Core Insights - The article discusses the dominance of the Transformer architecture in large models and introduces Liquid AI's new model, LFM2.5-1.2B-Thinking, which operates efficiently on edge devices [1][2]. Group 1: Model Overview - Liquid AI has released LFM2.5-1.2B-Thinking, a reasoning model that can run entirely on edge devices with only 900 MB of memory [2][3]. - This model excels in generating internal reasoning trajectories before arriving at final answers, demonstrating superior performance in tool usage, mathematical reasoning, and instruction following [3][14]. Group 2: Performance Metrics - Compared to its predecessor LFM2.5-1.2B-Instruct, LFM2.5-1.2B-Thinking shows significant improvements in three key areas: mathematical reasoning (from 63 to 88 on MATH-500), instruction following (from 61 to 69 on Multi-IF), and tool usage (from 49 to 57 on BFCLv3) [7][9]. - In various reasoning benchmarks, LFM2.5-1.2B-Thinking's performance matches or exceeds that of Qwen3-1.7B, despite having approximately 40% fewer parameters [7][10]. Group 3: Training and Development - The model's training involved multi-step reasoning to enhance capabilities while maintaining concise answers for low-latency deployment [16]. - Liquid AI implemented strategies to reduce the occurrence of "doom looping" in the model's responses, achieving a reduction from 15.74% to 0.36% in the final training phase [17][18]. Group 4: Ecosystem and Compatibility - Liquid AI is expanding the ecosystem for the LFM series, ensuring compatibility with popular reasoning frameworks and supporting various hardware accelerations [24]. - The model has been tested across different devices, showcasing its efficient performance in long-context reasoning [26]. Group 5: Future Implications - LFM2.5-1.2B-Thinking signifies a shift away from the exclusive reliance on Transformer models, suggesting that smaller, powerful edge reasoning models may offer superior solutions [27]. - The decreasing barriers to running inference models on various devices is seen as a positive development for AI potential [28].