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AI 모델 84760

• segformer-b3-finetuned-ade-512-512-ONNX↗

  onnx-community

• InternVL2_5-26B-AWQ↗

  OpenGVLab

• OpenMed-NER-ChemicalDetect-ModernClinical-149M↗

  OpenMed

  **Specialized model for Chemical Entity Recognition - Identifies chemical compounds and substances in biomedical literature** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for chemical entity recognition - identifies chemical compounds and substances in biomedical literature. 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 BC4CHEMD dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem

• OpenMed-NER-OncologyDetect-MultiMed-335M↗

  OpenMed

  This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for cancer genetics - cancer-related genetic 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. The BioNLP 2013 CG (Cancer Genetics) corpus is a specialized dataset focusing on cancer genetics entities and gene regulation in oncology research. This corpus contains annotations for genes, proteins, and molecular processes specifically related to cancer biology and tumor genetics. Developed for the BioNLP Shared Task 2013, it supports the development of text mining systems for cancer research, oncological studies, and precision medicine applications. The dataset is particularly valuable for identifying cancer-related biomarkers, tumor suppressor genes, oncogenes, and therapeutic targets mentioned in cancer research literature. It serves as a benchmark for evaluating NER systems used in cancer genomics, personalized medicine, and oncology informatics. *Figure: OpenMed (Open-Source) vs. Latest SOTA (Closed-Source) performance comparison across biomedical NER datasets.*

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

  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

• Qwen2.5-VL-72B-Instruct-Pointer-AWQ↗

  PointerHQ

• MiMo-V2.6-Distill-Qwen-9B-MLX↗

  prithivMLmods

• japanese-hubert-base-phoneme-ctc-v4↗

  prj-beatrice

• Ternary-Bonsai-2-27B-MTP↗

  ProCreations

  Updated September 18, 2026 with the best verified continued-training checkpoint: **r3-mtp**. The original Ternary Bonsai 2 27B PQ2_0 target is unchanged. The head is available as BF16 master weights and **integer Q8_0** inference weights, not FP8. **The +1.26% below is the extra gain over our previous trained MTP head, not the total gain from enabling MTP.** For an independent MTP-on versus MTP-off comparison, see the community reproduction below. Matched testing against the previous release measured **167.14 → 169.25 decode tokens/sec (+1.26%)** on eight unseen prompts with two repeats. This is a small measured gain, not a large speedup. Both arms used the same runtime and maximum 2 draft tokens.

• styx-100m↗

  PrometheanStudio