Instructions to use will702/Qwen3-8B-DFly-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use will702/Qwen3-8B-DFly-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3-8B-DFly-MLX will702/Qwen3-8B-DFly-MLX
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Qwen3-8B DFly MLX
MLX conversion of AngelSlim/Qwen3-8B-DFly-Block8 for native DFly speculative decoding on Apple Silicon.
The conversion uses upstream revision 5712926, the last revision with the correct Qwen3-8B metadata. The current upstream config.json describes an incompatible 80-layer target, so dfly-mlx pins and validates the known-good revision.
Use
pip install dfly-mlx
hf download will702/Qwen3-8B-DFly-MLX \
--local-dir ./models/qwen3-8b-dfly-mlx
dfly-mlx generate \
--draft ./models/qwen3-8b-dfly-mlx \
--prompt "Write a quicksort in Python." \
--max-tokens 256
The target defaults to mlx-community/Qwen3-8B-4bit. Generation is greedy and uses Qwen3 no-thinking mode.
Verification status
Checkpoint conversion, loading, and generation were tested on Apple Silicon with MLX 0.32.0. Every emitted draft proposal is accepted by the target verifier. As with the underlying DFlash runtime, output can differ from pure autoregressive generation near tied logits because MLX dispatch differs between single-token and block verification.
Source, reproducible benchmark command, and attribution: will702/dfly-mlx.
License
Apache-2.0. The original AngelSlim model license is included as License_AngelSlim_model_and_dataset.txt.
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