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AI Models 85001

• bpemb-attention↗

  deepparse

• MiniMax-H3-GGUF↗

  Derdisirtlan

• bitvoice-asr↗

  dhanr4j

• Qwen3-4B-Kimi2.5-Reasoning-Distilled-GGUF↗

  DripGhad1223

• 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