Follow this step‑by‑step checklist to pre‑train and fine‑tune a 25M parameter GLM‑style model using Python on any CPU.
Based on: Build & Train a GLM‑5.3‑Flash Model From Scratch with Python by freeCodeCamp.org
▶ Watch the source video on YouTube
What if you could build and train your own AI language model today — on the same laptop you use for everything else — without a massive GPU cluster or a PhD?
Most people assume that AI research is reserved for engineers at OpenAI or Anthropic with millions in compute budgets. So they watch tutorials, get overwhelmed by the theory, and never actually build anything. Meanwhile, the field is moving so fast that the skills researchers need most right now — designing experiments, constructing training environments, asking the right research questions — are things you can genuinely practice on a CPU with a 25-million-parameter model.
Imagine finishing a weekend project where your own model goes from answering zero out of 24 coding tasks correctly to solving 16 of them — because you personally designed the reinforcement learning environment that taught it how to think. That's not a fantasy. That's exactly what freeCodeCamp.org walks through in this course, covering pre-training, post-training, mixture-of-experts architecture, sparse attention, hyperconnections, and GRPO-style reinforcement learning — all explained through working Python code you can run right now.
freeCodeCamp.org has spent years making world-class technical education completely free and accessible. This course reflects the absolute cutting edge of what AI researchers actually do in 2024: not hand-coding every experiment, but knowing which experiments to run, how to measure results properly, how to avoid false conclusions from small sample sizes, and how to construct environments where a model can genuinely learn. Those judgment skills are what separate a researcher from a script follower — and this course teaches both.
This checklist distills every actionable step from the full tutorial into a clean, structured reference you can work through at your own pace. From setting up your character-level tokenizer with a 260-token vocabulary, to configuring your 8-expert mixture-of-experts layers, to running GRPO reward experiments and interpreting statistical significance correctly — every step is here. Use it to build your first model, design your first real experiment, and start posting your results. Consistency beats intensity: one experiment posted per week will do more for your AI career than a single all-nighter ever could.
Every checklist item comes with actionable notes to guide you — things like "Don't forget to do this before you start," "Avoid this common mistake," or "Set a reminder for 30 days out." Nothing vague, just clear next steps.
+ 34 more action items inside...
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