全站搜索

AI 模型 84999

• 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:

• WhiteRabbitNeo-7B-v1.5a-GGUF↗

  bartowski

• WikiHow-Mistral-Instruct-7B-GGUF↗

  bartowski

• Bespoke-Nimble-9B-v3↗

  bespokelabs

• spot-wetfloor-convnext-tiny↗

  builtbyd3v

  Binary image classifier: `wet` (visible standing water or a wet surface) vs `dry`. Fine-tuned from facebook/convnext-tiny-224 (Apache-2.0). Built for SPOT Dispatch, a readiness board for Boston Dynamics Spot inspection routes. Spot's user guide says not to walk on wet or slippery surfaces, so the board can flag route photos that look wet for an operator to review. `onnx/model_quantized.onnx` (33.8 MB vs 111.4 MB fp32) loads with `{ dtype: "q8" }`. It is onnxruntime `quantize_dynamic`, uint8 weights, MatMul/Gemm layers only (convs stay fp32). Scored with onnxruntime on the same test split: q8 misses one more same-source wet image and changes 1 of 752 test predictions vs fp32 (kit images: same calls). Use fp32 when recall matters more than load time. Quantizing the convs too (28 MB) lost 2 same-source wet images, so it is not shipped.