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AI Models 85003

  • RoLlama3-8b-Instruct↗

    OpenLLM-Ro

  • OpenMed-ZeroShot-NER-Chemical-Tiny-60M↗

    OpenMed

    **Specialized model for Chemical Entity Recognition - Identifies chemical compounds and substances in biomedical literature** Purpose-built for **chemical entity recognition** in biomedical literature. It robustly identifies small molecules, drugs, reagents, and chemical synonyms across abstracts and full-text articles.Ideal for **drug discovery workflows**, **compound indexing**, **entity linking to ChEBI/DrugBank**, and **pharmacology literature curation**. 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.

  • ov-tiny-random-distilbert↗

    optimum-intel-internal-testing

    This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Virchow↗

    paige-ai

  • Bonsai-4B-mlx-1bit↗

    prism-ml

  • granite-3.0-8b-instruct-GGUF↗

    QuantFactory

  • Qwen2.5-Math-7B-PRM800K↗

    阿里巴巴 · Qwen

  • Qwen3 4B (Non-reasoning)↗

    阿里巴巴 · Qwen

  • Llama-3.1-8B-Instruct-NVFP4↗

    RedHatAI

    This model is a quantized version of Meta-Llama-3.1-8B-Instruct. It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model. This model was obtained by quantizing the weights and activations of Meta-Llama-3.1-8B-Instruct to FP4 data type, ready for inference with vLLM>=0.9.1 This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. Only the weights and activations of the linear operators within transformers blocks are quantized using LLM Compressor.

  • Qwen3.8-27B-speculator.dspark↗

    RedHatAI