Visual Question Answering
Transformers
Safetensors
English
Chinese
minicpmv
image-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
- Xet hash:
- f102cc5b0f2453d020f17b2ab8ea20692b47132a077f9008357f24446b53c19c
- Size of remote file:
- 1.99 MB
- SHA256:
- b046f377b2f09a90623a46611bd5d23cb76ac89ca7804a372408f37aff7b96a0
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