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:
# pip install -U transformers accelerate # 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
Commit History
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Enhance model card with detailed information and `library_name: transformers`
Verified signatureThis PR significantly enhances the model card for UniMedVL by incorporating comprehensive details from the official GitHub repository. Key improvements include: - Addition of `library_name: transformers` to the metadata, enabling the automated "how to use" widget on the Hugging Face Hub due to the model's compatibility with the Transformers library (as evidenced by `llm_config.json`). - Expansion of the content to include an in-depth introduction, methodology, open-source plan, qualitative and quantitative results, and detailed "Getting Started" instructions, providing users with a richer understanding of the model's capabilities and usage. - Integration of the paper's abstract to offer a concise summary. - Updated and consolidated author information, linking to Hugging Face profiles where available. These changes aim to make the UniMedVL model card more informative and user-friendly.ab056b9
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