Any-to-Any
Transformers
Safetensors
MLX
minicpmo
image-feature-extraction
minicpm-o
minicpm-v
multimodal
full-duplex
custom_code
4-bit precision
Instructions to use mlx-community/MiniCPM-o-4_5-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlx-community/MiniCPM-o-4_5-4bit with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mlx-community/MiniCPM-o-4_5-4bit", trust_remote_code=True, dtype="auto") - MLX
How to use mlx-community/MiniCPM-o-4_5-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir MiniCPM-o-4_5-4bit mlx-community/MiniCPM-o-4_5-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Xet hash:
- df5e97593d5b71795029c19c93124ed9c2a939faefe40e19d893e8e57496aafa
- Size of remote file:
- 782 MB
- SHA256:
- 94eec5685bbbdd2611070d789aa5e5beba9a8fcbd9ba6849762160a9813a0a1d
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