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AI 모델 84757

• borealis-open-1b↗

  NbAiLab

• vietnamese-llama2-7b-120GB-GGUF↗

  nold

• MiniCPM5-2B-DSpark-GGUF↗

  openbmb

  This model has no autonomous intent or legal personhood; its outputs are text generated from statistical patterns and may be inaccurate, biased, or offensive, and may be manipulated by carefully crafted prompts ("jailbreaks") into producing unintended content. Its responses on sensitive topics such as politics, health, finance, and law are not reviewed by experts and should not be treated as professional advice. This model is provided "**AS IS**", without warranty of any kind, express or implied, and the developers are not liable for any damages arising from its use. Users must employ the model only for lawful, compliant, and ethical purposes, configure their own safeguards, and label AI-generated content where required; deliberate jailbreaking, injection attacks, or inducing harmful output is prohibited, and any such testing is at the user's own risk.

• OpenMed-ZeroShot-NER-Anatomy-Medium-209M↗

  OpenMed

  **Specialized model for Anatomical Entity Recognition - Anatomical structures and body parts** Tailored to **anatomical structure recognition**, including organs, tissues, and substructures in clinical narratives.Supports **radiology and surgical note parsing**, **site-of-disease extraction**, and **anatomy-aware analytics**. 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.

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

  OpenMed

  Oncology-focused model for **cancer genetics**, capturing genes, variants, and cellular processes in tumor biology contexts.Useful for **cancer pathway curation**, **driver gene analysis**, and **precision oncology literature mining**. 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. Built on the robust GLiNER architecture and fine-tuned on curated biomedical corpora, OpenMed ZeroShot NER delivers high-precision entity recognition for critical healthcare and life sciences applications. Its zero-shot capability means you can flexibly define and extract any entity type relevant to your workflow, from standard biomedical categories to custom clinical concepts, supporting rapid adaptation to new research domains and regulatory requirements.

• tiny-random-minicpm3↗

  optimum-intel-internal-testing

• Qwen3-4B-ORA-W3↗

  oracomputing

• spotlight-vm↗

  percepta-ai

• Qwen3.6-27B-GGUF↗

  prithivMLmods

• SauerkrautLM-Nemo-12b-Instruct-GGUF↗

  QuantFactory