Non-record: GPTQ-lite Scale Clamp Fix + 6-bit Packing + Depth Recurrence on Stack B

records/track_non_record_16mb/2026-04-05_StackB_GPTQLite_DepthRecurrence/README.md

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+# Stack B + GPTQ-lite (Fixed Scale Clamp) + Depth Recurrence + 6-bit Packing
+
+Non-record submission. Three quantization insights that may help others building on the Stack B foundation.
+
+## Foundation
+
+Built on the Stack B architecture (PR #1218 / #1260 lineage):
+- 11 transformer layers, 512 model dim, 4096 vocab (sp4096 tokenizer)
+- 4x MLP expansion (2048 hidden), GQA (8 heads / 4 KV heads)
+- LeakyReLU(0.5) squared, partial RoPE (16/64), LN scale (1/sqrt(layer+1))
+- XSA on all 11 layers, EMA (decay=0.997), MuonEq-R
+- Depth recurrence: layers 4,5 share MLP weights
+- Decoupled weight decay 0.085
+
+## Quantization Contributions
+
+### 1. GPTQ-lite Scale Clamp Bug Fix
+
+The original GPTQ-lite (as used in several leaderboard entries) computes per-row scales as:
+
+```python
+scale = (row_clip / clip_range).clamp_min(1 / clip_range)
+```
+
+For int6 with `clip_range=31`, the minimum scale is `1/31 ~ 0.032`. But typical weight row
+maxima are `O(0.01-0.05)`, meaning the clamp fires on most rows and forces scales ~16x too
+large. This wastes ~90% of the available quantization dynamic range.
+
+**Fix:** Use `clamp_min(1e-7)` (same as the int8 path). This allows scales to track actual
+weight magnitudes. The fix is one line but affects every int6-quantized tensor.
+
+### 2. 6-bit Packing
+
+Standard int6 stored in int8 wastes 25% of every byte. We pack 4 int6 values into 3 bytes:
+
+```
+byte0 = a[0:6] | b[0:2] << 6
+byte1 = b[2:6] | c[0:4] << 4
+byte2 = c[4:6] | d[0:6] << 2
+```
+
+This is a 25% payload reduction for all int6 tensors, which directly helps fit under the
+16MB artifact limit. The pack/unpack routines are ~10 lines each and add negligible eval-time
+overhead.
+
+### 3. Forced Int8 for Depth-Recurrence Shared Layers
+
+When layers share weights (depth recurrence), quantization error compounds through each
+reuse. A weight matrix used twice has its quantization error applied twice to the forward
+pass. For layers 4 and 5 sharing MLP weights, int6 quantization (6-bit, clip_range=31)
+produces enough error to measurably degrade output quality.
+
+**Solution:** Force int8 (127 levels) for shared layers while keeping int6 for non-shared
+layers. The extra bytes for 2 layers at int8 vs int6 are small compared to the quality gain.
+
+This is controlled via the `int8_forced_layers` parameter in `quantize_state_dict_mixed()`.
+
+## Results (1xH100 NVL, undertrained)
+
+717 training steps on 1xH100 NVL (~3.5% of the 20,000 step target on 8xH100):
+
+| Metric | Value |
+|--------|-------|
+| Pre-quant val BPB | 1.377 |
+| Post-quant val BPB (mixed int6/int8) | 1.727 |
+| Quant degradation | +0.350 BPB |
+
+The large quantization gap is expected for undertrained models. Fully-trained weights settle
+into more compressible distributions where the int6 degradation is much smaller (typically
++0.01-0.03 BPB based on prior entries).
+
+No 8xH100 run has been performed yet. Expected full-training result: ~1.10-1.12 BPB
+post-quantization.
+
+## Usage
+
+```bash
+pip install -r requirements.txt
+
+# Single GPU (testing)
+torchrun --nproc_per_node=1 train_gpt.py
+
+# 8xH100 SXM (competition)
+torchrun --nproc_per_node=8 train_gpt.py
+```
+
+All techniques are ON by default. Override via environment variables (e.g., `GPTQ_LITE=0`
+to use standard int8, `DEPTH_RECURRENCE_LAYERS=""` to disable weight sharing).
+
+## Credits
+
+- Stack B architecture: PR #1218, PR #1260
+- MuonEq-R: PR #1260 by @signalrush
+- Depth recurrence concept: PR #1260, inspired by Universal Transformer
+- GPTQ-lite baseline: PR #374, PR #1019
+- XSA: @abaybektursun
+- LeakyReLU squared: PR #493 by @parinzee, PR #518 by @sofiabod

records/track_non_record_16mb/2026-04-05_StackB_GPTQLite_DepthRecurrence/requirements.txt

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+torch>=2.5.0
+numpy
+sentencepiece
+zstandard

records/track_non_record_16mb/2026-04-05_StackB_GPTQLite_DepthRecurrence/submission.json

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+{
+  "track": "non_record_16mb",
+  "date": "2026-04-05",
+  "name": "Stack B + GPTQ-lite (Fixed Scale Clamp) + Depth Recurrence + 6-bit Packing",
+  "author": "Rome-1",
+  "github_id": "Rome-1",
+  "val_bpb": null,
+  "bytes_total": null,
+  "blurb": "Stack B foundation (11L/512d/4096vocab/4xMLP) with three quantization contributions: (1) fix for GPTQ-lite scale clamp bug that wastes 90% of dynamic range, (2) 6-bit packing (4 int6 values -> 3 bytes = 25% payload reduction), (3) forced int8 for depth-recurrence shared layers where quantization error amplifies through reuse. Non-record: no 8xH100 run yet, validated on 1xH100 NVL (717 steps, undertrained).",
+  "hardware": "1xH100 NVL (undertrained, 717 steps)"
+}