Text Generation
PEFT
Safetensors
English
emergency-response
resqai
structured-json
lora
qwen2.5
Indian-emergency-system
Instructions to use Devrcb/Emergency_informed_Qwen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Devrcb/Emergency_informed_Qwen with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
File size: 2,104 Bytes
e7c846d da70581 e7c846d da70581 d9da20b da70581 6b93bde da70581 6b93bde da70581 6b93bde da70581 6b93bde da70581 6b93bde da70581 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | ---
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: peft
pipeline_tag: text-generation
language:
- en
tags:
- emergency-response
- resqai
- structured-json
- lora
- qwen2.5
- Indian-emergency-system
---
# Emergency_informed_Qwen
**Emergency_informed_Qwen** is a fine-tuned LoRA adapter based on [`Qwen/Qwen2.5-0.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)[cite: 2]. It is specifically trained as the **Emergency Understanding Agent** for the **ResQAI Emergency Response System**[cite: 2, 3].
This model can be seamlessly integrated into the development of modern emergency response systems to extract structured key features from raw distress inputs[cite: 2, 3]. These extracted features can then be mapped directly with real-time tools—such as geocoding APIs, routing engines, and dispatch databases—to form a complete, end-to-end intelligent emergency agent.
---
## Model Details
* **Developed by:** ResQAI Project Team[cite: 3]
* **Model Type:** Causal Language Model (Fine-Tuned Adapter via PEFT/LoRA)
* **Base Model:** `Qwen/Qwen2.5-0.5B-Instruct`
* **Task:** Zero-shot / Few-shot Emergency Classification & Structured JSON Extraction
* **Fine-Tuning Framework:** Hugging Face `trl` (SFTTrainer) & `peft`
* **Language:** English (optimized for Indian emergency context)
---
## Intended Use
### Primary Use Case
This model acts as an emergency feature extraction engine[cite: 2, 3]. Given an unstructured report (voice transcripts, text messages, citizen distress calls), it extracts[cite: 2, 3]:
1. **Emergency Type & Severity Level**
2. **Services Required** (EMS, Fire Brigade, Police)
3. **Medical Speciality Needed**
4. **Immediate Citizen First-Aid / Action Advice**
### Output Format
The model strictly outputs **only valid JSON** adhering to the following schema:
```json
{
"citizen_advice": ["Call emergency services immediately", "Stay clear of smoke"],
"emergency_type": "Fire",
"medical_speciality": "Emergency Medicine",
"services_required": ["Emergency Medical Services (EMS)", "Firefighting"],
"severity": "High"
} |