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 README.md
Browse files
README.md
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@@ -38,7 +38,7 @@ res = model.chat(
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msgs=msgs,
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tokenizer=tokenizer,
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sampling=True, # if sampling=False, beam_search will be used by default
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temperature=0.7
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# system_prompt='' # pass system_prompt if needed
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)
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print(res)
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msgs=msgs,
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tokenizer=tokenizer,
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sampling=True, # if sampling=False, beam_search will be used by default
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temperature=0.7,
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# system_prompt='' # pass system_prompt if needed
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)
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print(res)
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