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AI 模型 84759

• gemma-1.1-2b-it-GGUF↗

  bartowski

• MathCoder2-Mistral-7B-GGUF↗

  bartowski

• MN-12B-Lyra-v4-GGUF↗

  bartowski

• UI-Venus-2-9B-GGUF↗

  bartowski

  **Model details:** - Parameter count: 9B - Input support: text, image (with mmproj file) - details - Speculative decoding: no - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (5.91GB) - usually a good mix of size and performance. Download instructions available here These quants were made with llama.cpp release b10630 - if this model's architecture is newly supported, you'll need that release or newer to run them.

• GLM-5.3-Flash-Derisked-BF16↗

  Blackfrost-AI

• qwen2.5-1.5b-instruct-alpaca-id-grpo↗

  blue-valentine13

• MegaDescriptor-T-224↗

  BVRA

  A Swin-T image feature model. Superwisely pre-trained on animal re-identification datasets. - **Model Type:** Animal re-identification / feature backbone - **Model Stats:** - Params (M): 28.3 - Image size: 224 x 224 - Architecture: swin_tiny_patch4_window7_224 - **Paper:** WildlifeDatasets_An_Open-Source_Toolkit_for_Animal_Re-Identification - **Related Papers:** - Swin Transformer: Hierarchical Vision Transformer using Shifted Windows - DINOv2: Learning Robust Visual Features without Supervision - **Pretrain Dataset:** All available re-identification datasets --> https://github.com/WildlifeDatasets/wildlife-datasets

• flux2-klein-4B-uncensored-text-encoder↗

  Cordux

• Ministral-3-14B-Instruct-2512-AWQ-4bit↗

  cyankiwi

• shisa-v2.1-llama3.2-3b-UD-japanese-imatrix↗

  dahara1