What's Next?
Try It Live
Your model is deployed on Hugging Face! Try it right here:
Hi! I'm a tiny 105K parameter LLM that can do basic math. Try asking me something like "two plus three" or "seven times eight".
This is calling the exact model you built, running on Hugging Face Spaces via a Gradio API. The same 105K parameters, trained on addition, subtraction, and multiplication.
You Built a Transformer!
Congratulations! You've implemented every component that powers GPT, Claude, and other LLMs:
- Tokenization — converting text to numbers
- Embeddings — giving tokens meaning
- Positional encoding — adding word order
- Attention — letting tokens communicate
- Feed-forward networks — processing information
- Training loop — learning from data
- Generation — producing output token by token
Scaling Up
The difference between your calculator and GPT-4 is just scale:
| Aspect | Your Model | GPT-4 |
|---|---|---|
| Parameters | ~50K | ~1.7 trillion |
| Training data | 10K examples | Trillions of tokens |
| Vocabulary | 36 tokens | 100K+ tokens |
| Context length | 10 tokens | 128K+ tokens |
| Training cost | Free (laptop) | $100M+ |
Same architecture. Same math. Just more of everything.
Keep Learning
- Try larger numbers (0-100 instead of 0-19)
- Add division operation
- Experiment with more layers and heads
- Train on different tasks (translation, summarization)
- Read the original "Attention Is All You Need" paper
You now understand transformers at a fundamental level. Everything else—BERT, GPT, Claude—is variations on what you've built. The mystery is gone. Go build something amazing!
Helpful?