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Multi Model


Multi-model AI systems are incredibly versatile, seamlessly integrating diverse AI models to perform a wide array of tasks. They excel in NLP, image recognition, speech synthesis, code generation, audio analysis, and video processing. This versatility empowers them to enhance applications across communication, creative content generation, software development, multimedia analysis, and more, making them invaluable in today's AI landscape.

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  • LLaVA (opens in a new tab) (2023): LLaVA-1.5 excels in 11 benchmarks with minimal changes from LLaVA, using only public data, quick training in ~1 day on a single 8-A100 node, and outperforming billion-scale data methods. It's a powerful multimodal model, rivaling GPT-4 in chat capabilities and setting a new Science QA accuracy record. (code) (paper)

  • SeamlessM4T (opens in a new tab): Meta AI's advanced translation model, effortlessly enables speech and text communication across diverse languages. Supporting speech recognition and translation in nearly 100 languages, it unifies speech-to-speech, speech-to-text, text-to-speech, and text-to-text capabilities. This single-system model improves efficiency, quality, and addresses limitations of existing translation systems, marking a significant step toward a universal translator. (live model) (paper) (blog)

  • Fuyu-8B (opens in a new tab), an ADEPT multimodal model, excels at text and image comprehension. It simplifies the traditional transformer architecture, making it easier to grasp, scale, and deploy. Fuyu-8B handles complex visual relationships, including charts and documents, and performs tasks like OCR and text localization in images.