Our verdict
A solid, actively developed project. This assessment is derived from GitHub's own repository metrics on 2026-08-24, not from hands-on testing.
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024). The project is written primarily in Python, released under Apache-2.0, and has 74,318 stars and 9,094 forks on GitHub. 296 contributors have committed to it, and the most recent push was 2026-08-20. The latest tagged release is v0.9.5 (2026-05-30). Figures come from the GitHub API on 2026-08-24 and are refreshed daily; the score below weighs adoption, maintenance, release discipline, contributor breadth, licence clarity and issue hygiene.
Repository
74.3kStars
9.1kForks
344Watching
296Contributors
1.1kOpen issues
- Licence
- Apache-2.0
- Language
- Python
- Last push
- 2026-08-20
Releases
-
v0.9.5v0.9.5: Qwen3.5/3.6, Gemma 4, Transformers v5 -
v0.9.4v0.9.4: Goodbye 2025 -
v0.9.3v0.9.3: Llama4, Gemma3, Qwen3, InternVL3, Qwen2.5-Omni -
v0.9.2v0.9.2: MiniCPM-o, SwanLab, APOLLO -
v0.9.1v0.9.1: Many Vision Models, Qwen2.5 Coder, Gradient Fix -
v0.9.0v0.9.0: Qwen2-VL, Liger-Kernel, Adam-mini
Pros and cons
Pros
Cons
User reviews
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Side by side
| Tool | Our score | From | Free tier | Best for | |
|---|---|---|---|---|---|
| LlamaFactory this page | 8.6 | — | Yes | ||
| Transformers | 9.4 | — | Yes | ||
| Ultralytics | 9.1 | — | Yes | ||
| Context7 | 9 | — | Yes | ||
| MoneyPrinterTurbo | 9 | — | Yes | ||
| vLLM | 8.9 | — | Yes | ||
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