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Author SHA1 Message Date
vh 7db6c44bcd feat(r49-prep): author-voice LoRA regime prep on gx10 — carriers staged, throughput measured, adapters secured
Prep for the BabyBronte / brokkr-smithy R49 author-voice adapter regime, plus
the operator's "keep the adapter" ruling made durable.

Measured on pfi-gx10 (GB10, sm_121), n=10 per arm after 3 warmup steps, seq
4096, LoRA r=32 on q/k/v/o + MLP, bf16, sdpa, grad-checkpointing on:

  Qwen3-0.6B-Base    dense    0.616 B   1.707 s/step   2,399 tok/s
  Qwen3-1.7B-Base    dense    1.755 B   2.895 s/step   1,415 tok/s
  Qwen3.5-0.8B-Base  hybrid   0.765 B   7.581 s/step     540 tok/s

The dense 1.755 B carrier trains 2.6x faster than the hybrid 0.765 B one on 2.3x
the parameters (~6x per parameter), with more LoRA modules adapted (196 vs 96).
Spreads of 0.6-2.6% put instrument noise an order of magnitude below the effect.
Cause: Qwen3.5 is 18 linear-attention (SSM) layers to 6 attention, and no fused
linear-attention kernel is installed on the box. Grad checkpointing is not the
culprit (19%, and saves 2.6x memory). Batching is not the lever for either
family -- both sit at this box's roofline at batch 1.

Projected per voice on a Brontë-scale corpus: dense 0.6B 2.7 h, dense 1.7B
4.6 h, hybrid 0.8B 12 h. The hybrid would take longer than the 7 h 26B-A4B tune
the regime exists to replace, so the carrier family is now an open decision with
a recommendation for the dense Qwen3 line -- the design doc's original pin.

Two further Qwen3.5 findings, both measured rather than read off the config: the
Base checkpoints ship a vision tower (153/297 model.visual.* Linear tensors that
target_modules="all-linear" would train on text) and an MTP head, both dropped
for free by loading through AutoModelForCausalLM -- which renames modules
relative to the vLLM serving path, so adapter binding needs the
sampled-target-changed check on the serving side; and cross-document packing is
unsafe because SSM state ignores the attention mask, breaking the per-copy
name-consistency invariant the design doc calls sacred. Neither exists on dense.

Adapter disposition, per the operator's ruling: all five gx10-resident ERP
adapters (run-03c/04/05/06/07) mirrored to ana-ml2:/tank/erp-tune/run-<N>/adapter
matching the layout runs 01-03 already used, byte-totals identical both sides and
sha256 matching on every adapter_model.safetensors. /tank/* is deliberately
excluded from ana-ml2's restic sources, so the profile gains one documented
carve-out for /tank/erp-tune/run-*/adapter, verified by resticprofile --dry-run
to expand to exactly those eight paths.

Nothing is training and nothing is queued.
2026-09-09 22:41:47 -07:00