Release validation
The public adapter SHA-256 is
c26031b9e1932ae0e76b29ec894b49bb6f719a9ff997cf733eae8bbf443f9fb2.
The loader refuses a different adapter checksum, parameter inventory or shape.
CPU checks — seven passed
- 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.
Allocated-GPU portability canary — four passed
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.
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.
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.