dense/TRAINING_PLAN.md

Dense Training Plan

Setup

Use the same JSONL checkpoint bundle as MoE. Keep random seed, batch token target, learning-rate schedule, and validation split aligned.

Primary input:

dataset/<checkpoint>/dataset/*.jsonl

Do not train from hidden staging folders unless rebuilding checkpoints. Hidden data/curated_upload is a staging queue, not the training contract.

Curriculum

1. Start with short and medium records.
2. Add long FIM examples.
3. Add continuation/code generation records.
4. Evaluate FIM and next-token loss separately.

H100 Defaults

• Precision: bf16.
• Context: 2048 smoke, 4096 main run.
• Optimizer: AdamW or fused AdamW.
• LR schedule: warmup then cosine decay.
• Gradient clipping: enabled.
• Batch target: choose by total tokens, not examples, because FIM lengths vary.
• Validation: fixed files or fixed checksums from the checkpoint report.

Preprocessing

• Preserve whitespace exactly.
• Append <|endoftext|> between records during packing.
• Keep FIM markers visible; do not strip special tokens.
• Reject empty text, invalid JSONL, and records exceeding context unless deterministic chunking is enabled.
• Bucket by token length to reduce padding waste.

Stop Conditions

• Validation loss diverges twice after LR reduction.
• FIM metrics regress while train loss improves.
• Tokenizer round-trip or special-token audit fails.
• Syntax parse rate drops while raw loss improves.