Instructions to use spicyneuron/Qwen3.5-122B-A10B-MLX-4.7bit-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use spicyneuron/Qwen3.5-122B-A10B-MLX-4.7bit-vision with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("spicyneuron/Qwen3.5-122B-A10B-MLX-4.7bit-vision") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use spicyneuron/Qwen3.5-122B-A10B-MLX-4.7bit-vision with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "spicyneuron/Qwen3.5-122B-A10B-MLX-4.7bit-vision" --prompt "Once upon a time"
Qwen3.5-122B-A10B optimized for MLX. This quant supports image input and requires a vision-enabled MLX server.
For the non-vision model: https://huggingface.co/spicyneuron/Qwen3.5-122B-A10B-MLX-4.6bit
EDIT: Updated chat template to enable better prompt caching.
Usage
# Start server at http://localhost:8080/chat/completions
uvx --from mlx-vlm --with torchvision \
mlx_vlm.server \
--host 127.0.0.1 \
--port 8080 \
--model spicyneuron/Qwen3.5-122B-A10B-MLX-4.7bit-vision
Methodology
Quantized using a custom script inspired by Unsloth/AesSedai/ubergarm style mixed-precision GGUFs. MLX quantization options differ than llama.cpp, but the principles are the same:
- Sensitive layers like MoE routing, attention, and output embeddings get higher precision
- More tolerant layers like MoE experts get lower precision
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Model size
20B params
Tensor type
BF16
路
U32 路
F32 路
Hardware compatibility
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Quantized
Model tree for spicyneuron/Qwen3.5-122B-A10B-MLX-4.7bit-vision
Base model
Qwen/Qwen3.5-122B-A10B