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AI 模型 84759

• OpenMed-NER-ProteinDetect-BigMed-560M↗

  OpenMed

  **Specialized model for Biomedical Entity Recognition - Various biomedical entities** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for biomedical entity recognition - various biomedical entities. 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 FSU dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem

• OpenMed-PII-EuroMed-Large-210M-v1↗

  OpenMed

  **OpenMed-PII-EuroMed-210M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection** in text. This model identifies and classifies **54 types of sensitive information** including names, addresses, SSNs, medical record numbers, and more. ⚠️ **Important**: This model is intended as an **assistive tool**, not a replacement for human review.

• DeepSeek-V4.1-Flash-MTPLX-streaming-q2↗

  OpensourceWTF

• Z-Image-De-Turbo↗

  ostris

  This is a de-distilled version of Tongyi-MAI/Z-Image-Turbo. It was fine tuned on images generated by Tongyi-MAI/Z-Image-Turbo in order to break down the turbo distillation. It can be used to train LoRAs on top of that should remain compatability with the base model, or it can be continued to finetune well beyond what you could do with the turbo model with the ostris/zimage_turbo_training_adapter. This can be trained on directly without an adapter. It can also be used for inference directly. For inference, use a low CFG (2.0 - 3.0) and 20-30 steps. It seems to work well with CFG normalization as well.

• PP-OCRv5_server_det↗

  PaddlePaddle

  PP-OCRv5_server_det is one of the PP-OCRv5_det series, the latest generation of text detection models developed by the PaddleOCR team. Designed for high-performance applications, it supports the detection of text in diverse scenarios—including handwriting, vertical, rotated, and curved text—across multiple languages such as Simplified Chinese, Traditional Chinese, English, and Japanese. Key features include robust handling of complex layouts, varying text sizes, and challenging backgrounds, making it suitable for practical applications like document analysis, license plate recognition, and scene text detection. The key accuracy metrics are as follow: For details about PaddlePaddle installation, please refer to the PaddlePaddle official website. You can also integrate the model inference of the text detection module into your project. Before running the following code, please download the sample image to your local machine.

• Qwen3-0.6B↗

  PrimeIntellect

  This is a clone of `Qwen/Qwen3-0.6B` with a multi-turn, tool-call compatible chat template.

• Qwen3.6-35B-A3B-abliterated-MAX-F32-GGUF↗

  prithivMLmods

• karakuri-lm-7b-apm-v0.2-GGUF↗

  QuantFactory

• Qwen2-VL-2B-Instruct-GPTQ-Int8↗

  阿里巴巴 · Qwen

• curia↗

  raidium