Build & Train a GLM-5.3-Flash Model From Scratch with Python

freeCodeCamp.org and Vuk Rosić · 2026-10-06

Build & Train a GLM-5.3-Flash Model From Scratch with PythonfreeCodeCamp.org and Vuk Rosić ▶ Play the video

Why it earned a slot

Vuk Rosić’s companion repository builds a 25.7M-parameter educational model inspired by GLM-5.3-Flash. It combines simplified linear and sparse attention, mixture-of-experts layers and learned residual streams, then uses executable rewards for a small Python task. The code supports CPU, Apple Silicon MPS and NVIDIA CUDA. This is a miniature for studying the components, not a reproduction of the full released model. Start with the repository’s small example before increasing the training budget.

The short version

Study modern model components in a runnable miniature.

Why it matters

A small implementation makes attention, expert routing and residual paths easier to inspect. The creator states where these components simplify the released architecture, which helps keep a teaching exercise separate from a production-model comparison.

My take

I would use this to trace one training batch and one generated program through the model, then change one component while holding the data and budget fixed. Multi-GPU scaling is a later question; first establish what the miniature learns on its own held-out tasks.

How it connects

• Inspect attention, expert routing and residual streams separately.
• Keep educational simplifications visible.
• Evaluate executable rewards on held-out tasks.

Bottom line

Open the companion repository, run its smallest supported example, and inspect the held-out results before scaling.

Topics: GLM, flash attention, Mixture of Experts, model training, multimodal