iql-fire-rescue

IQL Fire Rescue Operator Model

This model uses Implicit Q-Learning (IQL) to select optimal conversation policies for emergency response scenarios.

Model Description

• Task: Policy selection for fire rescue emergency conversations
• Algorithm: Implicit Q-Learning (IQL)
• Input: Conversation history (embedded using sentence-transformers)
• Output: Best policy and Q-values for all policies

Policies

1. provide_information - Share facts, data, risk levels
2. offer_assistance - Provide help, resources, support
3. express_urgency - Convey time pressure, danger
4. ask_question - Gather information from resident
5. give_direction - Provide specific instructions
6. acknowledge_concern - Validate resident's feelings
7. build_rapport - Establish trust and connection

Usage

import requests

API_URL = "https://api-inference.huggingface.co/models/YOUR_USERNAME/iql-fire-rescue"
headers = {"Authorization": f"Bearer YOUR_HF_TOKEN"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

# Example
output = query({
    "inputs": "Conversation state here",
    "parameters": {"character": "bob"}
})

Training Data

Trained on emergency response conversations with multiple character profiles.

Citation

If you use this model, please cite:

@misc{iql-fire-rescue,
  title={IQL-based Emergency Response Operator},
  author={Your Name},
  year={2025},
  publisher={Hugging Face}
}