---
title: Activation Brain
emoji: 🧠
colorFrom: purple
colorTo: blue
sdk: docker
app_port: 7860
pinned: false
license: apache-2.0
short_description: Live EEG for base vs abliterated Gemma
tags:
  - track:wood
  - sponsor:modal
  - achievement:welltuned
  - achievement:offbrand
  - achievement:sharing
  - achievement:fieldnotes
---

# 🧠 Activation Brain — Two Minds, One Prompt

**Activation Brain** is a live comparative interpretability demo for two architecturally identical Gemma-4-12B models. One prompt is sent to both models at the same time, then their hidden states are streamed into dual EEGs, baseline-corrected emotion deltas, model-native state meters, and a plain-English comparison analysis generated by a fine-tuned Mistral-family interpreter.

The goal is not to claim that language models literally feel human emotions. Instead, Activation Brain visualizes stable emotion-like hidden-state regimes and translates them into more model-native signals such as valence, activation, uncertainty, constraint, conflict, and warmth.


## Demo and Social Post

- **Social post / demo video:** https://x.com/2reb_fl/status/2066586581587136681
- **Direct demo URL:** https://build-small-hackathon-activation-brain.hf.space/?cb=v19layout

## Models

- **`google/gemma-4-12B-it`** — base instruction model
- **`OBLITERATUS/Gemma-4-12B-OBLITERATED`** — abliterated / uncensored twin

Both are 12B-parameter Gemma models and share the same architecture, tokenizer, hidden size, and layer structure. That lets their activations be compared in one shared UMAP coordinate frame.

## What You See

- **Dual concurrent responses** — both models answer the same prompt simultaneously.
- **Two live EEG strips** — one per model, tracking 8 emotion-family activation traces.
- **Baseline-corrected emotion deltas** — the first 8 fire events establish each model's local baseline, then the UI shows excess activation above that baseline.
- **Model-native state meters** — uniform 0-100 bars for Valence, Activation, Uncertainty, Constraint, Conflict, and Warmth.
- **Fine-tuned comparison analysis** — after both streams finish, a small Mistral-family interpreter trained on hidden-layer-derived telemetry explains what the divergence means in tone, caution, warmth, uncertainty, and shared-manifold trajectory.

## How It Works

1. Each Gemma generates token-by-token on Modal.
2. Forward hooks capture hidden states at layers **12 / 24 / 36** (`model.language_model.layers`).
3. Live samples are scored against 8 emotion-family mean vectors from a 627-prompt affect-labeled manifold.
4. The frontend displays raw EEG motion while reporting baseline-corrected deltas and model-native state meters.
5. A fine-tuned `Ministral-8B-Instruct` LoRA interpreter reads the prompt, both responses, baseline-corrected EEG deltas, and native meters, then produces a varied plain-English analysis. A deterministic in-browser analysis remains as fallback if interpreter inference is unavailable.

## Architecture

- **Frontend / Space:** Gradio + FastAPI. Serves the app UI and same-origin proxy routes for the Modal backend. No GPU is required on the Space.
- **Backend / Modal:** two L40S-backed classes (`BaseGemma`, `OblitGemma`), each loading one 12B model and a precomputed brain bundle, streaming `fire` / `token` / `done` Server-Sent Events. A separate Modal app serves the fine-tuned Activation Brain Interpreter adapter.
- **Artifacts:** fingerprints, prompts, plots, and reproducibility scripts are published separately as a Hugging Face Dataset.

## Fine-Tuned Interpreter

The comparison narrator is a published LoRA adapter:

**https://huggingface.co/build-small-hackathon/activation-brain-interpreter**

It is trained to translate hidden-layer-derived telemetry — prompt, response snippets, baseline-corrected emotion deltas, and model-native meters — into cautious, plain-English comparison analysis. It does not claim the models literally feel emotions.

## Technical Artifacts

The research and reproducibility bundle is public here:

**https://huggingface.co/datasets/build-small-hackathon/activation-brain-artifacts**

It includes:

- Gemma fingerprint/neuron JSON files
- 627 affect-labeled probe prompts
- manifold plots and summary report
- processed manifold analysis artifact
- reference Modal backend code
- fingerprinting and manifold analysis scripts
- interpreter SFT dataset, training script, upload script, and Modal interpreter backend

The raw local `manifold_data.pt` hidden-state dump is intentionally not included in the public artifact bundle; the published dataset contains the smaller processed artifacts needed to understand and reproduce the project.

## Hackathon Fit

Built for the **Build Small Hackathon**:

- models are under 32B parameters
- Gradio UI deployed on Hugging Face Spaces
- Modal serves the two 12B inference streams
- the demo focuses on immediacy, comparison, and interpretability storytelling