datasets\nconda install -c conda-forge accelerate\nconda install -c conda-forge huggingface_hub\n```\n\nFinally, install `bitsandbytes` and `wandb`\n```bash\npip install bitsandbytes\npip install wandb\n```\nTo get the full list of arguments with descriptions you can run the following command on any script:\n```\npython scripts/some_script.py --help\n```\nBefore you run any of the scripts make sure you are logged in and can push to the hub:\n```bash\nhuggingface-cli login\n```\nMake sure you are logged in `wandb`:\n```bash\nwandb login\n```\nNow that everything is done, you can clone the repository and get into the corresponding directory.\n\n---\n\n# Inference hardware requirements\nIn FP32 the model requires more than 60GB of RAM, you can load it in FP16 or BF16 in ~30GB, or in 8bit under 20GB of RAM with\n```python","github_created_at":"2023-04-24T12:32:21+00:00","created_at":"2026-07-11T23:43:56.614274+00:00","updated_at":"2026-08-05T12:01:57.383507+00:00","categories":[{"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"},{"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"}],"tags":[{"slug":"conda","name":"conda"},{"slug":"fine-tuning","name":"fine-tuning"},{"slug":"inference","name":"inference"},{"slug":"pip","name":"pip"},{"slug":"starcoder","name":"starcoder"}],"trust":{"provenance":{"is_fork":false,"github_id":631962458,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-05T12:01:56.608Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":890,"last_release_at":null},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":48,"high_count":0,"last_scan_at":"2026-07-11T23:43:57.987Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-05T12:01:57.092Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-08-05T12:01:57.092Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-05T12:01:57.092Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you need to perform inference tasks on a model that can be managed within hardware constraints such as FP16 or BF16 formats in about 30GB of RAM","If your project requires detailed setup instructions using conda, for environments needing specific versions of PyTorch compatible with CUDA"],"when_not_to_use":["Avoid if your environment lacks resources and you cannot meet the minimum memory requirement even in an 8-bit precision mode which needs under 20GB RAM","If you seek a tool that does not rely heavily on setup instructions guided by specific frameworks such as Hugging Face Transformers or PyTorch"],"source":"enrich:decision_facts","observed_at":"2026-07-17T13:04:03.564Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Starcoder, under Apache-2.0 license, provides tools for installation and usage of StarCoder, supporting both fine-tuning and inference processes."}]}}