Search FoxAI

AI Models 84759

• Qwen3.5-9B-ultimate-irrefusable-heretic-GGUF↗

  mradermacher

• Qwen3-Grand-Horror-DARK-Child-1.7B-i1-GGUF↗

  mradermacher

• switchboard-router-32b-GGUF↗

  mradermacher

• Yislerp2-34B-i1-GGUF↗

  mradermacher

• LLM-as-Judge-LFM-GGUF↗

  NEWWWWWbie

• Llama Nemotron Super 49B v1.5 (Non-reasoning)↗

  NVIDIA

• InternVL3_5-38B-Pretrained↗

  OpenGVLab

• OpenMed-NER-AnatomyDetect-SnowMed-568M↗

  OpenMed

  This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for anatomical entity recognition - anatomical structures and body parts. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as **drug interaction detection**, **medication extraction from patient records**, **adverse event monitoring**, **literature mining for drug discovery**, and **biomedical knowledge graph construction** with **production-ready reliability** for clinical and research applications. Anatomy corpus focuses on anatomical entity recognition for medical terminology and healthcare applications. The Anatomy corpus is a specialized biomedical NER dataset designed for recognizing anatomical entities and medical terminology in clinical and biomedical texts. This corpus contains annotations for anatomical structures, body parts, organs, and physiological systems mentioned in medical literature. It is essential for developing clinical NLP systems, medical education tools, and healthcare informatics applications where accurate anatomical entity identification is crucial. The dataset supports the development of automated systems for medical coding, clinical decision support, and anatomical knowledge extraction from medical records and literature. It serves as a valuable resource for training NER models used in medical imaging, surgical planning, and clinical documentation.

• OpenMed-ZeroShot-NER-Chemical-Base-220M↗

  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.

• PP-OCRv6_medium_rec_onnx↗

  PaddlePaddle

  PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks **🔥 Official Website** **📝 Technical Report** PP-OCRv6 is a lightweight OCR system that combines architectural innovation with data-centric optimization. It redesigns the backbone, detection neck, and recognition neck around a unified MetaFormer-style building block with structural reparameterization. Three model tiers (medium, small, tiny) share the same block primitives, covering deployment scenarios from server to edge.