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AI 모델 84759

• zephyr-7b-hh-dpo-i1-GGUF↗

  mradermacher

• Cosmos-1.0-Autoregressive-5B-Video2World↗

  NVIDIA

• OpenMed-PII-ModernMed-Large-395M-v1↗

  OpenMed

  **OpenMed-PII-ModernMed-Large-395M-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.

• MyAwesomeModel-TestRepo↗

  OwenJago

• PaddleOCR-VL-1.5-GGUF↗

  PaddlePaddle

  PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing **🔥 Official Website** **📝 Technical Report** **PaddleOCR-VL-1.5 is an advanced next-generation model of PaddleOCR-VL, achieving a new state-of-the-art accuracy of 94.5% on OmniDocBench v1.5.** To rigorously evaluate robustness against real-world physical distortions—including scanning artifacts, skew, warping, screen photography, and illumination—we propose the Real5-OmniDocBench benchmark. Experimental results demonstrate that this enhanced model attains SOTA performance on the newly curated benchmark. Furthermore, we extend the model’s capabilities by incorporating seal recognition and text spotting tasks, while remaining a 0.9B ultra-compact VLM with high efficiency.

• bert-tiny↗

  phob0s

• dinov3-vitl-pretrain-lvd1689m↗

  PIA-SPACE-LAB

  DINOv3 is a family of versatile vision foundation models that outperforms the specialized state of the art across a broad range of settings, without fine-tuning. DINOv3 produces high-quality dense features that achieve outstanding performance on various vision tasks, significantly surpassing previous self- and weakly-supervised foundation models. These are Vision Transformer and ConvNeXt models trained following the method described in the DINOv3 paper. 12 models are provided: Each Transformer-based model takes an image as input and returns a class token, patch tokens (and register tokens). These models follow a ViT architecture, with a patch size of 16. For a 224x224 image, this results in 1 class token + 4 register tokens + 196 patch tokens = 201 tokens (for DINOv2 with registers this resulted in 1 + 4 + 256 = 261 tokens).

• KingNish-Llama3-8b-v0.2-GGUF↗

  QuantFactory

• nbeerbower_-_Lyra-Gutenberg-mistral-nemo-12B-gguf↗

  RichardErkhov

• nanbeige-4.1-3b-deepseek-distilled↗

  RoroMachine14