Build a Modern LLM From Scratch in PyTorch: DeepSeek 7B/67B

Large language models are no longer a tool for developers. They are changing how everyone writes, learns, works and makes decisions, and this is the beginning, not the peak. In a few years, knowing how these models work will matter the way knowing how the internet worked mattered in 2000. Yet very few people do, because the courses that promise to teach it build a toy model. This one builds DeepSeek.

You will write a modern large language model in pure PyTorch, one component at a time: attention with rotary position embeddings, grouped-query attention, RMSNorm, SwiGLU, the decoder stack, a KV cache and a tokenizer. Then you will load the official DeepSeek weights into the code you wrote, and your model will produce exactly the same output as DeepSeek's own implementation, token for token.

No library hides the model from you. Every tensor shape, every design choice and every number in the config file is written and understood by you.

What you will build

• Attention and RoPE. Multi-head attention from first principles, then rotary position embeddings and why every modern model uses them.

• Grouped-query attention. How DeepSeek 67B shares 8 key/value heads across 64 query heads, and why that makes its KV cache 8x smaller.

• RMSNorm and SwiGLU. What replaced LayerNorm and the classic feed-forward block, and the evidence behind each change.

• The decoder stack. Assemble 30 layers for the 7B model, or 95 for the 67B, into a network that matches the published architecture exactly.

• The KV cache. Turn generation from recomputing the whole sequence at every step into computing each token once: prefill, decode, and the bugs that silently corrupt generation.

• The tokenizer. Special tokens, the chat format the model was fine-tuned on, and why a single wrong blank line degrades the answers.

• Real weights. Load the official DeepSeek 7B and 67B checkpoints into your model with strict shape matching, and generate text.

  
  

How the course works

Every component follows the same two steps. First comes the concept, explained from the ground up with the reasoning behind each design choice. Then comes the implementation, written step by step so that every line is motivated by what you just learned. The result is code you understand line by line, not code you copied.

What this course is not

It does not pretrain a model from random weights, because that costs millions of dollars of compute. You will understand how DeepSeek was trained, and you will run the real result. It is also not a survey of architectures. It is one architecture, the one behind most open models today, built completely.

By the end, you will have written DeepSeek yourself, loaded its official weights, and watched your own code produce its answers. That is the difference between using these models and understanding them. Every serious LLM role now expects the second. Enroll now and start with the first block.