benchmark performance
-
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.
-
AI2025Dev: A New Era in AI Analytics
Read Full Article: AI2025Dev: A New Era in AI Analytics
Marktechpost has launched AI2025Dev, a comprehensive analytics platform for AI developers and researchers, offering a queryable dataset of AI activities in 2025 without requiring signup. The platform includes release analytics and ecosystem indexes, featuring "Top 100" collections that connect models to research papers, researchers, startups, founders, and investors. Key features include insights into open weights adoption, agentic systems, and model efficiency, alongside a detailed performance benchmarks section for evaluating AI models. AI2025Dev aims to facilitate model selection and ecosystem mapping through structured comparison tools and navigable indexes, supporting both quick scans and detailed analyses. This matters because it provides a centralized resource for understanding AI developments and trends, fostering informed decision-making in AI research and deployment.