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 from transformers import pipeline pipe = pipeline("visual-question-answering", model="openbmb/MiniCPM-Llama3-V-2_5-int4", trust_remote_code=True)# 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
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README.md
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pipeline_tag: visual-question-answering
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---
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## MiniCPM-Llama3-V 2.5 int4
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for new_text in res:
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generated_text += new_text
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print(new_text, flush=True, end='')
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```
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---
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pipeline_tag: visual-question-answering
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base_model:
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- openbmb/MiniCPM-Llama3-V-2_5
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---
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## MiniCPM-Llama3-V 2.5 int4
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for new_text in res:
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generated_text += new_text
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print(new_text, flush=True, end='')
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```
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