Configuration Audit Matches Recommendations

Configuration Audit Matches Recommendations | Generated by AI

Home 2026.05

Audited the config against both Q&A docs. Nothing material to change — no re-smoke needed.

What the Q&A recommends vs. what’s already set

Recommendation	Current	Status
Keep `block_size=1024`	1024	✓
`batch_size=4` (8 OOMs)	4	✓
`grad_accum=128` → 524k tok/step	128	✓
`compile=True`	True	✓
`always_save_checkpoint=True`, `eval_interval=500` (resumable)	True / 500	✓
`max_iters=19073` baseline	19073	✓
`warmup_iters` ≈ 715	715	✓
`lr=6e-4`, `min_lr=6e-5`, `beta2=0.95`, `wd=0.1`, `grad_clip=1.0`	all match	✓

Silent defaults — verified active (the Q&A flagged these as “make sure”)

PyTorch 2.10.0 + CUDA 12.8 ⇒ flash attention via SDPA enabled ✓
model.py:69 uses F.scaled_dot_product_attention(...) ✓
bf16 auto-selected on Ada (4070): smoke log showed GradScaler(enabled=False) ⇒ bf16 path is in use, no scaler overhead ✓
5.6 GB VRAM headroom confirmed at batch=4

Optional micro-tweaks (not required, no re-smoke)

log_interval=20 ⇒ ~3.5 min between log lines. If you want denser visibility for the first few hours, override with --log_interval=10 on the command line. Trivial change, no need to re-smoke.
eval_iters=100 ⇒ ~9s per eval every 500 iters (≈0.2% overhead). Fine.

Conclusion

Your config already implements every recommendation from both Q&A docs, and the platform-level wins they mention (bf16 + flash SDPA + compile) are all confirmed active. The earlier smoke run validated:

Shards load, vocab right (50304)
No OOM at the chosen batch/grad-accum
Loss decreasing (11.04 → 10.56 over 4 iters)
~11s/iter projected at the real grad_accum=128

Launch in another terminal — same command as before:

cd /mnt/data/nanoGPT
tmux new -s nano
source venv/bin/activate
PYTORCH_ALLOC_CONF=expandable_segments:True \
python -u train.py config/train_fineweb_gpt3.py 2>&1 \
  | tee -a out-fineweb-gpt3/train.log

For the “fluent text first” 5k-iter checkpoint strategy from Q&A #2, append --max_iters=5000 --lr_decay_iters=5000 --warmup_iters=200 — then --init_from=resume later to continue toward 19k.

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