Instructions to use nicolasramos/Qwen3.8-9B-Distill-MLX-4bit-mtp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use nicolasramos/Qwen3.8-9B-Distill-MLX-4bit-mtp with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3.8-9B-Distill-MLX-4bit-mtp nicolasramos/Qwen3.8-9B-Distill-MLX-4bit-mtp
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Qwen3.8-9B-Distill-MLX-4bit-mtp
MLX conversion of empero-ai/Qwen3.8-9B-Distill (Qwen3.5-9B distilled from Qwen3.8, Apache-2.0) with the native Qwen3.5 MTP head embedded.
MTP
This checkpoint carries the model's built-in Multi-Token Prediction head
(15 tensors language_model.mtp.*, 1 layer, shared embeddings) in
model-mtp.safetensors. The config.json declares
mtp_num_hidden_layers: 1 (inherited from the Qwen3.5 base).
Default mlx-lm converters strip mtp.* weights during sanitize(); this
checkpoint was produced with a converter that preserves them, so the
oMLX Lightning MTP toggle works out of the box.
Usage
With oMLX (0.6.x): enable Lightning MTP in the model settings
(mtp_enabled: true). Requires mlx-lm 0.31.3+.
import mlx_lm
model, tokenizer = mlx_lm.load("nicolasramos/Qwen3.8-9B-Distill-MLX-4bit-mtp")
4bit quantization: group_size 64, affine. The MTP fusion projection
(mtp.fc) is kept in full precision.
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