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AI Models 84755

• 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.

• pig-vae↗

  calcuis

• cisimcik-base↗

  cisimcik

• gmma-jepa↗

  clevrpwn

• deberta-v3-base-rte↗

  cliang1453