Hello World — Train Your Own Language Model

Deploy a real GPU and train a GPT language model from scratch in under 10 minutes. Same transformer architecture behind ChatGPT, Claude, and Gemini — just smaller. ~10-20 million parameters, 6 transformer layers, 6 attention heads, 5,000 training steps. Free $25 GPU credit, no credit card required.

What You Will Build

A character-level GPT that learns to write by predicting the next character in a sequence. Choose your training data — Shakespeare, Python code, Wikipedia, or math proofs — and watch the model learn patterns, grammar, and structure from raw text.

How Transformer Language Models Work

• Tokenization: Text becomes numbers. Each character maps to a unique integer so GPUs can process it mathematically.
• Embeddings: Each number transforms into a high-dimensional vector where similar characters naturally cluster together.
• Position Encoding: The model adds learned positional information so it understands sequence order, not just identity.
• Self-Attention: The breakthrough mechanism — each character computes a Query and compares against every previous character's Key to find relevant context, then reads their Values.
• Multi-Head Attention: 6 attention heads run in parallel across 6 layers — 36 unique perspectives analyzing grammar, meaning, and long-range patterns simultaneously.
• Generation: The model outputs a probability distribution for every possible next character. Sample from it, feed it back in, repeat. That is how AI writes text.

Your Model vs GPT-4

Same architecture, different scale. Your model: ~10 million parameters, trained in minutes on 1 GPU. GPT-4: 1.8 trillion parameters, trained over 100 days on 25,000 GPUs. The math is identical — yours is a smaller version of exactly what powers ChatGPT.

Temperature and Sampling

After training, you can prompt your model and control its creativity. Temperature controls randomness: low temperature (0.3) produces safe, repetitive text; high temperature (1.5) produces creative, unpredictable text. Top-k sampling limits the candidate pool to the k most likely next characters.

GPU Options

DAIRX automatically selects the cheapest available GPU suitable for this workload. RTX 4090 ($0.59/hr typical), L40S ($0.80/hr), or A100 ($2.50/hr) as fallback. Training takes approximately 7 minutes. Total cost: under $0.10 per session.