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AI Models 2119
- GPT-6 Luna Pro (batch)↗
OpenAI
GPT-6 Luna Pro is the same underlying model as GPT-6 Luna, served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks. **Cost note:** pro mode spends far more...
- GPT-6.1 Sol Pro↗
OpenAI
GPT-6.1 Sol Pro is the same underlying model as GPT-6.1 Sol, served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks. **Cost note:** pro mode spends far more...
- GPT-6.1 Sol Pro (batch)↗
OpenAI
GPT-6.1 Sol Pro is the same underlying model as GPT-6.1 Sol, served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks. **Cost note:** pro mode spends far more...
- GPT-6.1 Terra Pro↗
OpenAI
GPT-6.1 Terra Pro is the same underlying model as GPT-6.1 Terra, served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks. **Cost note:** pro mode spends far more...
- GPT-6.1 Terra Pro (batch)↗
OpenAI
GPT-6.1 Terra Pro is the same underlying model as GPT-6.1 Terra, served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks. **Cost note:** pro mode spends far more...
- comfy-workflows↗
javawock7618
Anima, Krea-2, Qwen-Image 2.1, MOSS-Image & YuE2 INT8 Workflows Collection
- OmniUE-7B↗
Sony
OmniUE is an instruction-conditioned general-purpose embedder that can map text, images, videos, and audio into a 3,584-dimensional embedding space. It also supports visual interaction through bounding boxes or points, as well as audio interaction through time spans.
- 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.
- samantha-33b-i1-GGUF↗
mradermacher
- Guardian-Samantha-7b-slerp-GGUF↗
mradermacher