Build Your Own LLM, Lesson 12: Quantize and Run Your Model Locally
Shrink models with quantization: fp32 to int8 with PyTorch dynamic quantization, size and speed trade-offs, GGUF and 4-bit formats, and running open models locally…
Read article →Build Your Own LLM
A step-by-step course: tokenizer, embeddings, attention and a tiny GPT you train yourself.
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← AI & ML
Build Your Own LLM
A step-by-step course: tokenizer, embeddings, attention and a tiny GPT you train yourself.
📄 13 articles⬇️ 13 with practice files🕒 Updated 28 Sep 2026
🎓 Start here: Vibe Coding Course → 🎓 Start here: AI Agents & RAG Course → 🎓 Start here: AI for Office Work Course →Build Your Own LLM, Lesson 11: Instruction Tuning and Chat Templates
Build Your Own LLM, Lesson 11: Instruction Tuning and Chat Templates
Turn a text predictor into an assistant: instruction-response data, chat templates with role tokens, masking the loss on prompts, and why each model needs…
Read article →Build Your Own LLM, Lesson 10: Fine-Tuning a Pre-Trained Model With LoRA
Build Your Own LLM, Lesson 10: Fine-Tuning a Pre-Trained Model With LoRA
Fine-tune efficiently with LoRA: freeze the base model, add low-rank adapter matrices to attention layers, train under 2% of the parameters, and how the…
Read article →Build Your Own LLM, Lesson 9: Evaluating a Model — Loss, Perplexity and Test Prompts
Build Your Own LLM, Lesson 9: Evaluating a Model — Loss, Perplexity and Test Prompts
Measure a language model: cross-entropy loss, perplexity and what it means, validation sets, fixed test prompts, and why benchmarks and human checks are both…
Read article →Build Your Own LLM, Lesson 8: Sampling — Temperature, Top-k and Top-p
Build Your Own LLM, Lesson 8: Sampling — Temperature, Top-k and Top-p
How a language model picks the next token: greedy decoding, temperature, top-k and nucleus (top-p) sampling, implemented in a dozen lines and compared side…
Read article →Build Your Own LLM, Lesson 7: Train a Tiny GPT on Your Own Text
Build Your Own LLM, Lesson 7: Train a Tiny GPT on Your Own Text
Stack transformer blocks into a GPT, add token and position embeddings, train it on a text file with AdamW on a laptop CPU, watch…
Read article →Build Your Own LLM, Lesson 6: Multi-Head Attention and the Transformer Block
Build Your Own LLM, Lesson 6: Multi-Head Attention and the Transformer Block
Extend single-head self-attention to multi-head attention, add the feed-forward network, layer norm and residual connections, and test the causal mask — the full transformer…
Read article →Build Your Own LLM, Lesson 5: Self-Attention From Scratch
Build Your Own LLM, Lesson 5: Self-Attention From Scratch
Understand and code the attention mechanism behind transformers: queries, keys, values, the causal mask and softmax — first with NumPy on four tokens, then…
Read article →Build Your Own LLM, Lesson 4: Embeddings and Your First Neural Language Model
Build Your Own LLM, Lesson 4: Embeddings and Your First Neural Language Model
Rebuild the bigram model as a neural network in PyTorch, train it with gradient descent, and add embeddings — the vectors that let language…
Read article →Build Your Own LLM, Lesson 3: A Language Model Made of Counting
Build Your Own LLM, Lesson 3: A Language Model Made of Counting
Build a bigram language model with plain Python dictionaries, generate text from it, and measure it with loss — the same number every LLM…
Read article →Build Your Own LLM, Lesson 2: Write a BPE Tokenizer in Python
Build Your Own LLM, Lesson 2: Write a BPE Tokenizer in Python
Build the byte-pair encoding tokenizer used by GPT-style models from scratch in about 40 lines of Python: training merges, encoding, decoding and why the…
Read article →Build Your Own LLM, Lesson 1: How a Language Model Actually Works
Build Your Own LLM, Lesson 1: How a Language Model Actually Works
A plain-English tour of what happens inside ChatGPT-style models: tokens, probabilities, training on next-token prediction, and why a model that only guesses the next…
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