Evaluating Model Bits per Byte | Generated by AI
Question: What does “val bpb” mean for your nanochat model, and is 0.8658 bpb good?
Answer:
bpb = bits per byte — a language-agnostic evaluation metric for language models.
It measures how many bits you need to encode one byte of validation data on average:
bpb = cross_entropy_loss / ln(2)
Is 0.8658 bpb good?
Yes, that’s solid. Context:
- English text (Wikipedia, books): typically 1.5–2.0 bpb
- Your nanochat-d12 on validation set: 0.8658 bpb → well below natural English
- This is expected; validation losses are usually lower than real-world perplexity because the validation set often comes from the same distribution as training
- Suggests your model isn’t overfitting badly (Chinchilla-optimal ratio helps)
Related metric — perplexity:
perplexity = 2^bpb = 2^0.8658 ≈ 1.84
This is exceptionally low perplexity for a 286M model with just 5.7B tokens. For comparison:
- GPT-2 (1.5B tokens): ~29 perplexity on WikiText-103
- Your model: ~1.84 on your validation set (likely a subset of pretraining data)
Practical takeaway: Your model is memorizing/fitting the validation set very well. This is normal and good during training. The real test is generalization—how it performs on held-out data outside the training distribution. If your downstream tasks (next-token prediction on novel text, instruction-following, etc.) perform well, this bpb is a strong signal.
Quick check: What’s your test set bpb? If test bpb » val bpb, you have distribution shift or overfitting to watch.