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
Update tokenizer_config.json
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
CHANGED
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@@ -147,7 +147,7 @@
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"clean_up_tokenization_spaces": false,
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"eos_token": "</s>",
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"legacy": true,
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"model_max_length":
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"pad_token": "<unk>",
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"padding_side": "right",
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"sp_model_kwargs": {},
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| 147 |
"clean_up_tokenization_spaces": false,
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| 148 |
"eos_token": "</s>",
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| 149 |
"legacy": true,
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+
"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<unk>",
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"padding_side": "right",
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"sp_model_kwargs": {},
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