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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
The project lists 31 contributors on GitHub.
Gained 15% more stars in the last 90 days than in the 90 days before.
Use Cases & Benefits
- Provides a series of pretrained large language models and chat models supporting Chinese and English with up to 72B parameters and 32k token context length.
- Delivers competitive multilingual performance with efficient quantization options and support for tool use, agents, and code interpretation.
- Use for building advanced multilingual chatbots and conversational AI applications requiring long-context understanding.
- Use for fine-tuning large language models with low GPU memory using LoRA and Q-LoRA techniques for domain-specific tasks.
- Use for deploying scalable LLM inference on multi-GPU, CPU, or cloud API services with integration examples for Transformers and ModelScope.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
Watch-outs
- No commits in the last six months (last push Mar 2026).
- No GitHub releases; pin a commit or tag instead of a release.
General guidance from repository metadata, not legal advice. How we score
From the README
中文  ｜  English  ｜  日本語 ｜  Français ｜  Español
WeChat (微信)   |   Discord   ｜   API
We opensource our Qwen series, now including Qwen, the base language models, namely Qwen-1.8B, Qwen-7B, Qwen-14B, and Qwen-72B, as well as Qwen-Chat, the chat models, namely Qwen-1.8B-Chat, Qwen-7B-Chat, Qwen-14B-Chat, and Qwen-72B-Chat. Links are on the above table. Click them and check the model cards. Also, we release the technical report. Please click the paper link and check it out!
In brief, we have strong base language models, which have been stably pretrained for up to 3 trillion tokens of multilingual data with a wide coverage of domains, languages (with a focus on Chinese and English), etc. They are able to achieve competitive performance on benchmark datasets. Additionally, we have chat models that are aligned with human preference based on SFT and RLHF (not released yet), which are able to chat, create content, extract information, summarize, translate, code, solve math problems, and so on, and are able to use tools, play as agents, or even play as code interpreters, etc.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
2.0K+128
Open issues
38-7
Compared with peers in LLM
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| thedotmack/claude-mem | 113.6K | +8.8K | 35 | today |
| NVIDIA/auto-ontology | 7.5K | +5.6K | 37 | — |
| HKUDS/DeepTutor | 55.5K | +5.5K | 65 | 5 days ago |
| affaan-m/ECC | 297.9K | +5.2K | 63 | 35 days ago |
| DietrichGebert/ponytail | 185.3K | +5.1K | 60 | 7 days ago |
| QwenLM/Qwen | 21.9K | — | 24 | — |
| Category median | 113.6K | +5.5K | 60 | — |
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