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AI Models 84759

  • UnslopNemo-12B-v3-i1-GGUF↗

    mradermacher

  • CustomerLM↗

    MultiSense

  • KVzap-mlp-Qwen3-32B↗

    NVIDIA

  • Llama3-70B-PPO-Chat↗

    NVIDIA

  • qwen3-4b-structured-output-merged-stage-a-OBLITERATED↗

    OBLITERATUS

    OBLITERATUS is an open-source tool for removing refusal behavior from language models via activation engineering (abliteration). Learn more at github.com/elder-plinius/OBLITERATUS.

  • parakeet-tdt-0.6b-v3-ultra-onnx↗

    Olicorne

  • OpenMed-NER-DNADetect-SuperMedical-355M↗

    OpenMed

    **Specialized model for Biomedical Entity Recognition - Proteins, DNA, RNA, cell lines, and cell types** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for biomedical entity recognition - proteins, dna, rna, cell lines, and cell types. 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 JNLPBA dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem

  • OpenMed-NER-PharmaDetect-BigMed-278M↗

    OpenMed

    **Specialized model for Chemical Entity Recognition - Chemical entities from the BC5CDR dataset** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for chemical entity recognition - chemical entities from the bc5cdr dataset. 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 BC5CDR_CHEM dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem

  • Qwen3-4B-abliterated-f32-GGUFs↗

    prithivMLmods

  • DialoGPT-medium-Loki-GGUF↗

    QuantFactory