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 / hf_space /README.md

drdeveloper88

Upload WorldDisasterLM-8B source code: FastAPI backend, training pipeline, 11-language support

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	---
	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}
	}
	```