Microsoft introduces Phi-4-multimodal and Phi-4-mini, small language models (SLMs) designed for efficient on-device AI. These models process speech, vision, and text with reduced computational demands, enabling advanced AI applications on smartphones, laptops, and edge devices. Phi-4 showcases the shift towards accessible, efficient, and specialized AI, democratizing powerful capabilities beyond large data centers.
Microsoft's Phi-4 family redefines AI efficiency. These compact models, including Phi-4-multimodal and Phi-4-Mini, process text, images, and speech with less computational power. 'Mixture of LoRAs' enables multimodal capabilities without performance loss. Phi-4 excels in benchmarks, offering real-world benefits like cost savings and edge deployment, democratizing AI access for diverse applications.
Microsoft introduces Phi-4-multimodal and Phi-4-mini, powerful small language models. Phi-4-multimodal excels in multimodal tasks like speech, vision, and text. Phi-4-mini is optimized for text-based tasks. Both offer efficiency, low latency, and on-device capabilities, revolutionizing AI applications across industries while prioritizing safety and security in their development and deployment.
Microsoft Research introduces Phi-4, a 14 billion-parameter small language model designed for advanced math reasoning. It outperforms similar and larger models using synthetic data pre-training, organic data curation, and novel post-training methods. Phi-4 surpasses its teacher model GPT-4o in STEM-focused QA.
Microsoft introduces MatterGen, a groundbreaking AI model for inorganic material creation, enhancing discovery and performance in sectors like battery tech and aerospace.
