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AI 모델 84759

• 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.

• zero123-xl-diffusers↗

  ashawkey

  _Note: This section is originally taken from the Stable Diffusion v2 model card, but applies in the same way to Zero-1-to-3._ The model is intended for research purposes only. Possible research areas and tasks include: - Safe deployment of large-scale models. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models.

• dolphin-2.9.4-gemma2-2b-GGUF↗

  bartowski

• MN-12B-Celeste-V1.9-GGUF↗

  bartowski

  Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using. If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: