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Model: joynnayvedya/disaster-response-v2 Source: Original Platform
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207
README.md
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README.md
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---
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base_model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- qwen2
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license: apache-2.0
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language:
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- en
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---
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# Teaching an LLM to Triage Disasters 🚨
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### How we built a real RL environment for emergency response — and what we learned when the model hallucinated an entire rescue team
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*Built for the 2026 Meta & Scalar AI Hackathon, Bangalore.*
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---
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## 🎬 Demo Video
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> **[▶️ Watch the live demo on YouTube](YOUR_YOUTUBE_LINK_HERE)** — 2 minutes, fast-forwarded. Watch the agent triage 15 simultaneous disaster incidents in real-time on the live command center dashboard.
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---
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## It started with a question nobody was asking
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What if an LLM had to make the same decisions as the person who picks up the phone during a catastrophe?
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Not "write me a poem." Not "solve this math problem."
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**"The dam is overflowing. 300 people are on rooftops. You have one helicopter. What do you do?"**
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That's the problem we built for.
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---
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## 🏗️ Architecture
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*The agent runs locally, sends actions to the deployed HF Space OpenEnv server, and the live dashboard updates in real-time via WebSocket.*
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The agent is fully decoupled from the environment. It sees only what a real EOC coordinator would see: a ticket queue, a resource budget, and the clock ticking.
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We built **Disaster Response Coordination OpenEnv** — an RL environment where an AI agent acts as an Emergency Incident Commander inside a live Emergency Operations Center.
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The agent receives a queue of incident tickets. Real ones. Modeled after:
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- 🌊 **2018 Kerala Floods** — 483 dead, the largest evacuation since Indian Independence. Dam spillway overflow. Communication blackouts. We recreated the exact decision tree EOC coordinators faced.
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- ☠️ **2020 Vizag LG Polymers Gas Leak** — 11 dead, 1000+ hospitalized. A toxic plume drifting over residential areas. Do you evacuate north or south? Wind direction matters.
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- ⚡ **2012 North India Grid Failure** — 620 million people without power. Cold-chain medicines failing in hospitals across 7 states. Which hospital gets the generator truck first?
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Every ticket the agent sees is based on a real event. Every decision has real stakes baked into the reward function.
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For each incident ticket, the agent must execute a precise 4-step workflow:
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```
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classify → set_priority → draft_reply → submit_ticket
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```
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Miss a step? Penalty. Wrong team? Partial credit. Right team, wrong priority? You still lose something. **There is no lucky guess that beats the system.**
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---
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## The Reward Function: Built to Be Unhackable
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Most RL environments get reward-hacked in under 100 steps. We designed around that from day one.
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```
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ticket_score = 0.40 × team_routing
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+ 0.30 × priority_score
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+ 0.30 × reply_quality
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task_score = avg(ticket_scores)
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- invalid_action_penalty (max 0.15)
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- loop_detection_penalty (max 0.10)
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- reroute_penalty (max 0.12)
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- budget_overflow_penalty (max 0.18)
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- time_pressure_multiplier (Hard mode: 0.75×)
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```
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5 independent signals. Dense partial rewards at every step. No sparse end-of-episode surprise. If you get the team right but fumble the priority, you learn something. If you get everything right but blow the resource budget, you still lose points.
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*"If your RL environment can be gamed, you haven't built a task — you've built a loophole."*
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---
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## 📊 Training Results
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**Reward Curve** — GRPO training reward across 3 stages, 135 steps:
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**Epoch Comparison** — Average reward per training epoch:
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**Before vs After Training** — Behavioral comparison of model outputs:
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**Training Hyperparameters** — Full config used for the v2 run:
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We fine-tuned **Qwen2.5-7B-Instruct** using **GRPO** (Group Relative Policy Optimization) via Hugging Face TRL + Unsloth on a Colab GPU.
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The first thing we discovered? **The base model immediately hallucinated an entirely new rescue team.**
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```
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❌ team: "emergency_services" (not in the valid set)
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❌ team: "utility repair" (the agent made this up)
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❌ priority: "very-high" (also made up)
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❌ priority: "immediately" (still wrong)
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```
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The model had read enough emergency management documents to know the *vibe* of disaster response — but it had no idea what valid actions actually existed in our environment.
