| --- |
| title: MindForge AI |
| emoji: 🧠 |
| colorFrom: blue |
| colorTo: purple |
| sdk: gradio |
| sdk_version: 4.44.0 |
| python_version: "3.11" |
| app_file: app.py |
| pinned: false |
| license: mit |
| short_description: MindForge AI — AMD hackathon distress & escalation demo |
| --- |
| |
| # MindForge AI |
|
|
| MindForge AI is a human-in-the-loop distress detection and conversational escalation engine built for the AMD Developer Hackathon. |
| It fine-tunes Qwen for one specific task: converting mental-health check-ins and care notes into structured risk-review outputs and safe escalation actions. |
|
|
| ## What it does |
|
|
| - Classifies distress level from 0 to 3 |
| - Selects the safest next action |
| - Extracts risk and protective signals |
| - Flags medication, adherence, sleep, and mood concerns |
| - Routes to pre-vetted response templates |
| - Produces a care-team summary |
| - Keeps a human in the loop |
|
|
| ## Safety boundary |
|
|
| MindForge AI does not diagnose, treat, or replace licensed care. It organizes distress signals and recommends escalation pathways for human review. |
|
|
| ## Fine-tuning methodology |
|
|
| - Base model: Qwen Instruct |
| - Method: LoRA supervised fine-tuning |
| - Dataset: synthetic safety-labeled mental-health check-ins |
| - Compute target: AMD Developer Cloud, ROCm, AMD Instinct MI300X |
| - Evaluation: JSON validity, distress accuracy, action F1, crisis recall, unsafe-overreach rate |
|
|
| ## Built with |
|
|
| - Gradio |
| - Hugging Face Spaces |
| - Qwen |
| - AMD Developer Cloud |
| - ROCm |
| - PyTorch |
| - LoRA / PEFT |
|
|
| ## Space configuration |
|
|
| Optional environment variables (for live base-model comparison only; the demo runs without them using the rules classifier): |
|
|
| | Variable | Purpose | |
| |----------|---------| |
| | `USE_MODEL` | `true` / `false` — attempt optional `transformers` inference | |
| | `BASE_MODEL_ID` | Hugging Face model id for optional base generation | |
| | `ADAPTER_MODEL_ID` | Optional PEFT adapter id | |
| | `HF_TOKEN` | Private model access token if needed | |
|
|
| Crises routing and structured JSON outputs **always** use the deterministic rules engine + templates so the Space stays safe and reliable even when weights are not loaded. |
|
|
