Instructions to use ekryski/FastVLM-0.5B-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ekryski/FastVLM-0.5B-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir FastVLM-0.5B-4bit ekryski/FastVLM-0.5B-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
Upload README.md with huggingface_hub
Browse files
README.md
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---
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license: apache-2.0
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base_model: mlx-community/FastVLM-0.5B-bf16
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language:
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- en
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tags:
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- mlx
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- ffai
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- quantized
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- 4bit
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- affine
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---
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# FastVLM-0.5B-4bit
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4-bit affine quantization of [mlx-community/FastVLM-0.5B-bf16](https://huggingface.co/mlx-community/FastVLM-0.5B-bf16), produced with [FFAI](https://github.com/thewafflehaus/FFAI) 0.1.0's `ffai convert` (mlx-affine format, `group_size=64`).
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## Conversion
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```bash
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ffai convert mlx-community/FastVLM-0.5B-bf16 --bits 4 \
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--upload-repo ekryski/FastVLM-0.5B-4bit
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```
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## See also
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- [FFAI](https://github.com/thewafflehaus/FFAI) — fast Apple Silicon LLM inference. `Model.load("ekryski/FastVLM-0.5B-4bit")` runs this checkpoint end-to-end.
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- [FFAI quickstart](https://github.com/thewafflehaus/FFAI/blob/main/documentation/quickstart.md)
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- [FFAI quantization docs](https://github.com/thewafflehaus/FFAI/blob/main/documentation/quantization.md)
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