README.md

metadata

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