| # Release validation |
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|
| The public adapter SHA-256 is |
| `c26031b9e1932ae0e76b29ec894b49bb6f719a9ff997cf733eae8bbf443f9fb2`. |
| The loader refuses a different adapter checksum, parameter inventory or shape. |
|
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| ## CPU checks — seven passed |
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| - Exact adapter SHA and 292 FP32 tensor inventory. |
| - Rank-128 A/B pairs totaling 96,337,920 scalars. |
| - Full 146 attention-projection base skeleton on the meta device: every inserted |
| target name/shape matches the real saved adapter, without allocating model weights. |
| - Zero-initialized residual and FP32 adapter storage. |
| - Finite component-normalized advantages with invalid candidates excluded. |
| - Warmup/cosine schedule endpoints. |
| - Finite VESPO policy-loss gradients. |
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| ## Allocated-GPU portability canary — four passed |
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|
| The **new portable inference loader**, pinned local base copy, exact adapter and |
| external codec were exercised on four GH200 GPUs under Slurm. Two short English |
| and two short German generations cover both reference/no-reference paths. |
| Each produced finite 48-kHz mono audio with 12 valid codec indices per frame. |
| Outputs were kept in four SQLite containers, not loose audio files. |
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| The tested runtime was PyTorch 2.9.1+cu128, CUDA 12.8 and Transformers 5.14.1. |
| The base loader uses BF16 SDPA; custom adapter multiplication remains FP32 at |
| scale 1.0. A generic upstream missing-key warning concerns explicitly tied text |
| and codebook output heads (the base implementation ties them to embeddings), |
| not missing adapter tensors; all 292 adapter tensor names/shapes are checked. |
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| This is a functional loading/generation test, **not** a subjective quality |
| evaluation or proof of voice identity. The separately completed 70-case matched |
| listening evaluation provides the aggregate quality numbers in the model card. |
| No fresh GPU training smoke is claimed for the public portable trainer's |
| additional warm-start argument; original scientific core files are unchanged, |
| while portability differences are documented in TRAINING.md. |
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|
| See [machine-readable validation](stats/validation.json). |
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