Search FoxAI
AI Models 84757
- pythia-160m-seed2↗
EleutherAI
This model is part of the **PolyPythias** suite, an extension of the Pythia project providing 45 additional training runs across 5 model sizes with 9 different random seeds each. These models enable systematic study of training stability and reproducibility in language models. Oskar van der Wal, Pietro Lesci, Max Muller-Eberstein, Naomi Saphra, Hailey Schoelkopf, Willem Zuidema, and Stella Biderman. *ICLR 2025*. The decoupled seed variants (data-seed and weight-seed) allow researchers to separately study the effects of data ordering vs. weight initialization.
- mask2former-swin-large-mapillary-vistas-semantic↗
facebook
Mask2Former model trained on Mapillary Vistas semantic segmentation (large-sized version, Swin backbone). It was introduced in the paper Masked-attention Mask Transformer for Universal Image Segmentation and first released in this repository. Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, MaskFormer both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks.
- mms-tts-agd↗
facebook
- mms-tts-gau↗
facebook
- tts_transformer-es-css10↗
facebook
- embeddinggemma-2-GGUF↗
Fastiraz
- Minueza-32M-Base↗
Felladrin
- Qwen2.5-Coder-1.5B-Instruct-GGUF↗
gaianet
- pix2struct-textcaps-large↗
Google
- opus-mt-de-en↗
Helsinki-NLP
* source languages: de * target languages: en * OPUS readme: de-en * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: opus-2020-02-26.zip * test set translations: opus-2020-02-26.test.txt * test set scores: opus-2020-02-26.eval.txt