MobileLLM-R1: Compact Powerhouse for Mathematical & Code Reasoning
14 days ago 高效码农★MobileLLM-R1: Revolutionizing Efficient AI Reasoning with Compact Models★ What Problem Does MobileLLM-R1 Solve? MobileLLM-R1 addresses the critical challenge of deploying high-performance AI reasoning capabilities in resource-constrained environments, proving that smaller models can achieve exceptional results when properly designed and trained. In an era where AI models are growing exponentially in size and computational requirements, Meta’s MobileLLM-R1 series emerges as a groundbreaking solution that challenges the “bigger is better” paradigm. This family of efficient reasoning models demonstrates that through careful architecture design and targeted training strategies, compact models can deliver performance comparable to much larger counterparts in specialized domains like mathematical …
K2-Think: How a 32-Billion-Parameter Model Outperforms Giants in Math Olympiads
15 days ago 高效码农A conversation starter “Can a model small enough to fit on four gaming GPUs beat the latest 120-billion-parameter heavyweights at high-school math competitions?” The Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) just proved the answer is ‘yes’. Below is a fully-transparent walk-through of their K2-Think recipe—data, code, training budget, safety filters and all—rewritten for junior-college graduates and busy engineers who simply want facts, numbers and reproducible steps. 1. Thirty-second summary Base model: Qwen2.5-32B (completely open weights) Post-training data: one open-source set, 92 k problems with automatically checkable answers Training stages: long-chain supervised fine-tuning → verifiable-reward RL → simple test-time …
EchoMimicV3: How a 1.3B-Parameter Model Masters Multi-Modal Human Animation
1 months ago 高效码农tags: – EchoMimicV3 – 1.3B – Soup-of-Tasks – Soup-of-Modals – CDCA – PhDA – Negative DPO – PNG – Long Video CFG – Wan2.1-FUN EchoMimicV3 — How a 1.3B-parameter Model Unifies Multi-Modal, Multi-Task Human Animation Intro (what you’ll learn in a few lines) This post explains, using only the provided project README and paper, how EchoMimicV3 is designed and implemented to produce multi-modal, multi-task human animation with a compact 1.3B-parameter model. You’ll get a clear view of the problem framing, the core building blocks (Soup-of-Tasks, Soup-of-Modals / CDCA, PhDA), the training and inference strategies (Negative DPO, PNG, Long Video CFG), …