jjang-ai/jangq
JANG — GGUF for MLX. YOU MUST USE JANG_Q RUNTIME. Adaptive Mixed-Precision Quantization + Runtime for Apple Silicon
- Stars
- 231
- Forks
- 28
- Open issues
- 10
- Last push
- 1d ago
jangq star history
+1 this week
About jangq
jangq is a free, open-source machine learning tool by jjang-ai, written mainly in Python. JANG — GGUF for MLX. YOU MUST USE JANG_Q RUNTIME. Adaptive Mixed-Precision Quantization + Runtime for Apple Silicon. It has 231 GitHub stars and 28 forks, and was last updated 1d ago. GitHub doesn't detect a standard license, so check the repository before using it commercially. According to the project, it works on macOS.
- License
- Not detected
- Commercial use
- No license
- Language
- Python
- Stars
- 231
- Last update
- 1d ago
- Runs on Mac
- Yes (as stated by the project)
- GPU
- Not stated
- Paid API key
- Not required
- Self-hostable
- Yes
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jangq FAQ
Can I use jangq commercially?
Unclear. No license was detected, which legally means all rights are reserved. Ask the author before using it commercially. This is a plain-English summary, not legal advice.
Is jangq free?
Yes. jangq is open source and free to use. GitHub doesn't detect a standard license, so check the repository before using it commercially.
Does jangq run on a Mac?
Yes. According to the project, jangq works on macOS.
Does jangq need a GPU?
The project doesn't state hardware needs. AI models generally run much faster with a GPU.
Is jangq still maintained?
Yes. It was last updated 1d ago and has 10 open issues on GitHub.
What are alternatives to jangq?
Similar free, open-source tools include Liger-Kernel, pmetal, Jackrong-llm-finetuning-guide, mlx-vlm.
Maintain jangq?
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