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
metadata
license: apache-2.0
base_model: mlx-community/FastVLM-0.5B-bf16
language:
- en
tags:
- mlx
- ffai
- quantized
- 4bit
- affine
FastVLM-0.5B-4bit
4-bit affine quantization of mlx-community/FastVLM-0.5B-bf16, produced with FFAI 0.1.0's ffai convert (mlx-affine format, group_size=64).
Conversion
ffai convert mlx-community/FastVLM-0.5B-bf16 --bits 4 \
--upload-repo ekryski/FastVLM-0.5B-4bit
See also
- FFAI — fast Apple Silicon LLM inference.
Model.load("ekryski/FastVLM-0.5B-4bit")runs this checkpoint end-to-end. - FFAI quickstart
- FFAI quantization docs