status string | commit string | runner_sha string | plan_sha string | total_runtime_s float64 | mimi_device string | four_cell_cb0 dict | g2_diff_in_diffs_D float64 | g2_survival_fraction float64 | interpretation string |
|---|---|---|---|---|---|---|---|---|---|
C7_RETRAIN_VCTK_LORA_ADAPTATION_COMPLETE | 66066ca | d8013be6e9d03a26194a72d8f3b098e8d826bdb7a4f1f915e578a934eba8956e | 1fb2930c4e7b061b4718794f715627dfc0db851fa5e40f8ca80da2248ea5f0c1 | 5,971.323 | cpu | {
"control_seen": 0.649775,
"control_novel": 0.663079,
"lora_seen": 0.202356,
"lora_novel": 0.202621
} | 0.013039 | 1.029143 | D > 0 meaningfully + survival ≈ 1: LoRA gain is genuine content-disjoint generalization, not just content-recognition |
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Check out the documentation for more information.
C7 VCTK Retrain
Seam-3 C7-retrain VCTK content-disjoint evaluation.
Design
- Retrain from scratch on content-reserved train_4000 (40 text_ids R reserved for novel eval)
- Two arms within run:
- control_no_lora: LoRA attached but frozen, AF+DT trainable
- lora_trainable: q/v r16 LoRA trainable, AF+DT trainable
- Eval on same 25 heldout speakers with:
- content_seen: 512 windows, text_ids NOT in R (seen during training)
- content_novel: 512 windows, text_ids IN R (unseen during training)
- Primary metric: diff-in-diffs D = (control_novel − lora_novel) − (control_seen − lora_seen)
Results
| Cell | cb0_real_over_baseline |
|---|---|
| control_seen | 0.650 |
| control_novel | 0.663 |
| lora_seen | 0.202 |
| lora_novel | 0.203 |
| D | 0.013 (positive) |
| Survival | 1.029 |
Interpretation
D > 0 and survival ≈ 1: LoRA gain genuinely generalizes to novel (never-trained) text_ids. Not just content-recognition. The C6 LoRA effect survives within-VCTK content-disjoint eval.
Artifacts
- c7_retrain_essential.tar.gz: report.json, report.md, checkpoints/
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