Instructions to use AdrienBrault/Nemotron-Cascade-2-30B-A3B-5bit-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AdrienBrault/Nemotron-Cascade-2-30B-A3B-5bit-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Nemotron-Cascade-2-30B-A3B-5bit-MLX AdrienBrault/Nemotron-Cascade-2-30B-A3B-5bit-MLX
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Nemotron-Cascade-2-30B-A3B — 5-bit MLX
MLX quantization of nvidia/Nemotron-Cascade-2-30B-A3B.
- Architecture: Hybrid Attention + Mamba (SSM) + MoE — 30B total parameters, 3B active
- Quantization: Uniform 5-bit, group size 64
- Size: ~21.7 GB
Quantization command
uvx --from "mlx-lm[train]" mlx_lm.convert \
--hf-path nvidia/Nemotron-Cascade-2-30B-A3B \
--mlx-path nemotron-cascade-30b-5bit \
--quantize \
--q-bits 5 \
--q-group-size 64
Usage
uvx --from mlx-lm mlx_lm.chat \
--model AdrienBrault/Nemotron-Cascade-2-30B-A3B-5bit-MLX
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Model size
32B params
Tensor type
BF16
·
U32 ·
F32 ·
Hardware compatibility
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5-bit
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Model tree for AdrienBrault/Nemotron-Cascade-2-30B-A3B-5bit-MLX
Base model
nvidia/Nemotron-Cascade-2-30B-A3B