NeuroBait: AI that sparks dopamine for ADHD | Keryc
NeuroBait was born from a domestic observation and a simple question: what makes a person with ADHD stop freezing in front of a task? Was it a test or a checklist? No — it was a tiny spark: a short, warm, actionable phrase that makes starting stop feeling impossible.
What is NeuroBait
NeuroBait is a fine-tuned model designed to produce micro-conversational interventions: responses of 3 to 6 sentences, warm, without clinical labels or walls of bullets. Its goal isn't to diagnose or overplan, but to spark enough dopamine to move your hand and do one small thing now.
Why does this matter? Because for many people with ADHD the problem isn't knowing what to do, it's getting started. A pretty to-do list can become another barrier. NeuroBait aims to close that gap between knowing and beginning.
It brings no guilt or sermons. It gives you back the feeling of being the actor who acts, not the patient who must improve.
How it was trained (technical details)
The project chose google/gemma-3-12b-it as the base: the dense variant of Gemma 3 12B adjusted with Gemma3ForConditionalGeneration. Fine-tuning was applied with light techniques to keep cost and deployment feasible.
Key training parameters:
Technique: fine-tuning with PEFT (low-rank adapters), to change behavior without retraining the whole model.
Configuration: r=16, alpha=16, dropout=0.3.
Optimization: learning rate 2e-4.
Batch: 1 with grad_accum 8.
Max sequence: 2048 tokens.
Turn markers: \<start_of_turn>user\\n and \<start_of_turn>model\\n to preserve conversational context.
save_strategy=\"no\" to avoid a known TRL bug related to checkpoints.
Why these choices? Gemma 3 offers a robust base for conditional generation; PEFT allows fast experimentation on a moderate budget; the r/alpha and dropout settings seek a balance between adaptability and stability.
What the model learned (emergent behavior)
The base model, before tuning, tended to return classic formats: headings, bullets, step lists. That's useful for many scenarios, but for someone frozen that format can be intimidating.
After fine-tuning, NeuroBait changed in kind, not just tone:
Shorter, more fluid responses, in warm prose.
Asks before assuming, to personalize the output.
Reframes context so the reply feels written for you, not a generic user.
Proposes a single immediate and very concrete action: 'Take one shirt from the pile' instead of 'organize the clothes'.
That's important: it's not about handbook psychology, it's about nudging the nervous system toward a small reward that makes the next thing possible.
Evaluation and limitations
Evaluation so far is mostly qualitative and based on real observation: the creator tested it with someone close (his wife) and with other users in everyday scenarios. Anecdotal results indicate the technique helps kick-start actions in many cases.
Known limitations:
Lack of public quantitative metrics so far; there are no formal benchmarks like F1 or ROUGE applied to this human-centered task.
Risk of out-of-context responses if the prompt doesn't contain enough relevant information.
It's not therapy and doesn't replace clinical support; it's a support tool for task initiation.
Important: effectiveness depends heavily on context quality and the model's sensitivity to avoid paternalism or inappropriate instructions.
Ethical and deployment considerations
When designing tools for neurodivergence you must avoid two traps: designing for rather than with the community, and skimping on safety testing. NeuroBait recognizes this and aims to open weights and the pipeline for community review.
Practical deployment points:
Keep latency low for quick responses; use optimized inference on GPUs/TPUs or servers with small batches.
Prompt control and safety filters to avoid harmful suggestions.
Continuous feedback with real users to tune voice and boundaries.
Next steps
The project's plan includes:
Publish weights and full pipeline for audit and contributions.
Bilingual support (English and Indonesian at launch) and then more languages.
Collaborative development with the ADHD community to iterate on voice, limits, and failure cases.
If you have ADHD or know someone who does, this isn't a distant experiment: it's a prototype that started in a backyard and wants to grow with the community. Will you try it and tell where it annoys you? That feedback is the project's raw material.
Resources and demo
You can try the demo on Hugging Face Spaces and see how it responds in real situations. It's a direct way to understand if this 'small spark' strategy works for you or someone you know.
