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
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## MiniCPM-Llama3-V 2.5 int4
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This is the int4 quantized version of [MiniCPM-Llama3-V 2.5](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5).
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Running with int4 version would use lower GPU
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## Usage
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## MiniCPM-Llama3-V 2.5 int4
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This is the int4 quantized version of [MiniCPM-Llama3-V 2.5](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5).
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Running with int4 version would use lower GPU memory (about 9GB).
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## Usage
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