{"data":{"slug":"zai-org-codegeex","name":"CodeGeeX","tagline":"CodeGeeX is an open multilingual code generation model implemented in Mindspore and available via PyTorch.","github_url":"https://github.com/zai-org/CodeGeeX","owner":"zai-org","repo":"CodeGeeX","owner_avatar_url":"https://avatars.githubusercontent.com/u/223098841?v=4","primary_language":"Python","stars":8809,"forks":688,"topics":["code-generation","pretrained-models","tools"],"archived":false,"github_pushed_at":"2024-08-13T05:59:38+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/zai-org-codegeex","markdown_url":"https://www.graphcanon.com/tools/zai-org-codegeex.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/zai-org-codegeex","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=zai-org-codegeex","description":"CodeGeeX: An Open Multilingual Code Generation Model (KDD 2023)","homepage_url":"https://codegeex.cn","license":"Apache-2.0","open_issues":188,"watchers":86,"ai_summary":"An open-source project for generating code through a multilingual code-generation model, initially developed with MindSpore and now compatible with Torch. It requires Python 3.7+, CUDA 11+, PyTorch 1.10+, DeepSpeed 0.6+. Users can set up the environment directly or via Docker.","readme_excerpt":"## Getting Started\n\nCodeGeeX is initially implemented in Mindspore and trained Ascend 910 AI Processors. We provide a torch-compatible version based on [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) to facilitate usage on GPU platforms.\n\n---\n\n### Installation\n\nPython 3.7+ / CUDA 11+ / PyTorch 1.10+ / DeepSpeed 0.6+ are required. Install ``codegeex`` package via: \n```bash\ngit clone git@github.com:THUDM/CodeGeeX.git\ncd CodeGeeX\npip install -e .\n```\nOr use [CodeGeeX docker](https://hub.docker.com/r/codegeex/codegeex) to quickly set up the environment (with [nvidia-docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker) installed):\n```bash\ndocker pull codegeex/codegeex:latest\n\n---\n\n## License\n\nOur code is licensed under the [Apache-2.0 license](LICENSE).\nOur model is licensed under the [license](MODEL_LICENSE).","github_created_at":"2022-09-17T14:06:29+00:00","created_at":"2026-07-11T23:20:04.651215+00:00","updated_at":"2026-08-02T18:01:18.829961+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"}],"tags":[{"slug":"ai-programming-tools","name":"ai programming tools"},{"slug":"code-generation","name":"code generation"},{"slug":"pretrained-models","name":"pretrained-models"}],"trust":{"provenance":{"is_fork":false,"github_id":537827151,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-02T18:01:18.092Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":719,"last_release_at":null},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":85,"high_count":0,"last_scan_at":"2026-07-11T23:20:09.632Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-02T18:01:18.526Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-08-02T18:01:18.526Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-02T18:01:18.526Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you require support for multilingual code generation and your project has a Python-based infrastructure with CUDA GPU availability.","For projects looking to integrate advanced AI-driven code completion or generation capabilities, given its compatibility with DeepSpeed for performance optimization on large-scale models."],"when_not_to_use":["If your development environment lacks the necessary dependencies like Python 3.7+, CUDA 11+, PyTorch 1.10+, and DeepSpeed 0.6+.","In scenarios where an open-source solution is not preferable or when support for exclusively one language's syntax is sufficient."],"source":"enrich:decision_facts","observed_at":"2026-07-17T04:15:00.999Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"CodeGeeX is an open-source multilingual code generation model, originally developed with MindSpore and compatible with PyTorch through DeepSpeed integration."}]}}