Cosmos-Reason1 is a physics AI model developed by NVIDIA, capable of understanding physical common sense and generating embodied decision-making natural language through long-chain reasoning.

MultimodalTransformersEnglish

This model is a fine-tuned version of Qwen2.5-7B-Instruct on the mlfoundations-dev/a1_science_stackexchange_physics_3k dataset, specializing in physics-related question-answering tasks.

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 high-performance pre-trained world foundation model designed for physical AI development, capable of generating physics-aware images, videos, and world states.

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

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 physics AI.

MultimodalDiffusers

General-Reasoner is a large language model training paradigm aimed at comprehensively enhancing the model's reasoning ability in different fields, covering multiple fields such as mathematics, programming, physics, chemistry, finance, and humanities. This model is trained based on Qwen2.5 - 14B, and improves cross - domain reasoning performance through diverse reasoning data and a model - based validator.

Natural Language ProcessingTransformersEnglish

A sequence-to-sequence Transformer model based on the BART architecture, specifically designed for generating Lagrangians in particle physics.

Scientific ComputingTransformers

A multimodal world foundation model based on diffusion architecture developed by NVIDIA, capable of generating high-quality physics-aware videos from text inputs

MultimodalDiffusers

PhysBERT is a text embedding model specifically designed for physics, trained on 1.2 million physics papers, outperforming general models on physics-specific tasks.

Natural Language ProcessingTransformers

Einstein-v7-Qwen2-7B is a text generation model obtained by full fine-tuning on Qwen/Qwen2-7B using various scientific field datasets. It performs excellently in multiple fields such as science, physics, chemistry, biology, and mathematics.

Natural Language ProcessingTransformersEnglish

CosmicBun-8B is a merged model based on the Llama3-8B architecture, specializing in text generation tasks for scientific fields such as mathematics, physics, chemistry, and biology.

Natural Language ProcessingTransformers

A physics-domain QA model based on the Phi-2 architecture, specifically fine-tuned for arXiv physics paper content

Natural Language ProcessingTransformersEnglish

Saily 220B is a powerful AI model built by merging based on Llama2-70B. It has been fine-tuned on multiple specific domain datasets and has capabilities in multiple aspects such as code, humor, mathematics, logical understanding, physics, reasoning, psychology, and role-playing.

Natural Language ProcessingTransformersEnglish