Trained adapters now embed (rank, alpha, target_modules, crate_version)
as JSON in safetensors __metadata__["rtx_csm_lora"]. apply_lora_adapter
reads it at load time so users no longer have to remember matching
--lora-rank/--lora-alpha/--extended-lora flags from training.
LoraAdapterMetadata::is_extended() heuristic: target_modules contains
any MLP path or output_proj or k_proj. Handles both the canonical
extended() preset and future custom configs that overlap it.
apply_lora_adapter rank/alpha/extended params became Option<_>
(None = use file metadata, Some = override). Both callers updated.
save_lora_adapter_with_metadata is the new path used by both trainers;
the plain save_lora_adapter still exists for the metadata-less case
(per-stage curriculum snapshots).
safetensors dep bumped 0.4 → 0.7 to match candle 0.9's transitive pin
so candle's Tensor: View impl is in scope for serialize_to_file
(candle's own save wrapper hardcodes the metadata arg to None).
Backward compat: pre-12.5 adapters load fine when explicit CLI flags
are passed; auto-detect path is skipped silently.
Verified end-to-end: trained adapter saved with metadata,
`generate --lora <path>` (no other flags) auto-detected
rank=8 alpha=16 extended=true and applied. Older metadata-less adapter
still loaded with explicit flags. 3 new unit tests; lib suite 99/99.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
Closes the personal_voice_training_guide.md §4 stack: capacity (12.1) +
control tokens (12.2) + multi-stage curriculum (this).
TrainingExample gains emotion_tag + stage. Trainer::train applies the
tag via the same apply_emotion_hint helper inference uses (now
pub(crate)) — training and inference must use identical prefix
formatting or the adapter won't transfer.
TrainingDataset::load_from_manifest reads JSONL
`{wav, transcript, emotion_tag?, stage?, speaker?}` rows; wav paths
resolve relative to manifest dir.
CurriculumStage + CurriculumTrainer run N stages sequentially against a
shared VarMap. Per stage: filter by ex.stage label, build a transient
sub-dataset, run Trainer, save snapshot if requested. The "*" stage
name is a global catch-all.
examples/lora_train_emotional.rs wraps the canonical 3-stage recipe:
audiobook (3 ep × lr 1e-4) → podcast (1 ep × lr 3e-5) → va (1 ep ×
lr 1e-5). --extended-lora recommended (FFN is the prosodic-style
carrier per the guide).
Verified end-to-end on Metal: 3-row manifest → all 3 stages execute,
checkpoints + final adapter written, prompt-token lengths varied by
emotion-tag length (9 vs 11 for different tags) confirming the tag
flowed through the training tokenization. Lib suite 96/96.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
