Inference
After training, the model generates new names it has never seen. Starting from BOS, it predicts the next character, samples one, feeds it back in, and repeats until BOS appears again.
Generate
Temperature 0.5: Conservative
At this conservative temperature, the model strongly prefers likely characters but occasionally takes a less obvious choice. Names will look realistic and common.
How Temperature Works
1. Model outputs logits: Raw scores for each character (e.g., a: 2.5, b: -0.3, c: 1.8)
2. Divide by temperature:
Low temp (0.3) = bigger differences = sharper peak
High temp (2.0) = smaller differences = flatter spread
3. Convert to probabilities (softmax):
Effect on Distribution
Same raw scores, different temperatures:
Low temp (blue) = sharp peak. High temp (orange) = even spread.
No names generated yet
Click "Generate 1" or "Generate 10" above to create names. You can then explore how each was built character-by-character.
How inference works
- • The model starts with the BOS token as input
- • At each step, it runs a full forward pass to get probabilities for all 27 tokens
- • Temperature scales the logits before softmax — dividing by T makes the distribution sharper (low T) or flatter (high T)
- • A character is sampled from this distribution (not just argmax, which would be greedy)
- • The sampled character becomes the input for the next step
- • Generation stops when BOS is predicted — the model decides the name is complete
- • The same model can generate infinitely many different names due to the randomness in sampling