Any-to-Any
Transformers
English
Chinese
qwen2
text-generation
medical
vision-language
multimodal
unified-model
medical-vqa
text-to-image
image-to-text
medical-understanding
report-generation
interleaved-multimodal
modality-transfer
custom_code
Instructions to use General-Medical-AI/UniMedVL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use General-Medical-AI/UniMedVL with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("General-Medical-AI/UniMedVL", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("General-Medical-AI/UniMedVL", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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### Model Description
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UniMedVL unifies medical multimodal understanding and generation within a single 14B-parameter architecture. The model supports visual question answering, medical report generation, text-to-medical-image synthesis, cross-modal translation, and virtual staining across 9 imaging modalities (CXR, CT, MRI, Ultrasound, Histopathology, Retinal Fundus, OCT, Endoscopy
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- **License:** Apache License 2.0
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- **Model Size:** 14B parameters
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### Model Description
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UniMedVL unifies medical multimodal understanding and generation within a single 14B-parameter architecture. The model supports visual question answering, medical report generation, text-to-medical-image synthesis, cross-modal translation, and virtual staining across 9 imaging modalities (CXR, CT, MRI, Ultrasound, Histopathology, Retinal Fundus, OCT, Endoscopy).
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- **License:** Apache License 2.0
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- **Model Size:** 14B parameters
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