ConTextTab is a deep learning model that combines semantic understanding and context learning, specifically designed to handle tabular data. It processes different data modalities through specialized embedding methods and is trained on large-scale real-world tabular data, performing excellently in multiple benchmark tests, especially setting a new standard in the semantically rich CARTE benchmark test.

SAP RPT 1 OSS is a deep learning model that combines semantic understanding and context learning, specifically designed for tabular data prediction tasks. The model uses specialized embeddings for different data modalities and is trained on large-scale real-world tabular data, performing excellently in a wide range of benchmark tests.

This is a Transformers model published on the Hugging Face Hub. Specific information needs to be supplemented by the model author. This model is based on the Transformer architecture and can be used for various natural language processing tasks.

Natural Language ProcessingTransformers

A comprehensive re-fusion and remastered 32k context large language model based on the Dark Sapling V1 7B model, with significant improvements in quantization level optimization and performance

Natural Language ProcessingGgufEnglish

Sapnous-6B is an advanced vision-language model that enhances perception and understanding of the world through powerful multimodal capabilities.

MultimodalTransformersEnglish

Minerva is an Italian-English bilingual large language model developed by Sapienza NLP, based on the Mistral architecture with 7 billion parameters, supporting instruction-following and dialogue tasks.

Natural Language ProcessingTransformersMultiple Languages

A Korean-optimized version based on Google's Gemma-2-9b-it model, developed by Saltware, suitable for Korean natural language processing tasks.

Natural Language ProcessingTransformersKorean

Sapiens is a family of vision Transformer models pre-trained on 300 million high-resolution human images, focusing on human-centric vision tasks.

Sapiens is a vision Transformer model pre-trained on 300 million high-resolution human images, specifically designed for pose estimation tasks, supporting 308 keypoint detection.

Sapiens is a vision Transformer model pre-trained on 300 million high-resolution human images, specifically designed for pose estimation tasks, supporting 308 keypoint detection.

FLUX.1-dev is an experimental text-to-image model supporting fp8 and float8_e4m3fn formats, specializing in realistic and hyper-realistic style image generation.

Computer VisionDiffusersEnglish

Sapiens is a series of vision transformer models pre-trained on 300 million human images at 1024x1024 resolution, focusing on human-centric vision tasks.

Sapiens is a vision Transformer model pre-trained on 300 million 1024x1024 resolution portrait images, focusing on human-centric vision tasks.

Sapiens-2B is a vision Transformer model pre-trained on 300 million high-resolution human images, specifically optimized for human depth estimation tasks, supporting 1K resolution inference with excellent generalization capabilities in real-world scenarios.

Sapiens is a family of Vision Transformer models pre-trained on 300 million 1024x1024 resolution human images, focusing on human-centric vision tasks.

Sapiens is a Vision Transformer model pre-trained on 300 million high-resolution human images, specializing in human-centric vision tasks

Sapiens is a vision transformer series model pre-trained on 300 million 1024x1024 resolution human images, focusing on human-centric vision tasks.

Sapiens is a vision Transformer model pre-trained on 300 million 1024×1024 resolution human images, supporting high-resolution inference and real-world scenario generalization.

Sapiens is a family of Vision Transformer models pre-trained on 300 million 1024x1024 resolution human images, supporting high-resolution inference and real-world scenario generalization.

Sapiens is a Vision Transformer model pre-trained on 300 million high-resolution human images, specializing in human-centric vision tasks.