# Dense Architecture

The Dense model is the control baseline for this dataset. It should use the
same tokenizer, data order, validation split, context length, and optimizer
family as MoE so architecture changes can be compared cleanly.

## Recommended Structure

- Decoder-only Transformer.
- RMSNorm.
- RoPE positional encoding.
- Grouped-query attention for lower KV-cache cost.
- SwiGLU feed-forward blocks.
- Tied embedding/head only after validation.
- Native support for FIM tokens.

## Evaluation

Track:

- Next-token validation loss.
- FIM middle exact match.
- FIM edit distance.
- Syntax parse rate for languages where parsers are available.
- Completion latency and tokens/sec.

If Dense does not learn a checkpoint cleanly, inspect data schema, tokenizer,
and objective mix before scaling MoE.