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AI Models 84759

• openbuddy-mistral-22b-v21.1-32k↗

  OpenBuddy

  We recommend using the fast tokenizer from `transformers`, which should be enabled by default in the `transformers` and `vllm` libraries. Other implementations including `sentencepiece` may not work as expected, especially for special tokens like ` `, ` ` and ` `. This format is also defined in `tokenizer_config.json`, which means you can directly use `vllm` to deploy an OpenAI-like API service. For more information, please refer to the vllm documentation. All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.

• OpenMed-NER-GenomeDetect-SuperClinical-141M↗

  OpenMed

  This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for gene/protein entity recognition - gene and protein mentions. 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. BC2GM corpus targets gene and protein mention recognition from the BioCreative II Gene Mention task. The BC2GM (BioCreative II Gene Mention) corpus is a foundational dataset for gene and protein name recognition in biomedical literature, created for the BioCreative II challenge. This corpus contains thousands of sentences from MEDLINE abstracts with manually annotated gene and protein mentions, serving as a critical benchmark for genomics and molecular biology NER systems. The dataset addresses the challenging task of identifying gene names, which often have complex nomenclature and ambiguous boundaries. It has been instrumental in advancing automated gene recognition systems used in functional genomics research, gene expression analysis, and molecular biology text mining. The corpus continues to be widely used for training and evaluating biomedical NER models.

• gemma-4-31B-it-qat-q4_0-uncensored-heretic-GGUF↗

  OS-Software

• qwen_image_edit_inpainting↗

  ostris

• turn-1-mini↗

  p99lab

• pansophic-1-preview-LLaMA3.1-8b-GGUF↗

  pansophic

• lion-warden-ai-security-classifier↗

  patronus-studio

• Jan-v2-VL-f32-AIO-GGUF↗

  prithivMLmods

• Video-ORA-9B-GGUF↗

  prithivMLmods

• ppt-pythia-1b-appendix-frequent-permuted-seed3408-stage2↗

  qing-yao