Instructions to use ekryski/Nemotron-H-4B-Base-8K-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ekryski/Nemotron-H-4B-Base-8K-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Nemotron-H-4B-Base-8K-4bit ekryski/Nemotron-H-4B-Base-8K-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Nemotron-H-4B-Base-8K-4bit
4-bit affine quantization of nvidia/Nemotron-H-4B-Base-8K, produced with FFAI 0.1.0's ffai convert (mlx-affine format, group_size=64).
Conversion
ffai convert nvidia/Nemotron-H-4B-Base-8K --bits 4 \
--upload-repo ekryski/Nemotron-H-4B-Base-8K-4bit
See also
- FFAI — fast Apple Silicon LLM inference.
Model.load("ekryski/Nemotron-H-4B-Base-8K-4bit")runs this checkpoint end-to-end. - FFAI quickstart
- FFAI quantization docs
- Downloads last month
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Model size
1B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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4-bit
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Model tree for ekryski/Nemotron-H-4B-Base-8K-4bit
Base model
nvidia/Nemotron-H-4B-Base-8K