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AI 모델 84760

• GuardReasoner-VL-7B↗

  yueliu1999

• GLM-4.5-Base↗

  zai-org

• RealVideo↗

  zai-org

• Ternary-Bonsai-2-27B-DFlash2-mlx-4bit↗

  zerodegress

• AstolfoMix↗

  6DammK9

• SmolLM2-360M-Instruct-GGUF↗

  Alcoft

• multilingual-e5-large-nep-16384↗

  alphaedge-ai

• lightning-105m↗

  Aobangaming

• DeepSeek-V4.1-Flash-MixedQ2-GGUF↗

  apetersson

  Routed-expert requantisation of `deepseek-ai/DeepSeek-V4.1-Flash` revision `2bc89ac599031fa673cab993f1df02fc4a98c673`, produced on a 32-vCPU Runpod CPU pod with the pinned llama.cpp ggml reference quantisers (PR `ggml-org/llama.cpp#28696` head `cd628010bc3fc0a787d156c969d52a0789451c96`). Tensor names follow that PR's `deepseek4` table. This is a **weights artifact**. No runtime has yet been shown to execute it end to end; the "Runtime status" and "Smoke-test findings" sections say exactly what has been measured on which hardware, and what is still blocked. `SHA256SUMS` covers the five shards. The pack is deterministic: two independent runs produced byte-identical shard digests.

• deformable-detr-DocLayNet↗

  Aryn

  Deformable DEtection TRansformer (DETR), trained on DocLayNet (including 80k annotated pages in 11 classes). The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.