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AI Models 84760

• gliner2-privacy-filter-PII-multi↗

  fastino

  **GLiNER2-PII** is a fine-tune of the GLiNER2 model (205M parameters) for detecting and masking personally identifiable information across **42 entity types** and **7 languages**. Trained entirely on a constraint-driven synthetic corpus of 4,910 annotated texts, it achieves the **highest span-level F1 (0.477)** on the SPY benchmark among four compared systems — including OpenAI Privacy Filter, NVIDIA GLiNER-PII, and urchade/gliner\_multi\_pii-v1. 📄 **Technical Report** 🔗 **GitHub**

• yolov8_animeface↗

  Fuyucchi

  Dataset was made created with 10 000 images manually annotated from safebooru. Split was train 70, val 20, test 10. This model is based on yolov8x6. It has been trained on the said dataset for 300 epoch at 1280px*1280px. It took ~110 hours to train on a RTX A4000. While it doesn't provide a huge mAP50-95, its predictions are always correct on the files I've tested. Confidence could be higher, but the model is very precise.

• Qwen2-VL-2B-Instruct-GGUF↗

  ggml-org

• augmented-b3e858bc6747b783↗

  gradients-io-tournaments

• opus-mt-tc-big-de-es↗

  Helsinki-NLP

• FLUX.2-klein-4B-bnb-4bit↗

  hlhc

• Hy4-preview-FP8↗

  腾讯

  - Model Introduction - A New Flagship Generation - Built for Productivity - Benchmark Appendix - Known Limitations - News - Model Links - Quickstart - Deployment - vLLM - SGLang - Finetuning - Quantization - License - Contact Us **Hy4 preview** is a new-generation Mixture-of-Experts (MoE) flagship model developed by the Tencent Hy Team. The model comprises 770B total parameters, of which 49B are activated per token. The backbone consists of 78 layers, where the first layer uses a standard dense FFN and the remaining 77 layers replace it with MoE, each containing 256 routed experts and 1 shared expert; every token activates the top-8 routed experts along with the shared expert. In addition to the backbone, 1 native MTP layer (10B total parameters, 0.7B activated) is built in for speculative decoding. On the architecture side, inspired by DeepSeek and GLM, the attention module employs Gated DeepSeek Sparse Attention (Gated DSA) with IndexCache for cross-layer sparse index reuse. The residual pathway uses iHC (identity Hyper-Connections) to expand inter-layer information flow.

• granite-3.0-3b-a800m-instruct↗

  ibm-granite

• ruaccent-coreml↗

  iliasaz

• Ling-3.0-flash-GGUF↗

  inclusionAI