Instructions to use openbmb/MiniCPM-V with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM-V with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "visual-question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("visual-question-answering", model="openbmb/MiniCPM-V", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("openbmb/MiniCPM-V", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## MiniCPM-V
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**MiniCPM-V** (i.e., OmniLMM-3B)is an efficient version with promising performance for deployment. The model is built based on MiniCPM-
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- 🚀 **High Efficiency.**
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## MiniCPM-V
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**MiniCPM-V** (i.e., OmniLMM-3B)is an efficient version with promising performance for deployment. The model is built based on [MiniCPM-2B](https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16) and SigLip-400M, connected by a perceiver resampler. Notable features of MiniCPM-V include:
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- 🚀 **High Efficiency.**
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