Pre-Training a Mini GPT (124M Parameters)
GPT-2 Small has 124 million parameters. That's 12 transformer layers, 12 attention heads, and 768-dimensional embeddings. You can train it from scratch on a single GPU in a few hours. Most people never do this. They use pre-trained checkpoints. But if you don't train one yourself, you don't actually understand what's happening inside the model you're building products on. Implement the full GPT-2 architecture (124M parameters) from scratch: token embeddings, positional embeddings, transformer blocks, and the language model head. Train a GPT model on a text corpus using next-token prediction with cross-entropy loss. Implement autoregressive text generation with temperature sampling and top-k/top-p filtering. Monitor training loss curves and validate that the model learns coherent language patterns. You know what a transformer is. You have read the diagrams. You can recite "attention is all you need" and draw boxes labeled "Multi-Head Attention" on a whiteboard. None of that means you understand what happens when a model generates text. There are 124,438,272 parameters in GPT-2 Small (with weight tying). Every single one of them was set by running a training loop: forward pass, compute loss, backward pass, update weights. Twelve transformer blocks. Twelve attention heads per block. A 768-dimensional embedding space. A vocabulary of 50,257 tokens. Every time the model generates a token, all 124 million parameters participate in a single matrix…
Pre-Training a Mini GPT (124M Parameters): GPT-2 Small has 124 million parameters. That's 12 transformer layers, 12 attention heads, and 768-dimensional…
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