PRD — Export TinyGPT SAEs in SAELens / Neuronpedia format

Goal

Add tinygpt sae export --format saelens <input.sae> --out <dir> that converts our shipped .sae sidecar format to the SAELens on-disk format (Neuronpedia ingest target). One-way export only — we keep our format internally; SAELens / Neuronpedia get a consumable artifact.

This is the cheapest of the three interop options floated in PLAN B17:

• (a) keep ours, no interop — current state
• (b) port to SAELens — 1-2 weeks, throws out our shipped code
• (c) export only — 1-2 days, no internal disruption

(c) is what this PRD does.

Why now

• Our SAE infra exists and works (tinygpt sae, sae-explore). Whether to interop with the rest of the field is a UX question, not a science question.
• Neuronpedia is the public storefront for SAE feature dictionaries — if we ever want our findings cited beyond our docs, that’s the publication target.
• The format gap is just JSON metadata + safetensors weights with a specific naming convention. No re-training, no quality risk.

Scope — in

• Sources/TinyGPT/SaeExport.swift — new subcommand. Reads a .sae file, writes a SAELens-compatible directory:
  • cfg.json (architecture + training config — fields documented in SAELens README)
  • sae_weights.safetensors (encoder + decoder)
  • sparsity.safetensors (per-feature activation rate, computed from a calibration corpus or extracted from .sae if we logged it)
  • README.md per Neuronpedia’s required schema
• Reverse direction (SAELens → .sae) is out for this PRD — defer until we have a reason to consume their dictionaries.
• One round-trip test: export a known .sae, load via SAELens’ Python API in a Python subprocess (no Python source in our tree), assert encoder + decoder weights byte-equal after re-normalization to the same convention.

Scope — out

• Inbound import (SAELens → tinygpt). Defer.
• Cross-format feature explorer — keep our sae-explore; the exported SAEs live on Neuronpedia for cross-tool exploration.
• Auto-publish to Neuronpedia. Their API exists but ingestion goes through their PR queue; we’d write the artifact and the human uploads.

Files to touch

Acceptance criteria

• tinygpt sae export --format saelens existing.sae --out /tmp/exp/ produces cfg.json, sae_weights.safetensors, sparsity.safetensors, README.md.
• SAELens’ loader (called via a 5-line Python subprocess in CI) loads the export without error.
• Roundtrip test passes — exported encoder/decoder weights are numerically identical (modulo SAELens’s column-vs-row convention) to the originals.
• docs/interpretability.md has a worked example pointing at a fixture export.

Reference patterns

• SAELens repo, folder sae_lens/sae.py — the on-disk format definition. Cite, don’t re-document.
• Sources/TinyGPT/HfLoad.swift’s HF-tensors writer — the safetensors-writing template; we already produce .safetensors for HF-compatible model exports.
• Sources/TinyGPTModel/SafetensorsWriter.swift — shared utility, reuse.

Open questions

• Whether to emit per-feature human-readable IDs (the SAELens convention) or pure numeric ids. Recommendation: numeric only; feature naming is the user’s job, not a converter’s.