Build & Train a GLM-5.3-Flash Model From Scratch with Python
📚 Main topics
- A small model for hands-on researchThe tutorial builds a roughly 25-million-parameter GLM-5.3-Flash-inspired model that can be trained on a CPU, using small experiments to explore modern model training. 2:37
- Tokens, embeddings, and transformer flowThe model uses byte-level tokens to keep its vocabulary small, then converts tokens to learned embeddings and predicts a probability distribution for the next token. 4:43
- Architecture componentsThe walkthrough covers manifold-constrained hyperconnections, weight tying, RMSNorm, NoPE and RoPE, a sparse-attention indexer, linear attention, and a mixture of experts. 8:59
- Vision and pre-trainingImages are divided into patches and represented as tokens; pre-training teaches the model by predicting the next token in examples, with AdamW used for optimization. 23:04
- Reinforcement learning and evaluationPost-training rewards correct task completions, explores GRPO-style comparisons among sampled answers, and evaluates both task gains and possible regressions. 30:22
✨ Key takeaways
- Small vocabularies suit small modelsA large vocabulary can consume most of a small model’s parameters, so a character- or byte-level vocabulary makes experimentation more practical. 5:49
- Hybrid attention balances cost and memoryLinear attention maintains a fixed-size compressed state, while sparse attention can retrieve selected distant tokens at greater computational cost. 18:21
- Data and environments matterOnce an algorithm is good enough, choosing useful training data and designing tasks the model can learn may matter more than fine-tuning the algorithm. 35:05
- Measure generalization and regressionsReinforcement learning improved performance on trained task families, but some other tasks did not improve and could regress; evaluation requires careful measurement. 39:18
🧠 Lessons learned
- Treat experiments as research questionsCompare choices such as partial rewards, invalid-output penalties, sampling temperature, and group size rather than assuming one setup is best. 3:38
- Change one variable at a timeSmall, controlled experiments and toy examples make outcomes easier to interpret than large, complicated runs. 38:16
- Check statistical reliabilityResults varied across seeds, and the experiments did not establish statistically significant improvements; repeated runs and sufficient samples are important. 41:55
- Be cautious with curriculum claimsInterleaving data appeared to help in the example, while curriculum training showed no substantial gain; the speaker stresses that experiment design limits what can be concluded. 28:50
🏁 Conclusion/next steps
- Practice research on small modelsUse the code and simple training runs to practice designing environments, forming hypotheses, and measuring outcomes without needing large-scale compute. 42:58
- Keep a consistent research routineThe speaker recommends doing small experiments regularly and sharing findings consistently, while focusing on learning rather than trying to outscale major labs. 43:28
Transcript excerpt
0:00 Learn how to build and train a 25 million parameter GLM 5.3 flash model from scratch using just a standard CPU. This tutorial covers core pre-training and reinforcement learning techniques, teaching you how to design effective experiments and think like a modern AI researcher. Let's build and train GLM 5.3 flesh which is ox alpha which is the latest best viral model from Z.ai. We will do both pre-training and
0:31 post-training as well as building everything from scratch. So you will learn how to do everything GLM researchers everything researchers at OpenAI Anthropic do just on a simpler scale. And AI researcher job has dramatically changed in just a few months. So this is the latest up-to-date uh course that you're going to need if you want to become AI researcher. All of the code is in the GitHub. Uh these are the experiments. We will reproduce everything you see the charts. So it's below the video. This is the latest job
1:03 posting for Anthropic and this job posting describes 95% of AI researcher roles. First you will understand the architectures and the algorithms in this course. Then I will show you how to do uh actual AI research with designing environments reinforcement learning asking questions. So right now cloud code codex AI is coding all of the experiments and it can do them autonomously. Your job as AI researcher is just to have highlevel idea which experiments to do. So even though
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Questions & Answers
Common questions about this video
What is the goal of building a small GLM-5.3-Flash model in this tutorial?
Why does the tutorial use a byte-level vocabulary of about 260 tokens?
How do linear attention and sparse attention differ in the model?
What is the purpose of mixture-of-experts layers?
How does pre-training differ from reinforcement learning in this tutorial?
How does GRPO compare a model's generated answers?
Why should researchers change one experimental variable at a time and test multiple seeds?
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