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AI 模型 84759

• DLER-R1-1.5B-Research↗

  NVIDIA

• Kumo-Forecast↗

  NVIDIA

• stt_hy_fastconformer_hybrid_large_pc↗

  NVIDIA

• PULI-GPT-2↗

  NYTK

• OpenMed-NER-ProteinDetect-SuperClinical-434M↗

  OpenMed

  **Specialized model for Biomedical Entity Recognition - Various biomedical entities** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for biomedical entity recognition - various biomedical entities. 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. - **High Precision**: Optimized for biomedical entity recognition - **Domain-Specific**: Trained on curated FSU dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem

• OpenMed-ZeroShot-NER-Genome-XLarge-770M↗

  OpenMed

  **Specialized model for Gene/Protein Entity Recognition - Gene and protein mentions** Accurate **gene/protein mention recognition**, including synonyms and symbol variants from biomedical literature.Enables **gene-centric curation**, **variant/association mining**, and **network construction**. 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.

• E1-600m↗

  Profluent-Bio

  This repository contains the model weights for the Profluent E1 family of models - our best in class single sequence and retrieval augmented protein representation models. They are designed to be drop-in replacement for ESM family of models. Please use them with code in https://github.com/Profluent-AI/E1 Your use of the Profluent-E1 model code is governed by the Apache License 2.0, while your use of the Profluent-E1 model weights and the full release of the Profluent-E1 model is governed by a similarly permissive license with additional attribution requirements - see the NOTICE file for details. You can use, share, and modify Profluent-E1 for free, but you must follow our ATTRIBUTION guidelines to give credit, include the license when you share and follow some other basic rules. Profluent is not responsible for what you build, and may terminate your rights to use Profluent-E1 if you breach the license.

• TinyLlama-1.1B-Chat-v1.0-GGUF↗

  QuantFactory

• Querit↗

  Querit

• GLM-5.2-FP8↗

  RedHatAI