{"data":{"slug":"qwenlm-qwen","name":"Qwen","tagline":"Official repo of Qwen, a large language model by Alibaba Cloud","github_url":"https://github.com/QwenLM/Qwen","owner":"QwenLM","repo":"Qwen","owner_avatar_url":"https://avatars.githubusercontent.com/u/141221163?v=4","primary_language":"Python","stars":21772,"forks":1916,"topics":["chinese","flash-attention","large-language-models","llm","natural-language-processing","pretrained-models"],"archived":false,"github_pushed_at":"2026-03-05T13:55:17+00:00","maintenance_label":"Slowing","stars_delta_30d":177,"url":"https://www.graphcanon.com/tools/qwenlm-qwen","markdown_url":"https://www.graphcanon.com/tools/qwenlm-qwen.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/qwenlm-qwen","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=qwenlm-qwen","description":"The official repo of Qwen (通义千问) chat & pretrained large language model proposed by Alibaba Cloud.","homepage_url":null,"license":"Apache-2.0","open_issues":43,"watchers":159,"ai_summary":"Qwen is a large language model and chat model developed by Alibaba Cloud, available in Python with support for PyTorch and Transformers. It includes Docker images for easy deployment and supports various deployment options including OpenAI API, Web UI, and CLI demo.","readme_excerpt":"## Requirements\n\n* python 3.8 and above\n* pytorch 1.12 and above, 2.0 and above are recommended\n* transformers 4.32 and above\n* CUDA 11.4 and above are recommended (this is for GPU users, flash-attention users, etc.)\n<br>\n\n---\n\n# pip install csrc/rotary\n```\n\nNow you can start with ModelScope or Transformers.\n\n---\n\n## 🐳 Docker\n\nTo simplify the deployment process, we provide docker images with pre-built environments: [qwenllm/qwen](https://hub.docker.com/r/qwenllm/qwen). You only need to install the driver and download model files to launch demos, deploy OpenAI API, and finetune the model.\n\n---\n\n# configure docker\nsudo systemctl start docker\n\n---\n\n# test if docker is correctly installed\nsudo docker run hello-world\n\n---\n\n### Deployment\n\nHere we use Qwen-7B-Chat as an example. Before launching a web demo or API, you can setup the configuration as shown below:\n\n```bash\nIMAGE_NAME=qwenllm/qwen:cu117\nPORT=8901\nCHECKPOINT_PATH=/path/to/Qwen-7B-Chat   # Path to downloaded model checkpoints and codes\n```\nThe following scripts can help you build:\n\n* OpenAI API\n```bash\nbash docker/docker_openai_api.sh -i ${IMAGE_NAME} -c ${CHECKPOINT_PATH} --port ${PORT}\n```\n\n* Web UI\n```bash\nbash docker/docker_web_demo.sh -i ${IMAGE_NAME} -c ${CHECKPOINT_PATH} --port ${PORT}\n```\n\n* CLI Demo\n```bash\nbash docker/docker_cli_demo.sh -i ${IMAGE_NAME} -c ${CHECKPOINT_PATH}\n```\n\nThe commands above will automatically download the required image and launch a Web UI demo in background (the service will auto-restart). You can open `http://localhost:${PORT}` on the host to use the demo.\n\nThe demo is successfully launched if you see the following output:\n\n```text\nSuccessfully started web demo. Open '...' to try!\nRun `docker logs ...` to check demo status.\nRun `docker rm -f ...` to stop and remove the demo.\n```\n\nIf you want to check the status of the demo, you can use `docker logs qwen` to display outputs.\n\nYou can use `docker rm -f qwen` to stop the service and remove the container.\n\n---\n\n## License Agreement\n\nThe source code provided at <https://github.com/QwenLM/Qwen> is licensed under the [Apache 2.0 License](./LICENSE) that can be found at the root directory.\n\nResearchers and developers are free to use the codes and model weights of both Qwen and Qwen-Chat. For their commercial use, please check the License Agreement accompanying each model.\n\n- Qwen-72B, Qwen-14B, and Qwen-7B are licensed under the [Tongyi Qianwen LICENSE AGREEMENT](./Tongyi%20Qianwen%20LICENSE%20AGREEMENT) that can be found at the corresponding HuggingFace and ModelScope repository. For commercial use, please fill out the form ([72B](https://dashscope.console.aliyun.com/openModelApply/Qwen-72B-Chat), [14B](https://dashscope.console.aliyun.com/openModelApply/Qwen-14B-Chat), and [7B](https://dashscope.console.aliyun.com/openModelApply/qianwen)) to apply.\n\n- Qwen-1.8B is licensed under the [Tongyi Qianwen RESEARCH LICENSE AGREEMENT](./Tongyi%20Qianwen%20RESEARCH%20LICENSE%20AGREEMENT) that can be found at the corresponding HuggingFace and ModelScope repository. For commercial use, please contact us.\n<br><br>","github_created_at":"2023-08-03T04:56:38+00:00","created_at":"2026-07-07T17:33:17.468764+00:00","updated_at":"2026-09-20T04:33:20.013933+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":"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"}],"tags":[{"slug":"chinese","name":"chinese"},{"slug":"docker","name":"docker"},{"slug":"flash-attention","name":"flash-attention"},{"slug":"large-language-models","name":"large-language-models"},{"slug":"llm","name":"llm"},{"slug":"natural-language-processing","name":"natural language processing"},{"slug":"pretrained-models","name":"pretrained-models"},{"slug":"pytorch","name":"pytorch"}],"trust":{"provenance":{"is_fork":false,"github_id":674075444,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-18T08:47:01.779Z","maintenance":{"label":"Slowing","score":36,"methodology":"github_public_v1","releases_90d":0,"days_since_push":196,"last_release_at":null,"stars_delta_30d":177,"open_issues_delta_30d":-1},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":50,"high_count":0,"last_scan_at":"2026-09-18T08:47:03.982Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-18T08:47:00.659Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-09-18T08:47:00.659Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-09-18T08:47:00.659Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":{"notes":["Requires Python 3.8 and above.","PyTorch 1.12 and above is required, with 2.0 and above recommended.","Transformers 4.32 and above are required.","CUDA 11.4 and above are recommended for GPU users."],"min_ram_gb":null,"requires_docker":true},"constraints":{"min_ram_gb":null,"requires_docker":true},"when_to_use":["When you need a large language model that supports Chinese language processing effectively.","If you are working with PyTorch and Transformers and want a model that integrates seamlessly with these frameworks.","For projects that require deployment flexibility, including OpenAI API, Web UI, and CLI demo options."],"when_not_to_use":["If your project strictly requires models under a different license, as Qwen is under the Apache-2.0 License with specific commercial use agreements.","When you need a model that does not require CUDA 11.4 or above for GPU acceleration, as Qwen recommends this for optimal performance.","If your deployment environment does not support Docker, as Qwen heavily relies on Docker images for easy setup and deployment."],"source":"enrich:decision_facts","observed_at":"2026-09-18T10:05:50.403Z"},"constraint_facets":{"min_ram_gb":null,"requires_docker":true},"decision_summary":[{"label":"Requirements","value":"Requires Docker; Requires Python 3.8 and above.; PyTorch 1.12 and above is required, with 2.0 and above recommended.; Transformers 4.32 and above are required.; CUDA 11.4 and above are recommended for GPU users."},{"label":"Adopt for","value":"Qwen is a large language model developed by Alibaba Cloud, available in Python with support for PyTorch and Transformers. It offers Docker images for easy deployment and supports various deployment options including Open"}]}}