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AI 模型 84760

• tiny-random-kimi-k3-mxfp4↗

  nikita-savelyev-cerebras

• InternVL2_5-1B-MPO↗

  OpenGVLab

• OpenMed-NER-ChemicalDetect-SuperClinical-184M↗

  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

• qwen3-forced-aligner-0.6b-q4-k-m↗

  OpenVoiceOS

• tiny-random-BeitForImageClassification↗

  optimum-intel-internal-testing

• tiny-random-xverse↗

  optimum-intel-internal-testing

• tiny-random-stable-diffusion-3↗

  optimum-internal-testing

  This is the model card of a 🧨 diffusers 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).

• Llama-3-8b-Ita-GGUF↗

  QuantFactory

• lewm-reacher↗

  quentinll

• Qwen3 1.7B (Non-reasoning)↗

  阿里巴巴 · Qwen