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AI Models 84759

• esm1v_t33_650M_UR90S_3↗

  facebook

• maskformer-swin-tiny-coco↗

  facebook

  MaskFormer model trained on COCO panoptic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository. Disclaimer: The team releasing MaskFormer did not write a model card for this model so this model card has been written by the Hugging Face team. MaskFormer 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.

• mms-tts-kyu↗

  facebook

• mms-tts-pad↗

  facebook

• mms-tts-pww↗

  facebook

• Qwen3.8-Flash-Next-NVFP4-capy-flash↗

  Feyd89

• certified-scorpion-topo-cbp-qwen-master-GGUF↗

  frankmorales2020

• Qwen3-8B-f16↗

  geoffmunn

• Qwen3.8-27B-NVFP4-RTX5090-LMHead4↗

  gittensor-model-hub

  > ## ⚠️ Superseded > > The main checkpoint now has the NVFP4 `lm_head` **and** the MTP head removed — it is strictly better than this variant (17.92 GB, 2 shards). > > **Use `Qwen3.8-27B-NVFP4-RTX5090` instead.** This repo stays up so existing pins keep working. `Qwen3.8-27B-NVFP4-RTX5090` with **`lm_head` quantized to NVFP4** instead of left in BF16. Every other tensor is bit-identical to the parent. At concurrency 1 this model is **weight-bandwidth bound, not compute bound**. Measured on the parent: 18.80 GiB of resident weights at 81.6 tok/s implies a **1.65 TB/s** read rate against the RTX 5090's 1.79 TB/s spec — about **92% of peak**. Decode is streaming the whole model once per token.

• paligemma-3b-ft-screen2words-448-jax↗

  Google