처음부터 나만의 LLM 교육

A hands-on workshop where you write every piece of a GPT training pipeline yourself, understanding what each component does and why. Andrej Karpathy's nanoGPT was my first real exposure to LLMs and transformers. Seeing how a working language model could be built in a few hundred lines of PyTorch completely changed how I thought about AI and inspired me to go deeper into the space. This workshop is my attempt to give others that same experience. nanoGPT targets reproducing GPT-2 (124M params) and covers a lot of ground. This project strips it down to the essentials and scales it to a ~10M param model that trains on a laptop in under an hour — designed to be completed in a single workshop session. No black-box libraries. No model = AutoModel.from_pretrained() . You build it all. A working GPT model trained from scratch on your MacBook, capable of generating Shakespeare-like text. You'll write: - Tokenizer — turning text into numbers the model can process - Model architecture — the transformer: embeddings, attention, feed-forward layers - Training loop — forward pass, loss, backprop, optimizer, learning rate scheduling - Text generation — sampling from your trained model - Any laptop or desktop (Mac, Linux, or Windows) - Python 3.12+ - Comfort reading Python code (you don't need ML experience) Training uses Apple Silicon GPU (MPS), NVIDIA GPU (CUDA), or CPU automatically. Also works on Google Colab — upload the files and run with !python train.py . Install uv if you don't have it: # macOS / Linux curl -LsSf https://astral.sh/uv/install.sh | sh # Windows powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" Then set up the project: uv sync mkdir scratchpad && cd scratchpad If you don't have a local setup, upload the repo to Colab and install dependencies: !pip install torch numpy tqdm tiktoken Upload data/shakespeare.txt to your Colab files, then write your code in notebook cells or upload .py files and run them with !python train.py . Work through the docs in order. Each part walks you through writing a piece of the pipeline, explaining what each component does and why. By the end, you'll have a working model.py , train.py , and generate.py that you wrote yourself. | Part | What You'll Write | Concepts | |---|---|---| | Part 1: Tokenization | Character-level tokenizer | Character encoding, vocabulary size, why BPE fails on small data | | Part 2: The Transformer | Full GPT model architecture | Embeddings, self-attention, layer norm, MLP blocks | | Part 3: The Training Loop | Complete training pipeline | Loss functions, AdamW, gradient clipping, LR scheduling | | Part 4: Text Generation | Inference and sampling | Temperature, top-k, autoregressive decoding | | Part 5: Putting It All Together | Train on real data, experiment | Loss curves, scaling experiments, next steps | | Part 6: Competition | Train the best AI poet | Find datasets, scale up, submit your best poem | Input Text │ ▼ ┌─────────────────┐ │ Tokenizer │ "hello" → [20, 43, 50, 50, 53] (character-level) └────────┬────────┘ ▼ ┌─────────────────┐ │ Token Embed + │ token IDs → vectors (n_embd dimensions) │ Position Embed │ + positional information └────────┬────────┘ ▼ ┌─────────────────┐ │ Transformer │ × n_layer │ Block: │ │ ┌────────────┐ │ │ │ LayerNorm │ │ │ │ Self-Attn │ │ n_head parallel attention heads │ │ + Residual │ │ │ ├────────────┤ │ │ │ LayerNorm │ │ │ │ MLP (FFN) │ │ expand 4x, GELU, project back │ │ + Residual │ │ │ └────────────┘ │ └────────┬────────┘ ▼ ┌─────────────────┐ │ LayerNorm │ │ Linear → logits│ vocab_size outputs (probability over next token) └─────────────────┘ | Config | Params | n_layer | n_head | n_embd | Train Time (M3 Pro) | |---|---|---|---|---|---| | Tiny | ~0.5M | 2 | 2 | 128 | ~5 min | | Small | ~4M | 4 | 4 | 256 | ~20 min | | Medium (default) | ~10M | 6 | 6 | 384 | ~45 min | All configs use character-level tokenization (vocab_size=65) and block_size=256. This workshop uses character-level tokenization on Shakespeare. BPE tokenization (GPT-2's 50k vocab) doesn't work on small datasets — most token bigrams are too rare for the model to learn patterns from. | Tokenizer | Vocab Size | Dataset Size Needed | |---|---|---| | Character-level | ~65 | Small (Shakespeare, ~1MB) | | BPE (tiktoken) | 50,257 | Large (TinyStories+, 100MB+) | Part 5 covers switching to BPE for larger datasets. - nanoGPT — The project this workshop is based on. Minimal GPT training in ~300 lines of PyTorch - build-nanogpt video lecture — 4-hour video building GPT-2 from an empty file - Karpathy's microgpt — A full GPT in 200 lines of pure Python, no dependencies - nanochat — Full ChatGPT clone training pipeline - Attention Is All You Need (2017) — The original transformer paper - GPT-2 paper (2019) — Language models as unsupervised learners - TinyStories paper — Why small models trained on curated data punch above their weight