Visual Question Answering
Transformers
Safetensors
minicpmv
feature-extraction
custom_code
4-bit precision
bitsandbytes
Instructions to use openbmb/MiniCPM-Llama3-V-2_5-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM-Llama3-V-2_5-int4 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-Llama3-V-2_5-int4", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("openbmb/MiniCPM-Llama3-V-2_5-int4", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update tokenizer_config.json
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
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"input_ids",
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"attention_mask"
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],
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"model_max_length":
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"pad_token": "!",
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"padding_side": "right",
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"tokenizer_class": "PreTrainedTokenizerFastWrapper",
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"input_ids",
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"attention_mask"
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],
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "!",
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"padding_side": "right",
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"tokenizer_class": "PreTrainedTokenizerFastWrapper",
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