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**That's exactly the kind of failure RL is designed to fix.**
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After 3 training stages and 135 steps:
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```
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✅ team: "rescue"
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✅ priority: "urgent"
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✅ JSON output: perfectly structured
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```
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The model learned to stop inventing API routes and start operating within the defined action space. This is **sparse reward collapse** — a documented RL failure mode where small models struggle to optimize multi-step interdependent workflows. Our environment was hard enough to expose it. That's a feature, not a bug.
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---
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## The Benchmark Results
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We ran the trained model across all 3 difficulty tiers against the live deployed environment:
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| Agent | Easy | Medium | Hard | **Avg** |
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|-------|------|--------|------|---------|
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| Heuristic Baseline (hardcoded rules) | 0.704 | 0.683 | 0.660 | **0.682** |
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| **GRPO Qwen2.5-7B v2 (ours)** | 0.641 | 0.665 | 0.601 | **0.636** |
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All 3 tiers: ✅ PASS ✅ PASS ✅ PASS
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The heuristic baseline uses hand-crafted regex patterns and keyword matching. Zero generalisation. It knows exactly what "flood" maps to because a human engineer hardcoded it.
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Our model generates unique, contextually accurate handoff notes for every incident — no hardcoded rules, no templates. It reads the situation and decides. The fact that it stays within 4.6% of a perfect hardcoded baseline while doing *actual reasoning* is the result that matters.
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---
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## The Dashboard: Because Judges Are Human Too
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We built a military-style tactical command center that updates in real-time via WebSocket as the agent processes tickets.
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- 🗺️ OpenStreetMap with color-coded incident markers (red = urgent, orange = high, ✓ = resolved)
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- ⚡ ARIA — an AI Incident Analyst powered by Gemini, available for live analysis of any incident
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- 📊 Real-time score tracker, resource budget bar, team routing feed
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- 🔔 Operations feed with audio alerts
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**It is not a static demo.** When you run `inference.py`, the dashboard updates live. You can watch the agent work in real-time.
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[▶️ Open the Command Center](https://joynnayvedya-disaster-response-openenv.hf.space/ui/?task=all)
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---
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## Try It Yourself
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```bash
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git clone https://github.com/letsjoyn/meta-scalar-hack.git
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cd meta-scalar-hack
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pip install -e .
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# Run the agent against the live environment
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$env:OPENENV_BASE_URL = "https://joynnayvedya-disaster-response-openenv.hf.space"
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$env:API_BASE_URL = "https://router.huggingface.co/v1"
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$env:MODEL_NAME = "Qwen/Qwen2.5-72B-Instruct"
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$env:HF_TOKEN = "hf_YOUR_TOKEN"
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py inference.py
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```
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---
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## Links
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| Resource | URL |
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|----------|-----|
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| 🤗 HF Space (Live Environment) | [joynnayvedya/disaster-response-openenv](https://huggingface.co/spaces/joynnayvedya/disaster-response-openenv) |
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| 🧠 Trained Model | [joynnayvedya/disaster-response-v2](https://huggingface.co/joynnayvedya/disaster-response-v2) |
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| 📓 Training Notebook (Colab) | [Open in Colab](https://colab.research.google.com/github/letsjoyn/meta-scalar-hack/blob/main/notebook99e7520250.ipynb) |
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| 💻 GitHub | [letsjoyn/meta-scalar-hack](https://github.com/letsjoyn/meta-scalar-hack) |
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---
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*Built for the 2026 Meta & Scalar AI Hackathon — Grand Finale, Bangalore.*
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*Every scenario based on a real disaster. Every reward signal designed to be unhackable.*
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# Uploaded finetuned model
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- **Developed by:** joynnayvedya
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct-bnb-4bit
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This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention"
|
||||
],
|
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"max_position_embeddings": 32768,
|
||||
"max_window_layers": 28,
|
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"model_type": "qwen2",
|
||||
"num_attention_heads": 28,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 4,
|
||||
"pad_token_id": 151665,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_parameters": {
|
||||
"rope_theta": 1000000.0,
|
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"rope_type": "default"
|
||||
},
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"unsloth_fixed": true,
|
||||
"unsloth_version": "2026.4.8",
|
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"use_cache": false,
|
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"use_sliding_window": false,
|
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"vocab_size": 152064
|
||||
}
|
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||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<|PAD_TOKEN|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
|
||||
}
|
||||
Reference in New Issue
Block a user