vision-language
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Liquid AI’s LFM2.5: Compact Models for On-Device AI
Read Full Article: Liquid AI’s LFM2.5: Compact Models for On-Device AI
Liquid AI has unveiled LFM2.5, a compact AI model family designed for on-device and edge deployments, based on the LFM2 architecture. The family includes several variants like LFM2.5-1.2B-Base, LFM2.5-1.2B-Instruct, a Japanese optimized model, and vision and audio language models. These models are released as open weights on Hugging Face and are accessible via the LEAP platform. LFM2.5-1.2B-Instruct, the primary text model, demonstrates superior performance on benchmarks such as GPQA and MMLU Pro compared to other 1B class models, while the Japanese variant excels in localized tasks. The vision and audio models are optimized for real-world applications, improving over previous iterations in visual reasoning and audio processing tasks. This matters because it represents a significant advancement in deploying powerful AI models on devices with limited computational resources, enhancing accessibility and efficiency in real-world applications.
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Local Advancements in Multimodal AI
Read Full Article: Local Advancements in Multimodal AI
The latest advancements in multimodal AI include several open-source projects that push the boundaries of text-to-image, vision-language, and interactive world generation technologies. Notable developments include Qwen-Image-2512, which sets a new standard for realistic human and natural texture rendering, and Dream-VL & Dream-VLA, which introduce a diffusion-based architecture for enhanced multimodal understanding. Other innovations like Yume-1.5 enable text-controlled 3D world generation, while JavisGPT focuses on sounding-video generation. These projects highlight the growing accessibility and capability of AI tools, offering new opportunities for creative and practical applications. This matters because it democratizes advanced AI technologies, making them accessible for a wider range of applications and fostering innovation.