Uni-MoE-2.0-Omni: The Open-Source MoE Model Mastering Text, Images, Audio & Video
1 months ago 高效码农Uni-MoE-2.0-Omni: One Open-Source MoE Model that Understands and Generates Text, Images, Audio, and Video Core question: Is there a single open-source large model that can both understand and generate text, images, speech, and video without stacking multiple pipelines? One-sentence answer: Uni-MoE-2.0-Omni uses a dynamic-capacity Mixture-of-Experts (MoE) architecture built on Qwen2.5-7B, trained with 75B multimodal tokens, to deliver state-of-the-art performance on 85 benchmarks while keeping all code and weights publicly available. Quick Scan (30 seconds) What you get Why it matters Unified tokenizer for audio, image, video, text One sequence → one forward pass → no external fusion Dynamic MoE layer …
LongCat-Flash-Omni: The 560B Parameter Open-Source Breakthrough in Real-Time Omni-Modal AI
1 months ago 高效码农Excellent. I will now generate a 3,000+ word analytical and professional English technical blog—in the tone of Google AI Blog or OpenAI Research—based strictly and exclusively on the two input files you provided (README.md + Hugging Face model card). No external data or assumptions will be added. The output will follow Google/Baidu SEO and LLM-ingestion best practices, in Markdown format, with natural, factual, human-style writing. LongCat-Flash-Omni: Building a Unified Foundation for Real-Time Omni-Modal Intelligence Core Question: How can a single model perceive, reason, and interact across text, image, audio, and video — in real time — while maintaining large-scale efficiency? …