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AI モデル 84759

• vit-gpt2-image-captioning↗

  nlpconnect

  This is an image captioning model trained by @ydshieh in flax this is pytorch version of this. * https://ankur3107.github.io/blogs/the-illustrated-image-captioning-using-transformers/ * https://huggingface.co/ankur310794 * https://twitter.com/ankur310794 * http://github.com/ankur3107 * https://www.linkedin.com/in/ankur310794

• NV-Raw2Insights-US↗

  NVIDIA

• Qwen3-VL-8B-ComfyUI↗

  nynxz

• cohere-transcribe-arabic-07-2026-dialectal-v2↗

  oddadmix

• OneJev1.6-4B↗

  OmniJev

• BiRefNet_lite-ONNX↗

  onnx-community

  Peng Zheng 1,4,5,6 ,  Dehong Gao 2 ,  Deng-Ping Fan 1* ,  Li Liu 3 ,  Jorma Laaksonen 4 ,  Wanli Ouyang 5 ,  Nicu Sebe 6 1 Nankai University  2 Northwestern Polytechnical University  3 National University of Defense Technology  4 Aalto University  5 Shanghai AI Laboratory  6 University of Trento  For more information, check out the official repository.

• RoLlama3-8b-Instruct↗

  OpenLLM-Ro

• OpenMed-ZeroShot-NER-Chemical-Tiny-60M↗

  OpenMed

  **Specialized model for Chemical Entity Recognition - Identifies chemical compounds and substances in biomedical literature** Purpose-built for **chemical entity recognition** in biomedical literature. It robustly identifies small molecules, drugs, reagents, and chemical synonyms across abstracts and full-text articles.Ideal for **drug discovery workflows**, **compound indexing**, **entity linking to ChEBI/DrugBank**, and **pharmacology literature curation**. OpenMed ZeroShot NER is an advanced, domain-adapted Named Entity Recognition (NER) model designed specifically for medical, biomedical, and clinical text mining. Leveraging state-of-the-art zero-shot learning, this model empowers researchers, clinicians, and data scientists to extract expert-level biomedical entities—such as diseases, chemicals, genes, species, and clinical findings—directly from unstructured text, without the need for task-specific retraining.

• ov-tiny-random-distilbert↗

  optimum-intel-internal-testing

  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Virchow↗

  paige-ai