dense/README.md

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.
• Untied embedding/head for the first quality run; tie only if validation shows no FIM regression.
• Native support for FIM tokens.
• QK norm for attention stability at longer context.
• Residual branch init scaling so deeper runs do not spike early loss.

Baseline Shape

Use Dense as the clean control model, not as the final scaling target.

• Small smoke: 12-16 layers, 768-1024 hidden, 8-16 heads, context 2048.
• H100 baseline: 24-32 layers, 2048-3072 hidden, GQA, context 4096.
• Keep the tokenizer, file order, validation split, optimizer, and batch-token target identical to MoE.

The first Dense run should answer one question: does the dataset/tokenizer learn cleanly without router complexity? If not, do not scale MoE yet.

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.