Any-to-Any
Transformers
ONNX
Safetensors
minicpmo
feature-extraction
minicpm-o
minicpm-v
multimodal
full-duplex
custom_code
Eval Results
Instructions to use openbmb/MiniCPM-o-4_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM-o-4_5 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("openbmb/MiniCPM-o-4_5", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add OmniCompass evaluation results
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by VCLab-PolyU - opened
.eval_results/omnicompass.yaml
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- dataset:
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id: VCLab-PolyU/OmniCompass
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task_id: OmniCompass-AV
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value: 17.5
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source:
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url: https://omnicompass.netlify.app/#leaderboard
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name: OmniCompass official results
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user: VCLab-PolyU
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notes: OmniCompass-AV; Overall CS on a 0-100 scale, higher is better. 32 video frames;
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paired Text and Text+Image; TG, SG and QA. Imported from the benchmark authors'
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published results, rounded to one decimal. Evaluation settings and model-specific
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limits follow the cited source. This submission does not claim HF verified status.
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