Liquid AI Unveils LFM2.5: Tiny Foundation Models for On-Device AI
Analysis
LFM2.5's focus on on-device agentic applications addresses a critical need for low-latency, privacy-preserving AI. The expansion to 28T tokens and reinforcement learning post-training suggests a significant investment in model quality and instruction following. The availability of diverse model instances (Japanese chat, vision-language, audio-language) indicates a well-considered product strategy targeting specific use cases.
Key Takeaways
- •Liquid AI released LFM2.5, a family of tiny on-device foundation models.
- •LFM2.5 is designed for on-device agentic applications with improved quality and lower latency.
- •The models are available in multiple instances, including general-purpose, Japanese chat, vision-language, and audio-language.
Reference
“It’s built to power reliable on-device agentic applications: higher quality, lower latency, and broader modality support in the ~1B parameter class.”