---
license: apache-2.0
base_model: Qwen/Qwen3-8B
library_name: transformers
pipeline_tag: text-generation
tags:
  - interpretability
  - natural-language-autoencoder
  - nla
  - activation-verbalizer
  - qwen3
datasets:
  - ceselder/qwen3-8b-nla-L24-finefineweb-100k
language:
  - en
---

# nanoNLA — Qwen3-8B Activation Verbalizer (Layer 24, Karvonen injection)

The **activation-verbalizer (AV)** half of a
[Natural Language Autoencoder](https://transformer-circuits.pub/2026/nla/index.html)
for **Qwen3-8B**, layer **24**. Hand it a residual-stream activation vector and it
describes, in natural language, what that activation represents.

This is the **AV-SFT** checkpoint — the supervised warm-start stage (before RL) —
trained with the **Karvonen norm-matched injection** formula.

- 🧠 **Code / training pipeline:** [github.com/ceselder/nanoNLA](https://github.com/ceselder/nanoNLA)
- 📚 **Docs:** [design](https://github.com/ceselder/nanoNLA/blob/main/docs/design.md) ·
  [inference](https://github.com/ceselder/nanoNLA/blob/main/docs/inference.md) ·
  [setup](https://github.com/ceselder/nanoNLA/blob/main/docs/setup.md) ·
  [Qwen3-8B run notes](https://github.com/ceselder/nanoNLA/blob/main/docs/qwen3_8b_run.md)
- 🗂️ **Warm-start dataset:** [ceselder/qwen3-8b-nla-L24-finefineweb-100k](https://huggingface.co/datasets/ceselder/qwen3-8b-nla-L24-finefineweb-100k)
- 📄 **Paper:** *Natural Language Autoencoders Produce Unsupervised Explanations of LLM Activations*
  (Fraser-Taliente et al., Transformer Circuits 2026) —
  [paper](https://transformer-circuits.pub/2026/nla/index.html) ·
  [blog](https://www.anthropic.com/research/natural-language-autoencoders)

---

## What this is

A Natural Language Autoencoder is a pair of fine-tuned LMs that map residual-stream
activations to language and back:

| direction | model | mechanism |
|---|---|---|
| activation → text | **AV (this model)** | inject the vector at a marker token, autoregress an explanation |
| text → activation | AR | truncated K+1-layer copy of the base + `Linear(d, d)` readout (reconstruction) |

This repo hosts the **AV** for Qwen3-8B @ layer 24.

## ⚠️ This checkpoint uses the Karvonen injection formula

It was trained with the **Karvonen norm-matched additive injection** at the marker
token (a layer-1 residual-stream hook):

```
h'_p = h_p + ‖h_p‖ · v / ‖v‖
```

where `v` is the layer-24 activation you want verbalized. **Serve it with the same
injection** — *not* the paper's embedding-replacement default. In the
[nanoNLA](https://github.com/ceselder/nanoNLA) code this is the Karvonen path; see
[`docs/qwen3_8b_run.md`](https://github.com/ceselder/nanoNLA/blob/main/docs/qwen3_8b_run.md).
The marker token, injection/MSE scales, and `d_model` are all read from the run's
`nla_meta.yaml` sidecar — never hardcode them.

## Usage

See [`docs/inference.md`](https://github.com/ceselder/nanoNLA/blob/main/docs/inference.md)
for the full harness (marker detection, neighbor-anchoring, the Karvonen hook). Sketch:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("ceselder/nanoNLA", torch_dtype="bfloat16")
tok   = AutoTokenizer.from_pretrained("ceselder/nanoNLA")
# 1. register the Karvonen layer-1 injection hook with your layer-24 activation `v`
# 2. put the marker token in the prompt (neighbor-anchored)
# 3. generate the explanation
# (full, correct hook + prompt template live in the nanoNLA repo)
```

## Training

- **Base:** [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) — fine-tuned; merged bf16 weights provided here
- **Layer:** 24 (residual stream)
- **Stage:** AV-SFT (warm-start), 1000 steps
- **Injection:** Karvonen norm-matched additive (layer-1 hook)
- **Warm-start labels:** explanations over
  [qwen3-8b-nla-L24-finefineweb-100k](https://huggingface.co/datasets/ceselder/qwen3-8b-nla-L24-finefineweb-100k)
- **Peak LR:** 3e-5 (cosine)

Exact recipe (rank/scales/hparams) and eval numbers are in the
[nanoNLA repo](https://github.com/ceselder/nanoNLA) and
[`docs/qwen3_8b_run.md`](https://github.com/ceselder/nanoNLA/blob/main/docs/qwen3_8b_run.md).

## Caveats

Research checkpoint, not a product. This is the **SFT** stage (pre-RL), so the AV
produces plausible-but-imperfect explanations. Whether an NLA's explanations are
*faithful* to the underlying computation is an open research question — see the paper.

## Citation

```bibtex
@article{frasertaliente2026nla,
  title   = {Natural Language Autoencoders Produce Unsupervised Explanations of LLM Activations},
  author  = {Fraser-Taliente, others},
  journal = {Transformer Circuits Thread},
  year    = {2026},
  url     = {https://transformer-circuits.pub/2026/nla/index.html}
}
```