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
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