AI & Agents · Advanced
Build Your Own LLM From Scratch
Train a small GPT-style model end to end: write the tokenizer, the transformer, pretraining, supervised fine-tuning, and a chat inference server you can talk to.
You implement a GPT-style language model from the ground up in PyTorch: a byte-pair encoding tokenizer, a multi-head self-attention transformer, a pretraining loop on a text corpus, and a supervised fine-tuning stage that turns the base model into a chat assistant. The result is a fully functional inference server that accepts prompts and streams generated text token by token. Building every layer yourself makes abstract concepts like attention, KV-caching, and loss schedules concrete and debuggable. This project is the clearest path from "I know Python" to "I understand how ChatGPT actually works."
What you build
- Byte-pair encoding tokenizer trained on your corpus with a configurable vocabulary size
- GPT-style transformer with configurable layers, heads, and embedding dimensions
- Pretraining loop with cosine LR decay, gradient clipping, and Weights and Biases loss tracking
- Supervised fine-tuning on an instruction dataset to produce a chat-capable model
- KV-cache inference for fast autoregressive token generation
- HTTP chat server that streams completions token by token to a simple web client
- Optional flash-attention kernel via Triton for GPU memory efficiency
What it teaches
- Transformer architecture internals: embeddings, causal self-attention, layer norm, and residual connections
- Byte-pair encoding tokenization and how vocabulary size affects model size and generalization
- Pretraining and fine-tuning as distinct stages with different data formats and learning rate regimes
- KV-caching and autoregressive decoding strategies including temperature and top-p sampling
- Experiment tracking and loss curve interpretation with Weights and Biases
- GPU memory management and mixed-precision training with PyTorch AMP
How it works
- 1
Raw Corpus
- TinyStories or Shakespeare txt
- BPE tokenizer training
↓ encode
- 2
Token File
- Binary .bin shard
- train / val split
↓ batch
- 3
Pretrain Loop
- GPT transformer
- AdamW + cosine LR
- W&B loss tracking
↓ checkpoint
- 4
SFT Fine-tune
- Instruction pairs
- lower LR second pass
↓ export
- 5
Inference Server
- KV-cache decode
- Flask SSE stream
↓ respond
- 6
Chat Client
- HTML page
- streamed tokens
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Take it further
- Replace the hand-written attention kernel with a Triton flash-attention implementation and benchmark the throughput gain on long sequences.
- Add reinforcement learning from human feedback using a small reward model trained on preference pairs, following the InstructGPT recipe.
- Package the inference server as a Docker image and deploy it to a GPU cloud instance with a public URL.
