PRD — DoRA on-disk adapter format

Goal

DoRA already trains in-session under tinygpt sft (default PEFT variant since 2026-05; PeftVariants.swift). It does NOT yet persist to disk in a reusable format — the adapter lives only in the running process’s parameters, so finishing an sft run with DoRA produces no shippable artifact. Ship the on-disk format + reader so DoRA reaches parity with LoRA / LoRA+ / VeRA (all of which already roundtrip).

Why now

• This is the highest-friction PEFT path: DoRA is the default and the one users hit first, and right now you can’t save what you trained. Diagnosed in factory-dora-serialization.md but not closed out.
• LoRA’s adapter format is well-defined (LoraAdapterReader.swift). DoRA needs the same shape plus the per-row magnitude vector m that distinguishes DoRA from LoRA. Three small fields, well-bounded.
• Specialists ship LoRA-style adapters to consumers; DoRA’s slight quality edge over LoRA goes away if we can’t deliver the artifact.

Scope — in

• Extend the existing .lora adapter format with two optional fields:
  • dora_magnitude: [Float] per-row magnitude vector (one per Linear in the adapted set)
  • peft_variant: "lora" | "dora" | "lora+" | ... discriminator string Backwards-compat: a .lora without peft_variant defaults to lora, so existing adapters still load.
• LoraAdapterReader.read(...) learns to apply the magnitude when peft_variant == "dora". New name kept for backcompat: PeftAdapter as a module-level alias may come later, but not in this PRD.
• tinygpt sft --peft dora ... --out adapter.lora actually writes a loadable adapter at the end of training. Today it prints “DoRA adapter not yet serializable” — that warning gets deleted.
• tinygpt compare base.tinygpt + adapter.lora vs base.tinygpt (the existing compare CLI) gives a real per-eval delta now that adapters are reloadable.

Scope — out

• Brand-new file extension (.dora). Same envelope, discriminator field — keeps the registry small. (factory-dora-serialization.md considered this and rejected; honor.)
• GGUF-style adapter export — that’s an interop story for HF consumers, not us today.
• Adapter merging (DoRA-on-DoRA stacking) — niche, defer.

Files to touch

Don’t touch

• LoRA / LoRA+ / VeRA save paths — extend LoraAdapterReader/Writer without forking. The magnitude field is optional, so other variants are unaffected.

Acceptance criteria

• tinygpt sft --peft dora --out /tmp/a.lora ... produces a file with peft_variant: "dora" and a non-empty dora_magnitude block.
• tinygpt sample <base> --lora /tmp/a.lora loads, runs, and produces token-identical output at T=0 to in-memory DoRA after the same SFT step count.
• DoRARoundtripTests.swift passes — save → reload → forward matches step-end logits to ε=1e-5.
• tinygpt compare base.tinygpt + a.lora vs base.tinygpt on a 200- prompt eval shows a non-zero delta consistent with the in-memory DoRA’s effect.

Reference patterns

• Sources/TinyGPTModel/LoraAdapterReader.swift — direct template; the safetensors-style binary envelope.
• Sources/TinyGPTModel/PeftVariants.swift — dora is in-session today, so the parameter shapes are settled; just need to expose m for serialization.
• factory-dora-serialization.md — note the prior reasoning. Don’t redo the format-choice argument; reference.

Open questions

• Whether magnitude should be stored as fp16 or fp32. Recommendation: fp32 since it’s one row per Linear (tiny — kilobytes for a 24-layer model), and DoRA quality is sensitive to magnitude precision per the original paper.