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AI 모델 85003

• VersaViT↗

  腾讯

• Sakura-Qwen-Image-2.1-Turbo-GGUF↗

  webmp3

• toxic-bert↗

  Xenova

  Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

• MiMo-Embodied-7B↗

  XiaomiMiMo

• Zarya-0.6B↗

  ai-forever

• flux-ghibsky-illustration↗

  aleksa-codes

• longformer-scico↗

  allenai

  This model is the `unified` model discussed in the paper SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts (AKBC 2021) that formulates the task of hierarchical cross-document coreference resolution (H-CDCR) as a multiclass problem. The model takes as input two mentions `m1` and `m2` with their corresponding context and outputs 4 scores: * 0: not related * 1: `m1` and `m2` corefer * 2: `m1` is a parent of `m2` * 3: `m1` is a child of `m2`. We provide the following code as an example to set the global attention on the special tokens: ` `, ` ` and ` `.

• Molmo2-8B↗

  allenai

  Molmo2 is a family of open vision-language models developed by the Allen Institute for AI (Ai2) that support image, video and multi-image understanding and grounding. Molmo2 models are trained on publicly available third party datasets as referenced in our technical report and Molmo2 data, a collection of datasets with highly-curated image-text and video-text pairs. It has state-of-the-art performance among multimodal models with a similar size. You can find all models in the Molmo2 family here. **Learn more** about the Molmo2 family in our announcement blog post. Molmo2-8B is based on Qwen3-8B and uses SigLIP 2 as vision backbone. It outperforms others in the class of open weight and data models on short videos, counting, and captioning, and is competitive on long-videos.

• coreml-sam2.1-large↗

  apple

• Ornith-1.5-35B-A3B-GGUF↗

  bartowski

  **Model details:** - Parameter count: 36B - Input support: text, image (with mmproj file) - details - Speculative decoding: yes (MTP) - details - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (21.86GB) - usually a good mix of size and performance. Download instructions available here The files marked `true` in the Split column above are stored as multiple parts in a folder. To download all the parts to a local folder, run: