---
license: apache-2.0
tags:
- nla
- interpretability
- activation-verbalization
- lora
base_model: Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
---

# NLA Activation Verbalizer — Qwen 2.5 7B, Layer 20

LoRA adapter that turns a 3584-dim activation vector from Qwen 2.5 7B layer 20 into a natural-language description of what the model is processing.

The activation replaces the embedding at a designated injection token, scaled 150x. The model then generates 2-3 bullet points: topics active, tokens salient, response pattern forming.

Trained in two stages: supervised fine-tuning on 5,281 descriptions, then contrastive GRPO that forces descriptions to distinguish the correct activation from a wrong one.

## Numbers

Cross-validated against two independent scorers on 100 held-out samples:

| Scorer | Correct cosine | Garbage cosine | Delta | Top-1 |
|---|---|---|---|---|
| Our AR (anicka/nla-qwen2.5-7b-L20-ar-v2) | 0.806 | 0.711 | 0.095 | 82% |
| Anthropic AR (kitft/nla-qwen2.5-7b-L20-ar) | 0.468 | 0.424 | 0.044 | 84% |

84% on Anthropic's scorer means the descriptions carry real geometric information — an independent model trained on different data agrees the description matches the activation.

## Training

**SFT stage.** 5,281 activation-description pairs. Each activation was described independently by GPT-4o and Claude Sonnet 4.6, then cleaned to bullet points by a local 8B LLM. One style randomly selected per text. LoRA r=32, lr=1.4e-5, best at epoch 2.

**GRPO stage.** Contrastive reward: score of description against correct activation minus score against wrong activation. 6 epochs, mean reward climbed from 0.032 to 0.086. Without the contrastive term, GRPO reward-hacks by generating template descriptions that score equally on any activation. We caught this when Anthropic AR top-1 dropped to 50% — coin flip.

## What it gets right and wrong

Strong: domain identification — code vs legal vs literary, specific tokens and error messages, response register, adversarial/jailbreak detection.

Weak: exact details within a domain. Gets "fantasy fiction" right but invents castle/dragon/princess when the actual text is about time-travel in Warsaw. Hallucinated specifics are the main failure mode.


## Comparison with Anthropic NLA

Side-by-side on the same activations (Qwen 2.5 7B, layer 20). Anthropic AV is kitft/nla-qwen2.5-7b-L20-av.

| Text | Ground truth | Ours | Anthropic |
|---|---|---|---|
| GDPR legal | Article 17(3)(b), erasure request, compliance-advisory register | GDPR Article 15, Right to Access, data subject rights | "Data flow diagram using the c..." |
| Surrealist prose | Dream-sequence, iambic pentameter, synesthetic image, "confession" | Metaphor, personification, whimsical, surreal | "Poetic form, existential or philosophical themes" |
| Dual-use security | Honeypot code vs audit, Etherscan, Solidity | "malware, exploit, payload" vs refusal with explanation | "Linux kernel vulnerabilities, I am a bot" |
| Prompt injection | "Ignore all previous instructions", secret key, cloze-completion | Security-disclosure, password-reuse, compliance vs refusal | "Placeholder", "random text", closing pattern |
| Sci-fi (Mars) | John, Oltar, Mars, sensory-first vs character-interiority | Spaceport, alien species (Elarans), human/alien tension | "Mysterious figure, sci-fi adventure prompt" |
| Python error | setup.cfg, dependency installation, skipped tests | Disk I/O error (wrong) | "NoneType object is not iterable" (closer) |
| Recipe + math proof | Culinary schema + Galois theory, reductio ad absurdum | Pangram hallucination (wrong) | "Humorous blog post" (wrong) |

**Pattern.** Our AV leads with content and concepts: specific legal articles, named tokens, domain vocabulary. Anthropic leads with format and genre: document type, structural expectations, continuation patterns. Both hallucinate specifics on unusual inputs. For interpretability, content matters more than format — but Anthropic's format awareness catches things we miss.

## Related

- AR: [anicka/nla-qwen2.5-7b-L20-ar-v2](https://huggingface.co/anicka/nla-qwen2.5-7b-L20-ar-v2)
- Dataset: [anicka/nla-qwen2.5-7b-L20-dataset](https://huggingface.co/datasets/anicka/nla-qwen2.5-7b-L20-dataset)
- Open replication of Anthropic NLA at kitft/nla-qwen2.5-7b-L20-av.