Most courses teach you to build a toy language model. This one teaches you to write the actual code behind a frontier model. You'll implement DeepSeek 7B and 67B in pure PyTorch — attention, rotary position embeddings, RMS normalization, the SwiGLU feed-forward network — and assemble them into a working model that generates text. Less than a thousand lines, nothing hidden. By the end, no frontier language model is a black box to you.
The gap between a toy GPT and a model people actually deploy isn't size — it's the exact details: the rotary embedding convention that has to match the saved weights, the normalization placement, the gated feed-forward, the numerical care that keeps everything stable. Get them right and your code runs the real weights and produces output token-for-token identical to the official model. Not "close enough." Identical.
The core mechanism, the scaling that's easy to get subtly wrong, and the causal mask — written from scratch.
The exact rotary convention the saved weights expect — the detail that makes the difference between "works" and "garbage out."
Pre-norm placement and the numerical handling that keeps a deep stack stable.
The gated MLP that replaced the classic ReLU block in modern frontier models.
Wire the blocks into a complete decoder, load the official weights, and watch it generate real text.
The same code — go from the 7B to the 67B model.
Your implementation runs the real weights and produces text token-for-token identical to the official release.
No framework magic, no black boxes. Clean, readable PyTorch you fully understand and can modify.
The 67B you build outperformed Llama 2 70B on reasoning, code, and math.
Learn the proven architecture used across modern open models — the foundation, not a simplified stand-in.
DeepSeek's latest model, V4 (April 2026), reaches near-frontier performance at 1.6T parameters — at a fraction of the cost of comparable closed models. You're learning the foundations of that family.
You implement the architecture and run the real model using the official released weights. No multi-week GPU runs, no training-data pipeline, no cluster. That's the point: you get the full understanding of how a frontier model is built and how it produces text, on hardware you already have — without the cost and complexity of pre-training.
Taught by the instructor behind the NeRF, 3D Gaussian Splatting, and Diffusion Models from-scratch courses.
Over 2,500 students worldwide, with a 5 / 5 median rating across 450+ verified reviews — same from-first-principles, pure-PyTorch approach, now applied to frontier LLMs.
Based on Udemy student reviews as of May 2026.
Part 1 is available now and you can start today. The complete course is finished by the end of June 2026 and delivered to you as it ships — and if it isn't, you get a full refund. The founding price is your reward for getting in before then.
No training required. You implement the architecture and run the official released weights, so there’s no multi-week training run. The 7B model runs on practical modern hardware — a 16GB GPU, or CPU-only with around 32GB RAM. The 67B model is much heavier, but the code is the same, so you’ll understand exactly what changes as the model scales.
Those build a small teaching model. Here you build the real architecture and load the actual released weights, so the output is token-for-token identical to the official model — not an approximation.
Comfort with Python and basic PyTorch. You don't need any prior knowledge of LLM internals — that's the point of the course.
You're buying before the course is complete, at a lower price, and helping shape it. In exchange you lock $199 for life while everyone after launch pays $349.
Build one yourself — under 1,000 lines, identical to the real thing. Join the founding cohort at $199 before it goes to $349.
Join the founding cohort — $199