Search FoxAI

AI Models 84760

• Zora-9B-v0-i1-GGUF↗

  mradermacher

• Qwen3.5-9B-SOMPOA-heresy↗

  MuXodious

• yigdzin1-gguf↗

  nakamura196

• food↗

  nateraw

• tinybrainbot-10m-base↗

  nkthebass

• Qwen3.5-4B-SoraFriendAI-GGUF↗

  nori-sdc

• bert-base-multilingual-cased-ner-hrl-ONNX↗

  onnx-community

• imagegpt-small↗

  OpenAI

  ImageGPT (iGPT) model pre-trained on ImageNet ILSVRC 2012 (14 million images, 21,843 classes) at resolution 32x32. It was introduced in the paper Generative Pretraining from Pixels by Chen et al. and first released in this repository. See also the official blog post. Disclaimer: The team releasing ImageGPT did not write a model card for this model so this model card has been written by the Hugging Face team. The ImageGPT (iGPT) is a transformer decoder model (GPT-like) pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-21k, at a resolution of 32x32 pixels.

• OpenMed-ZeroShot-NER-Disease-Base-220M↗

  OpenMed

  **Specialized model for Disease Entity Recognition - Disease entities from the BC5CDR dataset** Specialized for **disease and condition recognition** from biomedical texts, covering clinical disorders and pathological states.Supports **patient phenotyping**, **disease indexing**, **literature triage**, and **clinical evidence aggregation**. 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.

• GA-Guard-AIO-GGUF↗

  prithivMLmods