Any-to-Any
Transformers
Safetensors
MLX
minicpmo
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
- LM Studio
mlx-community/MiniCPM-o-4_5-4bit
This model was converted to MLX format from openbmb/MiniCPM-o-4_5 using mlx-vlm version 0.3.13.
Refer to the original model card for more details on the model.
Use with mlx
pip install -U mlx-vlm
python -m mlx_vlm.generate --model mlx-community/MiniCPM-o-4_5-4bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
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Model size
2B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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4-bit
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