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
Upload WorldDisasterLM-8B source code: FastAPI backend, training pipeline, 11-language support
495526b | 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}" | |
| ) | |