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Qwen

QwenLM/Qwen

Official repo of Qwen, a large language model by Alibaba Cloud

GraphCanon updated 4d · GitHub synced 4d · 39 views this month

22k stars1.9k forksLast push 5mo Python Apache-2.0

Decision brief

Qwen is a large language model by Alibaba Cloud with support for Chinese and advanced features like flash-attention.

Good fit when

  • If you are working with extensive Chinese text data, Qwen offers specialized capabilities that might not be found in other generic LLMs.
  • For applications requiring high-efficiency inference using flash-attention on GPU, Qwen provides optimized utilities and instructions for deployment.

Avoid when

  • If your project strictly requires a specific type of licensing, Qwen operates under the Apache-2.0 License with additional license agreements that may differ from general expectations and could pose a
Requirements:
Python 3.8+; PyTorch 1.12+; Transformers 4.32+; CUDA 11.4+ (GPU users)

Observed Jul 11, 2026 · Source: enrich:decision_facts

Verify the decision

Maintenance and security

Full trust report
Maintenance
Slowing (164d since push)
As of 4d
Provenance
Not a fork · Organization account
As of 4d
Security (OSV)
44 low (44 low)
As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install Qwen
PyPI

How it fits your stack(18)

Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.

Alternative

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Similar tools

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

Provides the source code and deployment instructions for using Qwen, including pretrained models and utilities for flashing attention. Supports Python 3.8+, PyTorch, Transformers, and CUDA.

Capability facts

Languages
python

Source: github.language · Aug 17, 2026

Categories

Graph entities

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

OpenAI APIOpenAI API

Source: README excerpt (regex_v1, Aug 17, 2026)

nly need to install the driver and download model files to launch demos, deploy OpenAI API, and finetune the model.
Source link
Python runtimePython

Source: README excerpt (regex_v1, Aug 17, 2026)

* python 3.8 and above
Source link

Tags

README

Requirements

  • python 3.8 and above
  • pytorch 1.12 and above, 2.0 and above are recommended
  • transformers 4.32 and above
  • CUDA 11.4 and above are recommended (this is for GPU users, flash-attention users, etc.)

pip install csrc/rotary


Now you can start with ModelScope or Transformers.

---

## 🐳 Docker

To 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.

---

# configure docker
sudo systemctl start docker

---

# test if docker is correctly installed
sudo docker run hello-world

---

### Deployment

Here we use Qwen-7B-Chat as an example. Before launching a web demo or API, you can setup the configuration as shown below:

```bash
IMAGE_NAME=qwenllm/qwen:cu117
PORT=8901
CHECKPOINT_PATH=/path/to/Qwen-7B-Chat   # Path to downloaded model checkpoints and codes

The following scripts can help you build:

  • OpenAI API
bash docker/docker_openai_api.sh -i ${IMAGE_NAME} -c ${CHECKPOINT_PATH} --port ${PORT}
  • Web UI
bash docker/docker_web_demo.sh -i ${IMAGE_NAME} -c ${CHECKPOINT_PATH} --port ${PORT}
  • CLI Demo
bash docker/docker_cli_demo.sh -i ${IMAGE_NAME} -c ${CHECKPOINT_PATH}

The 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.

The demo is successfully launched if you see the following output:

Successfully started web demo. Open '...' to try!
Run `docker logs ...` to check demo status.
Run `docker rm -f ...` to stop and remove the demo.

If you want to check the status of the demo, you can use docker logs qwen to display outputs.

You can use docker rm -f qwen to stop the service and remove the container.


License Agreement

The source code provided at https://github.com/QwenLM/Qwen is licensed under the Apache 2.0 License that can be found at the root directory.

Researchers 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.

  • Qwen-72B, Qwen-14B, and Qwen-7B are licensed under the Tongyi Qianwen LICENSE AGREEMENT that can be found at the corresponding HuggingFace and ModelScope repository. For commercial use, please fill out the form (72B, 14B, and 7B) to apply.

  • Qwen-1.8B is licensed under the Tongyi Qianwen RESEARCH LICENSE AGREEMENT that can be found at the corresponding HuggingFace and ModelScope repository. For commercial use, please contact us.

For agents

This page has a .md twin and JSON over the API.

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