liquid foundation models - RMSNorm in Liquid Foundation Models - code from scratch - part 3 скачать в хорошем качестве

liquid foundation models - RMSNorm in Liquid Foundation Models - code from scratch - part 3

🎬 Part 3 – RMSNorm in Liquid Foundation Models Welcome to Episode 3 of my LFM2 series! In this video, I break down RMSNorm (Root Mean Square Layer Normalization) — what it is, why Liquid Foundation Models use it, and how to implement it from scratch in code. In this video you’ll learn: The theory behind RMSNorm: how it differs from LayerNorm by not subtracting the mean and instead normalizing by the root mean square. Edinburgh Research +2 CSDN Blog +2 Why RMSNorm is more efficient: fewer operations and less memory / compute overhead. CSDN Blog +1 How RMSNorm is formally defined (equations + epsilon for numerical stability). NeurIPS Proceedings The benefits in transformer / large-model architecture: common in models like LLaMA, Qwen, and more. CVF Open Access +2 freedium.cfd +2 How to write your own RMSNorm layer from scratch in Python / PyTorch: I’ll walk through every line, from computing RMS to scaling with a learnable parameter. Integration into LFM2: where RMSNorm sits within the model architecture (e.g., before feedforward or attention blocks), and why it's a great fit for on-device models. Why this matters: Understanding and coding RMSNorm yourself helps you grasp one of the core building blocks of modern efficient LLMs. It’s not just a theoretical trick — it’s a practical way to make your models faster, leaner, and more deployable. #RMSNorm #LiquidAI #LFM2 #Normalization #RootMeanSquareNormalization #DeepLearning #Transformer #MachineLearning #OnDeviceAI #EdgeAI #AIFromScratch #NeuralNetwork #AIArchitecture #GenerativeAI #ModelBuilding #AITheory #LLM