Instructions to use ekryski/SmolLM2-360M-Instruct-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ekryski/SmolLM2-360M-Instruct-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir SmolLM2-360M-Instruct-4bit ekryski/SmolLM2-360M-Instruct-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
SmolLM2-360M-Instruct-4bit
4-bit affine quantization of HuggingFaceTB/SmolLM2-360M-Instruct, produced with FFAI 0.1.0's ffai convert (mlx-affine format, group_size=64).
Conversion
ffai convert HuggingFaceTB/SmolLM2-360M-Instruct --bits 4 \
--upload-repo ekryski/SmolLM2-360M-Instruct-4bit
See also
- FFAI — fast Apple Silicon LLM inference.
Model.load("ekryski/SmolLM2-360M-Instruct-4bit")runs this checkpoint end-to-end. - FFAI quickstart
- FFAI quantization docs
- Downloads last month
- 27
Model size
96.4M params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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4-bit
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Model tree for ekryski/SmolLM2-360M-Instruct-4bit
Base model
HuggingFaceTB/SmolLM2-360M Quantized
HuggingFaceTB/SmolLM2-360M-Instruct