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: 3,416 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 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | """
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()
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