Instructions to use rafal-adamczyk/Qwen3.5-9B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use rafal-adamczyk/Qwen3.5-9B-MLX-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3.5-9B-MLX-4bit rafal-adamczyk/Qwen3.5-9B-MLX-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Qwen3.5-9B-MLX-4bit
4-bit MLX quantization of Qwen3.5-9B-MLX-4bit
Conversion
Quantized using mlx_lm.convert with 4-bit quantization (q_bits=4, q_group_size=64)
Usage
from mlx_lm import load, generate
model, tokenizer = load("rafal-adamczyk/Qwen3.5-9B-MLX-4bit")
response = generate(model, tokenizer, prompt="Hello!", verbose=True)
Original Model
See Qwen/Qwen3.5-9B for full details.
License
Apache 2.0 (inherited from original)
- Downloads last month
- 173
Model size
1B params
Tensor type
BF16
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U32 ·
F32 ·
Hardware compatibility
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4-bit
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Model tree for rafal-adamczyk/Qwen3.5-9B-MLX-4bit
Base model
Qwen/Qwen3.5-9B-Base Finetuned
Qwen/Qwen3.5-9B