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 # 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-2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("openbmb/MiniCPM-V-2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from openbmb/MiniCPM-V-2: direct link, hf CLI and curl.
- Browser
- Download file 1.99 MB
-
https://huggingface.co/openbmb/MiniCPM-V-2/resolve/refs%2Fpr%2F13/tokenizer.model
- Command line
-
hf download hf://openbmb/MiniCPM-V-2@refs/pr/13/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/openbmb/MiniCPM-V-2/resolve/refs%2Fpr%2F13/tokenizer.model
1.99 MB
- Xet hash:
- f102cc5b0f2453d020f17b2ab8ea20692b47132a077f9008357f24446b53c19c
- Size of remote file:
- 1.99 MB
- SHA256:
- b046f377b2f09a90623a46611bd5d23cb76ac89ca7804a372408f37aff7b96a0
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