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(#39)
* Add Lower LR submission: val_bpb=1.2230 (MATRIX_LR=0.02)
Systematic LR sweep showed default Muon/Adam learning rates (0.04) were
too high. MATRIX_LR=0.02, SCALAR_LR=0.02, TIED_EMBED_LR=0.03 gives
consistent improvement. Same 9L/512d architecture, no other changes.
* Add 10L Mixed Precision submission: val_bpb=1.2147
10 transformer layers (vs baseline 9) with mixed int8/int6 compression:
- Full int8 for first/last 3 layers (precision-sensitive)
- Int6 (step=4 rounding) for middle layers 3-6 (compression-friendly)
- Lower LR: MATRIX_LR=0.02, SCALAR_LR=0.02, TIED_EMBED_LR=0.03
- Artifact: 15,928,974 bytes (under 16MB cap)
- Improvement: 0.0097 bpb / 0.0217 nats over baseline (1.2244)
Also adds PRUNE_RATIO and INT4_LAYERS/INT4_STEP support to train_gpt.py
for mixed-precision post-training quantization.
* Revert root train_gpt.py to upstream baseline
The root script should remain the baseline. Submission-specific
modifications (PRUNE_RATIO, INT4_LAYERS, INT4_STEP) only belong
in the records/ folder copy.
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* Warmdown-quantization co-optimization, val_bpb=1.2154
Novel finding: aggressive LR decay (WARMDOWN_ITERS=20000) reduces int8 quantization
penalty from 0.014 to 0.005 BPB. Combined with FP16 tied embeddings and moderate
NTK-RoPE extrapolation (eval@1408).
Full warmdown sweep across 10 values and detailed analysis in README.
* breakthrough: 1.1574 BPB via int6 + MLP 3x + sliding window stride=256
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Co-authored-by: Sam Larson <saml212@users.noreply.github.com>
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(val_bpb=1.1748) (#60)
* Add NTK Eval + Overtone Init submission (1.2160 BPB)
Train@1024 with overtone embedding init and phase-transition residual
mixing, eval@2048 with NTK-aware dynamic RoPE scaling. Mean val_bpb
1.2160 across 3 seeds (p=0.0012 for 0.0194-nat improvement over baseline).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Update submission: Muon WD + NTK Eval + Overtone Init (1.2094 BPB, p=0.0002)
* Update submission: 10-Layer + Muon WD + NTK Eval + Overtone Init (1.2029 BPB, p=0.0006)
* Update submission: FP16 Embed + 10L + Muon WD + NTK + Overtone (1.2008 BPB)
* Update submission: 1.2000 BPB — FP16 Embed + 10L + Muon WD + NTK@1408 + Overtone
* Update: 1.1748 BPB — Sliding Window + FP16 Embed + 10L + Muon WD + Overtone
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Co-authored-by: notapplica <notapplica@users.noreply.github.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Co-authored-by: spokane-way <spokane@way>
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The window_starts filter dropped windows shorter than stride,
silently skipping up to (stride-1) tokens at the end of the
validation set. Now includes all windows with >= 1 scoreable
token, and clamps the score start for short final windows.
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* SOTA attempt
* Improve score on SXM
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Co-authored-by: spokane-way <spokane@way>
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keep tok_emb.weight in fp16 during int8 export (kills the quant gap),
shrink MLP hidden to 992 to fit under 16MB, bump warmdown to 3600
and matrix LR to 0.06.
tested on 8xH100 SXM (2 seeds) and 8xH200 SXM (3 seeds).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Use eager mx.eval() to fix running train script on 16GB Mac devices
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force-evaluating the graph after each sub-batch step
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Update README.md
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Fix MLX multi-batch validation memory growth
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Update README typo
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MLX Timing Mismatch with Main Script
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