---
title: NeuroBait
colorFrom: green
colorTo: blue
sdk: gradio
app_file: app.py
pinned: false
license: apache-2.0
short_description: An ADHD-friendly space and gentle boost for your everyday.
---

# NeuroBait

NeuroBait is an ADHD-friendly companion for task initiation: a warm space and a
gentle boost for the moment when starting feels heavier than the task itself.

It is built for a very specific moment: the user already knows the task matters,
but the first move still feels too heavy. Instead of turning that friction into
a productivity lecture, NeuroBait replies with short, warm, agency-preserving
language. It avoids shame, streak pressure, diagnostic framing, and visible
prompt labels such as `Micro-action`, `Hook`, or `Stakes`.

This Space runs a fine-tuned small model through the app itself. It does not call
an external hosted LLM API for the chat response.

## Build Small Hackathon Submission

- Primary track: **Backyard AI**
- Why this track: NeuroBait focuses on a real everyday ADHD and
  neurodivergent friction - starting the thing that already matters - and turns a
  small model into a practical companion for that moment.
- Bonus quest fit: **Well-Tuned**, because the Space uses a published LoRA
  adapter fine-tuned for NeuroBait's voice and behavior.
- Bonus quest fit: **Off-Brand**, because the app uses custom Gradio styling and
  product copy instead of the default chatbot shell.
- Sponsor fit: **Modal-powered**, because fine-tuning and generation evaluation
  were run on Modal GPU infrastructure.

NeuroBait was fine-tuned with Modal and deployed as a Gradio app on Hugging Face
ZeroGPU.

## What The App Does

The app is intentionally narrow. NeuroBait should:

- respond in concise, natural prose,
- preserve the user's agency,
- avoid guilt framing and productivity shame,
- ask one light question when context is sparse,
- offer one tiny concrete action when enough context exists,
- keep the tone gentle without pretending to be a therapist.

The interface includes a small mood check-in so the same model can adapt its
response style slightly:

- Calm
- Tired
- Anxious
- Focused

The mood input does not change the safety scope. It only nudges the app-level
prompting and presentation.

## Model And Runtime

- Base model: `unsloth/gemma-3-12b-it`
- Adapter: `build-small-hackathon/NeuroBait`
- Method: 16-bit LoRA via Unsloth
- Training hardware: Modal H100 80GB GPU
- Space runtime: `transformers` + `peft`
- Quantization: 4-bit bitsandbytes NF4 inside the `@spaces.GPU` window

Unsloth is used for training, not for Space inference. The deployed app uses the
standard `transformers` + `peft` path so the public demo can load the Gemma 3 12B
base model plus the NeuroBait LoRA adapter on Hugging Face ZeroGPU.

## Runtime Configuration

Expected environment variables:

```text
BASE_MODEL=unsloth/gemma-3-12b-it
ADAPTER_ID=build-small-hackathon/NeuroBait
LOAD_IN_4BIT=1
MAX_NEW_TOKENS=220
PREWARM=1
```

Weights are pre-warmed to the Space cache on CPU at import so the GPU window can
focus on quantized loading and generation.

## Training And Evaluation Summary

Run #4 used a small bilingual Indonesian/English conversational dataset:

- Train conversations: 270
- Eval conversations: 30
- Training steps: 102
- Train loss: 1.7501
- Eval loss: 1.8844

The loss is only a weak diagnostic for this project. The main target is behavior:
shorter responses, warmer task-initiation support, and fewer leaked internal
structure labels.

Generation eval over 8 held-out or novel prompts:

- Base persona average: 2.25 / 4
- Fine-tuned persona average: 4.0 / 4
- Base average words: 80.4
- Fine-tuned average words: 55.1
- Base label leaks: 5
- Fine-tuned label leaks: 0

## Related Repos

- Model adapter: https://huggingface.co/build-small-hackathon/NeuroBait
- Codex trace dataset: https://huggingface.co/datasets/build-small-hackathon/NeuroBait-Codex-Traces
- Source repo: https://github.com/Subrata15/NeuroBait-Build-Small-Model

## Safety Scope

NeuroBait is not a medical device, diagnostic tool, therapist, emergency support
system, or replacement for professional care. It is a small-model demo for
gentle task-initiation support.