MLX
Safetensors
English
Chinese
Korean
qwen3_5_moe
jang
quantized
mixed-precision
apple-silicon
Mixture of Experts
vlm
reasoning
thinking
Instructions to use JANGQ-AI/Qwen3.5-122B-A10B-JANG_2S with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use JANGQ-AI/Qwen3.5-122B-A10B-JANG_2S with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3.5-122B-A10B-JANG_2S JANGQ-AI/Qwen3.5-122B-A10B-JANG_2S
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat

- Xet hash:
- 566ec191ac694d9d53b2f8c5286b58e1d0b1a96587fc666ca44e571e656cab71
- Size of remote file:
- 840 kB
- SHA256:
- 1f9c870a815749841432c294bebe196a11b4a7788e85f1de885b000c51b3d703
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