BarkGPT40K Parameters. Infinite Woofs.The Smallest LLM That
Barks Back
BarkGPT is a tiny language model (just 40K parameters) that generates dog sounds. Built from scratch for educational purposes, it demonstrates how transformers work without billion-parameter complexity.
Why AI That Barks?
BarkGPT is a GPT-style language model trained from scratch for educational purposes. It uses the same transformer architecture as ChatGPT, Claude, and other modern LLMs — but with a tiny 9-word vocabulary that makes every concept accessible.
Built in pure PyTorch with no hidden abstractions, BarkGPT demonstrates how neural networks learn token probabilities through attention mechanisms, embeddings, and cross-entropy loss. It's the simplest path to understanding how LLMs actually work.
The model trains on synthetic data, exports to HuggingFace Transformers and GGUF format, and runs in production. Perfect for AI engineers who want to peek under the hood without drowning in billion-parameter complexity.
Built From Scratch
An educational deep learning project demonstrating how LLMs work
Real Transformer Architecture
BarkGPT uses the same GPT architecture as modern LLMs: token embeddings, positional encoding, multi-head attention, and layer normalization. A tiny 9-word vocabulary makes the concepts accessible.
Trained on Synthetic Data
The model learns to predict dog sounds (woof, arf, ruff, grrr) given a prompt contract. Training uses cross-entropy loss and demonstrates how neural networks learn token probabilities.
Pure PyTorch Implementation
No hidden abstractions. The entire model, training loop, and generation logic are written in plain PyTorch, making it easy to understand every component of an LLM.
Production-Ready Export
Learn how to wrap models with HuggingFace Transformers and export to GGUF format for fast inference and deployment in production environments.
Open Source & Free Forever
BarkGPT is open source. Star the repo to support the project and help others discover it.