在 CPU 上从零构建并训练 25M 参数 LLM

You don't need a massive GPU cluster to learn how modern frontier AI works. In our new video on the freeCodeCamp.org YouTube channel, you will build, pre-train, and fine-tune a working 25-million parameter language model directly on your laptop or standard CPU.

Frontier labs train massive systems with billions of parameters, but the core architecture, math, and workflows are fundamentally identical. Scaling the architecture down to 25 million parameters with a compact byte-level vocabulary strips away expensive cloud compute costs and lets training loops run locally on your CPU in seconds. This rapid iteration allows you to directly observe how changes to data curricula, loss functions, and reward designs alter model behavior in real time.

Here are some things covered in the course:

• Modern LLM Architecture
  Learn how cutting-edge techniques work under the hood, including hybrid linear/sparse attention, Mixture of Experts (MoE), tied embedding weights, and multimodal image patch inputs.

• Pre-Training from Zero
  Watch the model start from generating random characters and learn Python syntax and structure step-by-step using cross-entropy loss.

• Post-Training with Reinforcement Learning (RL)
  Build a lightweight RL loop where the model writes code, runs against automated unit tests, and receives rewards—boosting its task success rate from 0% up to passing the majority of evaluations.

• How AI Researchers Actually Work
  Learn how to think like a modern researcher by forming testable hypotheses, tweaking temperatures and reward structures, and checking for real statistical significance across multiple seeds.

Watch the full tutorial for free on the freeCodeCamp.org YouTube channel (1-hour watch).

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