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
File size: 579 Bytes
3ce0a28 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"image_processor_type": "MiniCPMVImageProcessor",
"auto_map": {
"AutoProcessor": "processing_minicpmv.MiniCPMVProcessor",
"AutoImageProcessor": "image_processing_minicpmv.MiniCPMVImageProcessor"
},
"processor_class": "MiniCPMVProcessor",
"max_slice_nums": 9,
"scale_resolution": 448,
"patch_size": 14,
"image_feature_size": 64,
"im_start": "<image>",
"im_end": "</image>",
"slice_start": "<slice>",
"slice_end": "</slice>",
"unk": "<unk>",
"norm_mean": [0.5, 0.5, 0.5],
"norm_std": [0.5, 0.5, 0.5]
} |