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 / worlddisasterlm /data /etl.py

drdeveloper88

Add usage code example (from_pretrained + BitsAndBytes + multilingual) to README

3b9fcc7 26 days ago

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2.13 kB

	from collections.abc import Iterable

	from worlddisasterlm.data.schemas import DisasterRecord


	class DisasterETL:
	def collect_records(self) -> list[DisasterRecord]:
	# Replace with API clients and ingestion jobs for production-scale collection.
	return [
	DisasterRecord(
	source="ReliefWeb",
	event_type="flood",
	region="South Asia",
	summary="Severe flooding displaced 12000 people and disrupted road access.",
	severity="high",
	),
	DisasterRecord(
	source="WHO",
	event_type="epidemic",
	region="East Africa",
	summary="Localized cholera outbreak with urgent water sanitation requirements.",
	severity="high",
	),
	DisasterRecord(
	source="USGS",
	event_type="earthquake",
	region="Pacific Rim",
	summary="Magnitude 6.8 earthquake with aftershock risk and infrastructure damage.",
	severity="critical",
	),
	]

	def deduplicate(self, records: Iterable[DisasterRecord]) -> list[DisasterRecord]:
	seen: set[tuple[str, str, str, str]] = set()
	deduped: list[DisasterRecord] = []
	for record in records:
	key = (record.source, record.event_type, record.region, record.summary)
	if key not in seen:
	deduped.append(record)
	seen.add(key)
	return deduped

	def normalize(self, records: Iterable[DisasterRecord]) -> list[DisasterRecord]:
	normalized: list[DisasterRecord] = []
	for record in records:
	normalized.append(
	DisasterRecord(
	source=record.source.strip(),
	event_type=record.event_type.strip().lower(),
	region=record.region.strip(),
	summary=" ".join(record.summary.split()),
	severity=record.severity.strip().lower(),
	)
	)
	return normalized