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AI Models 85003

• Florence-2-large-no-flash-attn↗

  multimodalart

  ⚠️ This is a modified version of Florence 2 that modifies the custom `modeling_florence2.py` file to remove the need for installing `flash-attn` package (by hijacking the flash-attn methods and replacing with regular attention). It probably has impact in performance. This Hub repository contains a HuggingFace's `transformers` implementation of Florence-2 model from Microsoft. Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model.

• Qwen3-Embedding-0.6B-ONNX↗

  n24q02m

• Solar-Open2-250B-Nota-INT4-GlobalPruned↗

  nota-ai

• Meta-Llama-3-70B↗

  NousResearch

• 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.