disaster-response-trained/README.md

---
base_model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
Title: Teaching an LLM to Triage Disasters: Building an RL Environment with OpenEnv
Tags: openenv reinforcement-learning disaster-response grpo qwen

markdown# Teaching an LLM to Triage Disasters 🚨

## The Problem

During a natural disaster, Emergency Operations Centers (EOCs) are overwhelmed 
by thousands of frantic incident reports. A flooded neighborhood, a chemical 
plant fire, a hospital wing collapse — all arriving simultaneously. Human 
coordinators must instantly decide: which team? what priority? what action?

We built an AI agent that does exactly this.

---

## The Environment

We built **Disaster Response Coordination OpenEnv** — a multi-step RL environment 
where an AI agent acts as an Emergency Incident Commander.

**15 real-world scenarios** across 3 difficulty tiers, modeled after actual disasters:
- 🌊 2018 Kerala Floods → dam spillway overflow, communication blackouts
- ☠️ 2020 Vizag Gas Leak → chemical plant fire, toxic plume evacuation  
- ⚡ 2012 North India Grid Failure → cold-chain medicine failures, hospital blackouts

### Action Space
For every incident ticket, the agent must complete a 4-step workflow:
classify → set_priority → draft_reply → submit_ticket

### Reward Function
reward = 0.40 × team_routing + 0.30 × priority + 0.30 × reply_quality

Dense, partial rewards at every step. No sparse end-of-episode signals.

### Difficulty Scaling
| Tier | Budget | Scenarios |
|------|--------|-----------|
| 🟢 Easy | 40 | Single-team, clear incidents |
| 🟡 Medium | 48 | Multi-agency, ambiguous |
| 🔴 Hard | 55 | Cascading mass-casualty + time pressure |

---

## Training with GRPO

We trained **Qwen2.5-7B-Instruct** using GRPO (Group Relative Policy Optimization) 
via TRL + Unsloth on a Google Colab T4 GPU.

**Setup:**
- Base model: `unsloth/Qwen2.5-7B-Instruct-bnb-4bit`
- Algorithm: GRPOTrainer (TRL)
- LoRA: r=16, 4-bit quantization
- Epochs: 3 | Steps: 14
- Reward: Live environment feedback via HF Space API

The reward function connected directly to our live HF Space — every training 
step sent real incident prompts to the environment and received real rewards back.

### Training Reward Curve
![reward_curve](reward_curve.png)

---

## What We Discovered: Sparse Reward Collapse

The untrained base model immediately revealed why this environment is hard:

**Before training**, the model hallucinated invalid outputs:
team: "emergency_services"     ❌ (not a valid team)
team: "utility repair"         ❌
priority: "very-high"          ❌ (not a valid priority)
priority: "higher"             ❌

**After training**, the model learned valid action spaces:
team: "rescue"      ✅
priority: "urgent"  ✅

However, we observed **sparse reward collapse** — a known RL failure mode where 
a small model (7B at 4-bit) struggles to optimize across a multi-step workflow 
with interdependent rewards. This validates our environment's quality: it is 
genuinely difficult enough to expose real RL failure modes that larger models 
or longer training runs would be needed to overcome.

---

## Baseline Results

| Agent | Easy | Medium | Hard | **Avg** |
|-------|------|--------|------|---------|
| Heuristic Baseline | 0.704 | 0.683 | 0.660 | **0.682** |
| GRPO Qwen2.5-7B | — | — | — | research ongoing |

All 3 difficulty tiers **passed** (score ≥ 0.6).

---

## The Dashboard

We built a military-style tactical command dashboard with:
- 🗺️ Live OpenStreetMap incident markers with radar pulse animations
- ⚡ ARIA — AI Incident Analyst (Gemini-powered, analyses any incident live)
- 📊 Real-time score tracking, threat level bar, team routing
- 🔔 Operations feed with meaningful event notifications

![dashboard](dashboard.png)

---

## Links

| Resource | URL |
|----------|-----|
| 🚀 HF Space (Live Environment) | [joynnayvedya/disaster-response-openenv](https://huggingface.co/spaces/joynnayvedya/disaster-response-openenv) |
| 🧠 Trained Model | [joynnayvedya/disaster-response-trained](https://huggingface.co/joynnayvedya/disaster-response-trained) |
| 💻 GitHub | [letsjoyn/meta-scalar-hack](https://github.com/letsjoyn/meta-scalar-hack) |

---

## Try It Yourself

```bash
git clone https://github.com/letsjoyn/meta-scalar-hack.git
cd meta-scalar-hack
pip install -e .
py inference.py
```

---

*Built for the 2026 Meta & Scalar AI Hackathon — Grand Finale, Bangalore.*
# Uploaded finetuned  model

- **Developed by:** joynnayvedya
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct-bnb-4bit

This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)