v0.0.1 — First independent open-source NLA

First open-source Natural Language Autoencoder (NLA) released independently of Anthropic's NLA team. Gemma-4-E2B AV+AR pair trained on a 4 GB consumer GPU.

Honest performance

• Round-trip cosine similarity: 0.438 ± 0.054 on n=42 held-out OpenWebText activations
• 42 of 42 rows clear the 0.30 noise-floor threshold
• Min cos 0.313, max 0.558
• Well below Anthropic's published 7B variants (cos ~0.7+) — framed as methodology validation at small-model scale, not numbers-parity

What ships

Models (HuggingFace):

• Solshine/gemma-4-e2b-nla-L23-av-v0_0_1 — AV (Actor) adapter
• Solshine/gemma-4-e2b-nla-L23-ar-v0_0_1 — AR (Critic) adapter + linear head

Datasets (HuggingFace):

• Solshine/gemma-4-e2b-nla-ar_sft-v0_0_x-haiku-persona-audit — 696-row AR-SFT corpus
• Solshine/gemma-4-e2b-nla-av_sft-v0_1_x-gemini-persona-audit — 4,734-row diversified AV-SFT corpus

In this repo:

• README.md, MODEL_CARD_AV.md, MODEL_CARD_AR.md
• lesswrong_post.md (the announcement)
• figures/ (6 release figures)
• examples/eval_round_trip.py
• LICENSE (CC BY 4.0)

Source repo (full reproducibility)

SolshineCode/deception-nanochat-sae-research — every script, every prompt, every result.

Methodology citation

Fraser-Taliente, K., et al. (2026). Natural Language Autoencoders. https://transformer-circuits.pub/2026/nla/

The Anthropic reference repo is kitft/natural_language_autoencoders.