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AI 모델 85001
- 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
- CodeLlama-13b-hf↗
codellama
- mT5_multilingual_XLSum↗
csebuetnlp
This repository contains the mT5 checkpoint finetuned on the 45 languages of XL-Sum dataset. For finetuning details and scripts, see the paper and the official repository. Language | ROUGE-1 / ROUGE-2 / ROUGE-L ---------|---------------------------- Amharic | 20.0485 / 7.4111 / 18.0753 Arabic | 34.9107 / 14.7937 / 29.1623 Azerbaijani | 21.4227 / 9.5214 / 19.3331 Bengali | 29.5653 / 12.1095 / 25.1315 Burmese | 15.9626 / 5.1477 / 14.1819 Chinese (Simplified) | 39.4071 / 17.7913 / 33.406 Chinese (Traditional) | 37.1866 / 17.1432 / 31.6184 English | 37.601 / 15.1536 / 29.8817 French | 35.3398 / 16.1739 / 28.2041 Gujarati | 21.9619 / 7.7417 / 19.86 Hausa | 39.4375 / 17.6786 / 31.6667 Hindi | 38.5882 / 16.8802 / 32.0132 Igbo | 31.6148 / 10.1605 / 24.5309 Indonesian | 37.0049 / 17.0181 / 30.7561 Japanese | 48.1544 / 23.8482 / 37.3636 Kirundi | 31.9907 / 14.3685 / 25.8305 Korean | 23.6745 / 11.4478 / 22.3619 Kyrgyz | 18.3751 / 7.9608 / 16.5033 Marathi | 22.0141 / 9.5439 / 19.9208 Nepali | 26.6547 / 10.2479 / 24.2847 Oromo | 18.7025 / 6.1694 / 16.1862 Pashto | 38.4743 / 15.5475 / 31.9065 Persian | 36.9425 / 16.1934 / 30.0701 Pidgin | 37.9574 / 15.1234 / 29.872 Portuguese | 37.1676 / 15.9022 / 28.5586 Punjabi | 30.6973 / 12.2058 / 25.515 Russian | 32.2164 / 13.6386 / 26.1689 Scottish Gaelic | 29.0231 / 10.9893 / 22.8814 Serbian (Cyrillic) | 23.7841 / 7.9816 / 20.1379 Serbian (Latin) | 21.6443 / 6.6573 / 18.2336 Sinhala | 27.2901 / 13.3815 / 23.4699 Somali | 31.5563 / 11.5818 / 24.2232 Spanish | 31.5071 / 11.8767 / 24.0746 Swahili | 37.6673 / 17.8534 / 30.9146 Tamil | 24.3326 / 11.0553 / 22.0741 Telugu | 19.8571 / 7.0337 / 17.6101 Thai | 37.3951 / 17.275 / 28.8796 Tigrinya | 25.321 / 8.0157 / 21.1729 Turkish | 32.9304 / 15.5709 / 29.2622 Ukrainian | 23.9908 / 10.1431 / 20.9199 Urdu | 39.5579 / 18.3733 / 32.8442 Uzbek | 16.8281 / 6.3406 / 15.4055 Vietnamese | 32.8826 / 16.2247 / 26.0844 Welsh | 32.6599 / 11.596 / 26.1164 Yoruba | 31.6595 / 11.6599 / 25.0898
- Qwen3.6-27B-NEO-CODE-Di-IMatrix-MAX-GGUF↗
DavidAU
- Qwen3.8-27B-Uncensored-UD3-GGUF↗
DavidrPatton