OpenMMReasoner is a fully transparent two-stage multimodal reasoning solution that covers supervised fine-tuning (SFT) and reinforcement learning (RL). By carefully constructing high-quality datasets, this solution surpasses powerful baseline models in multiple multimodal reasoning benchmark tests, laying a solid empirical foundation for future large-scale multimodal reasoning research.

MultimodalTransformers

SenseNova-SI is a series of spatial intelligence enhancement models built on multimodal foundation models. Through careful training with 8 million samples of data, it has achieved excellent performance in multiple spatial intelligence benchmark tests while maintaining strong general multimodal understanding capabilities.

MultimodalTransformersEnglish

SenseNova-SI is a series of spatial intelligence enhancement models built on mature multimodal foundation models. Through training with carefully curated 8 million data samples, it demonstrates excellent performance in multiple spatial intelligence benchmark tests while maintaining strong general multimodal understanding ability.

MultimodalTransformers

SenseNova-SI is a series of spatial intelligence models built on multimodal foundation models, specifically optimized for spatial understanding capabilities. Through large-scale spatial intelligence data training, it performs excellently in metric estimation, spatial relationship understanding, viewpoint change handling, etc.

MultimodalTransformers

WavJEPA is an audio foundation model based on the waveform joint embedding prediction architecture. It uses advanced semantic representation learning to address the deficiencies in speech unit or token-level representation learning. It significantly outperforms the state-of-the-art time-domain audio foundation models in many downstream benchmark tasks while requiring significantly less computational resources.

Audio ProcessingTransformers

Gelato-30B-A3B is a state-of-the-art foundation model for GUI computer usage tasks. It is trained on the Click-100k dataset and outperforms previous specialized computer foundation models and larger vision-language models in multiple benchmark tests.

MultimodalTransformersEnglish

LFM2-Audio-1.5B is the first end-to-end audio foundation model launched by Liquid AI, designed specifically for low-latency and real-time conversations. With only 1.5 billion parameters, this model can achieve seamless conversational interactions, and its capabilities can rival those of models with much larger parameter scales.

MultimodalSafetensorsEnglish

GLM-4.5V-AWQ-4bit is a quantized multimodal model built on Zhipu AI's next-generation flagship text foundation model. Optimized by AWQ-4bit quantization technology, it significantly reduces the computational resource requirements while maintaining excellent performance. This model achieves SOTA performance among models of the same scale in 42 public visual language benchmark tests and has powerful visual reasoning capabilities.

MultimodalTransformersMultiple Languages

DINOv3 is a series of general-purpose visual foundation models that can outperform specialized state-of-the-art techniques on a wide range of visual tasks without fine-tuning. This model can generate high-quality dense features and performs excellently in various visual tasks, significantly surpassing previous self-supervised and weakly-supervised foundation models.

Computer VisionTransformersEnglish

DINOv3 is a series of general visual foundation models developed by Meta AI. Without fine-tuning, it can outperform specialized state-of-the-art models in a wide range of visual tasks. This model uses self-supervised learning to generate high-quality dense features and performs excellently in various tasks such as image classification, segmentation, and depth estimation.

Computer VisionTransformersEnglish

DINOv3 is a versatile visual foundation model developed by Meta AI. It can outperform specialized models in a wide range of visual tasks without fine-tuning. This model can generate high-quality dense features and performs excellently in various visual tasks, significantly surpassing previous self-supervised and weakly supervised foundation models.

Computer VisionTransformersEnglish

DINOv3 is a series of general visual foundation models developed by Meta AI. It can outperform specialized advanced models in various visual tasks without fine-tuning. The model adopts the Vision Transformer architecture and is pre-trained on 1.689 billion web images. It can generate high-quality dense features and performs excellently in tasks such as image classification, segmentation, and retrieval.

Computer VisionTransformersEnglish

DINOv3 is a series of general-purpose visual foundation models that can outperform specialized state-of-the-art models on a wide range of visual tasks without fine-tuning. The model uses self-supervised learning to generate high-quality dense features and performs excellently in various visual tasks, significantly surpassing previous self-supervised and weakly supervised foundation models.

Computer VisionTransformersEnglish

DINOv3 is a series of general visual foundation models that can outperform specialized state-of-the-art techniques in a wide range of visual tasks without fine-tuning. The model generates high-quality dense features through self-supervised learning and performs excellently in various visual tasks, significantly surpassing previous self-supervised and weakly supervised foundation models.

Computer VisionTransformersEnglish

Chroma1-Base is a text-to-image basic model with 8.9 billion parameters, built on the FLUX.1-schnell architecture and licensed under the Apache 2.0 license. This model is designed for fine-tuning, providing a powerful and neutral foundation for developers, researchers, and artists to create specialized generative models.

MultimodalDiffusers

MMaDA is a novel class of multimodal diffusion foundation models, excelling in various domains such as text reasoning, multimodal understanding, and text-to-image generation.

MultimodalTransformers

Sundial is a series of generative time series foundation models capable of zero-shot inference for both deterministic and probabilistic forecasting.

Scientific ComputingSafetensors

A pre-trained language model that integrates multiple 70B-parameter-scale models, based on the 'Forgotten Safe Word' foundation model, featuring an unrestrained style and a tendency for novel/story writing.

Natural Language ProcessingTransformers

Cosmos-Predict2 is a series of high-performance pre-trained world foundation models designed to generate physics-aware images, videos, and world states, which can be used for the development of physical artificial intelligence.

MultimodalDiffusers

Cosmos-Predict2 is a series of high-performance pre-trained world foundation models designed for physical AI to generate physics-aware images, videos, and world states.

MultimodalDiffusers