09-07-2026, 05:18 PM
The freeCodeCamp article introduces a six-hour hands-on course by Vivek Kalyanarangan that teaches how to build a Large Language Model from scratch using pure PyTorch, combining the underlying theory with practical implementation. It begins with the core Transformer architecture and training a small language model, then adds modern techniques such as RMSNorm, Rotary Positional Embeddings (RoPE), KV caching, mixed-precision training, and Mixture-of-Experts (MoE).
The course then moves into post-training and alignment, covering Supervised Fine-Tuning (SFT), reward modeling, and Reinforcement Learning from Human Feedback (RLHF) using PPO, showing how a base model can be shaped into a more useful and safer assistant. Overall, it is designed as an end-to-end introduction to how modern LLMs are constructed, optimized, scaled, fine-tuned, and aligned, with the complete code available for experimentation.
COURSE
The course then moves into post-training and alignment, covering Supervised Fine-Tuning (SFT), reward modeling, and Reinforcement Learning from Human Feedback (RLHF) using PPO, showing how a base model can be shaped into a more useful and safer assistant. Overall, it is designed as an end-to-end introduction to how modern LLMs are constructed, optimized, scaled, fine-tuned, and aligned, with the complete code available for experimentation.
COURSE
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│ KONSTANTINOS MICHAILIDIS │
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