Datasets:
File size: 1,448 Bytes
ddfe382 7056f24 ddfe382 7056f24 ddfe382 7056f24 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | # 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:
```text
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.
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