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Explore Build & Train a GLM-5.3-Flash Model From Scratch with Python with 200,489 views, 2,494 likes, and 65 comments. Experience the impact of this video content that has captured audience attention.

Learn how to design, build, and train an advanced multimodal language model from scratch using modern techniques like mixture-of-experts, sparse attention, and reinforcement learning. This hands-on guide walks you through the entire lifecycle from tokenization and pre-training to post-training optimization and evaluation. Video from @vukrosic GitHub: https://github.com/vukrosic/glm-5.3-flash-from-scratch Become AI researcher in 90 days: https://www.skool.com/become-ai-researcher-2669/about Slides: https://github.com/vukrosic/glm-5.3-flash-from-scratch/blob/main/slides/slides.html Blog: https://z.ai/blog/glm-5.3-flash ❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp ⭐️ Chapters ⭐️ - 00:00 Introduction & What We're Building - 01:02 The Modern AI Researcher Role & Asking Research Questions - 04:50 Tokenization & Byte-Level Vocabulary (Why Small Vocab Matters) - 07:01 Embeddings & Transformer Forward Pass Overview - 08:53 GLM-5.3 Architecture Overview & Model Specifications - 10:25 Code Walkthrough: Embeddings & Token Representation - 11:56 Manifold Constrained Hyperconnections (DeepSeek Residuals) - 13:27 Output Projection & Weight Tying - 14:27 RMSNorm & Normalization Layers - 15:10 Positional Encodings (RoPE vs. NoPE) & Sparse Attention Indexer - 18:30 Linear Attention (State-Space Memory) vs. Sparse Attention - 20:46 Mixture of Experts (MoE) & Shared Experts - 23:06 Adding Vision: Patch Embeddings & 2D RoPE - 26:14 Pre-Training Pipeline, Loss & Optimization (AdamW) - 28:05 Pre-Training Experiments: Data Diversity, Interleaving & Curriculums - 30:27 Post-Training & Reinforcement Learning (RL) Setup - 34:44 Designing Reward Functions & Group Relative Policy Optimization (GRPO) - 37:37 Parameter-Efficient RL Updates & Freezing Layers - 39:35 Evaluating RL Results: Task Gains & Regression Risks - 40:47 RL Hyperparameter Experiments: Group Size, Temperature & Seeds - 43:03 Summary & Advice for Aspiring AI Researchers 🎉 Thanks to our Champion and Sponsor supporters: 👾 @omerhattapoglu1158 👾 @goddardtan 👾 @akihayashi6629 👾 @kikilogsin 👾 @anthonycampbell2148 👾 @tobymiller7790 👾 @rajibdassharma497 👾 @CloudVirtualizationEnthusiast 👾 @adilsoncarlosvianacarlos 👾 @martinmacchia1564 👾 @ulisesmoralez4160 👾 @_Oscar_ 👾 @jedi-or-sith2728 👾 @justinhual1290 -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news

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