Instructions to use drdeveloper88/WorldDisasterLM-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use drdeveloper88/WorldDisasterLM-8B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="drdeveloper88/WorldDisasterLM-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("drdeveloper88/WorldDisasterLM-8B")
model = AutoModelForCausalLM.from_pretrained("drdeveloper88/WorldDisasterLM-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use drdeveloper88/WorldDisasterLM-8B with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use drdeveloper88/WorldDisasterLM-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "drdeveloper88/WorldDisasterLM-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "drdeveloper88/WorldDisasterLM-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/drdeveloper88/WorldDisasterLM-8B

SGLang

How to use drdeveloper88/WorldDisasterLM-8B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "drdeveloper88/WorldDisasterLM-8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "drdeveloper88/WorldDisasterLM-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "drdeveloper88/WorldDisasterLM-8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "drdeveloper88/WorldDisasterLM-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use drdeveloper88/WorldDisasterLM-8B with Docker Model Runner:
```
docker model run hf.co/drdeveloper88/WorldDisasterLM-8B
```

WorldDisasterLM-8B

File size: 2,335 Bytes

495526b

---
title: WorldDisasterLM-8B
emoji: 🌍
colorFrom: red
colorTo: pink
sdk: gradio
app_file: app.py
pinned: true
license: other
tags:
  - disaster-management
  - emergency-response
  - humanitarian-ai
  - multilingual
  - fine-tuned
  - qlora
  - text-generation
short_description: Multilingual disaster guidance in 11 languages
---

# 🌍 WorldDisasterLM-8B

**Open Foundation Model for Global Disaster Intelligence**

WorldDisasterLM-8B is an instruction-tuned language model built on **Meta Llama 3.1 8B Instruct**,
domain-adapted on global humanitarian disaster data for emergency guidance, risk assessment, and
crisis intelligence — across **11 languages**.

## Features

- 🗣️ **11 Languages** — English, Nepali, Spanish, French, Arabic, Hindi, Telugu, Chinese, Japanese, Korean, Portuguese
- 🏔️ **Nepal-first** — Nepali (Devanagari) with NDRRMA citations
- 📊 **Risk Scoring** — Composite disaster risk calculation (vulnerability × exposure)
- ⚡ **Live Demo** — Ask emergency questions, get actionable guidance instantly
- 🌐 **Global Coverage** — Earthquakes, floods, cyclones, wildfires, tsunamis, landslides

## Training Data Sources

| Source | Description |
|---|---|
| ReliefWeb | Humanitarian reports and disaster assessments |
| USGS | Earthquake catalog (M≥4.0, 10-year archive) |
| NOAA | Weather alerts and severe weather events |
| GDACS | Global disaster alert coordination events |
| OpenFEMA | US federal disaster declarations |
| WHO | Disease outbreak news and public health alerts |

## Try It

Type any disaster-related question in your language:
- **English:** "What should I do immediately after an earthquake?"
- **Nepali:** "भूकम्पको बेला के गर्ने?"
- **Spanish:** "¿Qué hacer durante una inundación?"
- **Arabic:** "ما الذي يجب فعله أثناء الإعصار؟"

## Safety Notice

> ⚠️ This model is for **informational and educational purposes only**.
> Always follow official emergency orders from local authorities.
> Do not use as a sole source for life-safety decisions.

## Citation

```bibtex
@misc{worlddisasterlm2026,
  title  = {WorldDisasterLM: Open Foundation Model for Global Disaster Management},
  year   = {2026},
  url    = {https://huggingface.co/spaces/YOUR_HF_USERNAME/WorldDisasterLM-8B}
}
```