Non-record: QNA + SQWA compression thesis (8xH100 SXM)

[Abhishek8108]

Summary

Non-record submission documenting a controlled 3-run ablation on 8xH100 SXM testing whether training for the quantized artifact directly improves post-quantization BPB.

• Quantization Noise Annealing (QNA): Inject uniform noise matching int6 quant error during training, scaled by LR. Teaches weight robustness to quantization from the start.
• Stochastic Quantized Weight Averaging (SQWA): Average quantize-dequantized EMA snapshots during warmdown, ensuring the final model lives in the quant-friendly subspace.

Results

Run	Config	Sliding Window val_bpb	Quant Gap (float→int6)	Artifact Size
1	Baseline	1.1216	0.0081	15.93 MB
2	+QNA	1.1222	0.0075	15.95 MB
3	+QNA+SQWA	1.1258	0.0028	16.15 MB

Interesting negative result: Both techniques work mechanistically — QNA reduced quant gap by 7%, SQWA by 65%. But neither improved the final leaderboard metric. The bottleneck in current SOTA is float model quality, not quantization error.

Key takeaway

With a baseline quant gap of only ~0.008 BPB, the existing late QAT already handles quantization well enough. Future improvements should target the float model directly rather than compression alignment.

Files

• train_gpt.py — full script with QNA/SQWA (env-var toggleable, defaults off)
• run1_base.log, run2_qna.log, run3_qna_sqwa.log — complete training logs
• README.md — detailed write-up with implementation details and analysis
• submission.json — metadata