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 | """ | |
| Upload WorldDisasterLM-8B as a public HuggingFace Space (Gradio demo). | |
| Usage | |
| ----- | |
| # Set your HF token first: | |
| $env:HF_TOKEN = "hf_xxxxxxxxxxxxxxxxxxxx" | |
| # Then run: | |
| python scripts/upload_space.py --username YOUR_HF_USERNAME | |
| # Optionally specify a custom space name: | |
| python scripts/upload_space.py --username YOUR_HF_USERNAME --space-name WorldDisasterLM-8B | |
| Requirements | |
| ------------ | |
| pip install huggingface_hub | |
| """ | |
| from __future__ import annotations | |
| import argparse | |
| import os | |
| import sys | |
| from pathlib import Path | |
| ROOT = Path(__file__).parent.parent | |
| SPACE_DIR = ROOT / "hf_space" | |
| def parse_args() -> argparse.Namespace: | |
| p = argparse.ArgumentParser(description="Upload WorldDisasterLM-8B to HuggingFace Spaces") | |
| p.add_argument("--username", required=True, help="Your HuggingFace username") | |
| p.add_argument("--space-name", default="WorldDisasterLM-8B", help="Space repository name") | |
| p.add_argument("--private", action="store_true", help="Create as private space (default: public)") | |
| return p.parse_args() | |
| def main() -> None: | |
| args = parse_args() | |
| # Check token | |
| token = os.environ.get("HF_TOKEN") | |
| if not token: | |
| print("ERROR: HF_TOKEN environment variable is not set.") | |
| print(" Set it with: $env:HF_TOKEN = 'hf_xxxxxxxxxxxx'") | |
| sys.exit(1) | |
| try: | |
| from huggingface_hub import HfApi, create_repo | |
| except ImportError: | |
| print("ERROR: huggingface_hub not installed. Run: pip install huggingface_hub") | |
| sys.exit(1) | |
| repo_id = f"{args.username}/{args.space_name}" | |
| api = HfApi(token=token) | |
| print(f"\n{'='*60}") | |
| print(f" WorldDisasterLM-8B → HuggingFace Space") | |
| print(f" Repo : {repo_id}") | |
| print(f" Private: {args.private}") | |
| print(f"{'='*60}\n") | |
| # 1. Create the Space repo | |
| print("Step 1/3 — Creating Space repository...") | |
| create_repo( | |
| repo_id=repo_id, | |
| repo_type="space", | |
| space_sdk="gradio", | |
| private=args.private, | |
| exist_ok=True, | |
| token=token, | |
| ) | |
| print(f" ✓ Space created: https://huggingface.co/spaces/{repo_id}") | |
| # 2. Patch README.md with actual username | |
| readme_src = SPACE_DIR / "README.md" | |
| readme_text = readme_src.read_text(encoding="utf-8") | |
| readme_text = readme_text.replace("YOUR_HF_USERNAME", args.username) | |
| import tempfile, shutil | |
| tmp_dir = Path(tempfile.mkdtemp()) | |
| try: | |
| # Copy space files to temp dir with patched README | |
| shutil.copytree(str(SPACE_DIR), str(tmp_dir / "space")) | |
| (tmp_dir / "space" / "README.md").write_text(readme_text, encoding="utf-8") | |
| # 3. Upload the folder | |
| print("Step 2/3 — Uploading files...") | |
| api.upload_folder( | |
| folder_path=str(tmp_dir / "space"), | |
| repo_id=repo_id, | |
| repo_type="space", | |
| commit_message="Upload WorldDisasterLM-8B Space demo", | |
| token=token, | |
| ) | |
| print(" ✓ Files uploaded") | |
| finally: | |
| shutil.rmtree(tmp_dir, ignore_errors=True) | |
| print("\nStep 3/3 — Verifying Space...") | |
| space_info = api.space_info(repo_id=repo_id, token=token) | |
| print(f" ✓ Space status: {getattr(space_info, 'runtime', {})}") | |
| print(f"\n{'='*60}") | |
| print(f" DONE! Your Space is live at:") | |
| print(f" https://huggingface.co/spaces/{repo_id}") | |
| print(f"{'='*60}\n") | |
| if __name__ == "__main__": | |
| main() | |