Visual Question Answering
Transformers
Safetensors
English
Chinese
minicpmv
feature-extraction
custom_code
Eval Results
Instructions to use openbmb/MiniCPM-V-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM-V-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="openbmb/MiniCPM-V-2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("openbmb/MiniCPM-V-2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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Please look at [GitHub](https://github.com/OpenBMB/
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## MiniCPM-V 1.0 <!-- omit in toc -->
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Please see the info about MiniCPM-V 1.0 [here](
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## License
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#### Model License
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print(res)
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```
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Please look at [GitHub](https://github.com/OpenBMB/MiniCPM-V) for more detail about usage.
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## MiniCPM-V 1.0 <!-- omit in toc -->
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Please see the info about MiniCPM-V 1.0 [here](https://huggingface.co/openbmb/MiniCPM-V).
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## License
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#### Model License
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