A coding course builds reasoning on a small pretrained model
Sebastian Raschka published eight coding lectures, totaling more than eleven hours, on evaluating and improving a small Qwen3 model with Python and PyTorch. The curriculum covers inference-time scaling, reinforcement learning and distillation. It begins with pretrained weights, rather than training a base model from zero; early generation and evaluation exercises can run on a CPU, while training benefits from a GPU.
Sebastian Raschka · Ahead of AI · Oct 24, 2026
Why it matters
Engineers who want to understand reasoning-model techniques can follow concrete implementations rather than relying only on model announcements.
More context
The written course overview and official code repository were reviewed. Raschka recommends familiarity with Python and PyTorch; his earlier base-model course is optional. Start with model loading and evaluation, then choose a later training chapter according to available hardware.
Keep in mind
This card summarizes the author’s written curriculum and repository, not a review of all eleven hours of video. The examples are educational small-model exercises and do not reproduce a frontier training run.
Original read