Instructions to use droyster/Qwen3.5-4B-Text-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use droyster/Qwen3.5-4B-Text-MLX-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3.5-4B-Text-MLX-4bit droyster/Qwen3.5-4B-Text-MLX-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Qwen3.5-4B-Text-MLX-4bit
mlx-community/Qwen3.5-4B-MLX-4bit with the unused vision tower removed:
297 vision_tower.* tensors dropped, 924 language-model tensors kept,
2.9 GB → 2.2 GB download (−667 MB). Nothing else is changed.
mlx_lm's qwen3_5 loader already discards vision tensors before load
(sanitize()), so this republish is purely a download/disk win — runtime
behavior is identical. Verified: maximum absolute logit delta vs the
original model is exactly 0.0 on a fixed prompt; greedy argmax matches.
Built for faster-context, a local repository-retrieval subagent for Apple Silicon, where this is the default serving model (zero-shot, no fine-tuning).
Use
pip install mlx-lm
mlx_lm.generate --model droyster/Qwen3.5-4B-Text-MLX-4bit --prompt "hello"
Provenance
- Base: Alibaba Qwen3.5-4B (Apache-2.0), community 4-bit MLX conversion
mlx-community/Qwen3.5-4B-MLX-4bit. - This repo: same weights, vision tower stripped with MLX
(
mx.load→ filtervision_tower.*→mx.save_safetensors),vision_configremoved fromconfig.json. Tokenizer files unchanged.
- Downloads last month
- 19
Model size
0.7B params
Tensor type
BF16
·
U32 ·
F32 ·
Hardware compatibility
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4-bit
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Model tree for droyster/Qwen3.5-4B-Text-MLX-4bit
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mlx-community/Qwen3.5-4B-MLX-4bit