Text Generation
Transformers
PEFT
llama
disaster-management
emergency-response
humanitarian-ai
multilingual
fine-tuned
qlora
lora
llama3
conversational
4-bit precision
bitsandbytes
Instructions to use drdeveloper88/WorldDisasterLM-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 1,459 Bytes
495526b | 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 | from worlddisasterlm.config import SUPPORTED_LANGUAGES
from worlddisasterlm.data.etl import DisasterETL
from worlddisasterlm.data.processors import build_instruction_dataset
from worlddisasterlm.data.scenario_builder import build_all_scenarios
def test_dataset_builder_generates_samples() -> None:
etl = DisasterETL()
records = etl.normalize(etl.deduplicate(etl.collect_records()))
samples = build_instruction_dataset(records)
assert len(samples) > 0
assert samples[0].instruction
def test_nepali_in_supported_languages() -> None:
"""Nepali must be present in SUPPORTED_LANGUAGES."""
assert "Nepali" in SUPPORTED_LANGUAGES
def test_nepali_scenario_samples_exist() -> None:
"""At least one Nepali-language training sample must be built from scenarios."""
samples = build_all_scenarios()
nepali_samples = [s for s in samples if s.language.lower() == "nepali"]
assert len(nepali_samples) >= 3, (
f"Expected >=3 Nepali samples, found {len(nepali_samples)}"
)
def test_nepali_samples_use_devanagari() -> None:
"""Nepali scenario instructions must contain Devanagari Unicode characters."""
samples = build_all_scenarios()
nepali_samples = [s for s in samples if s.language.lower() == "nepali"]
for sample in nepali_samples:
assert any("\u0900" <= ch <= "\u097F" for ch in sample.instruction), (
f"Nepali sample missing Devanagari: {sample.instruction!r}"
)
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