Instructions to use AuraWorxAI/weather-llm-initial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AuraWorxAI/weather-llm-initial with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AuraWorxAI/weather-llm-initial")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AuraWorxAI/weather-llm-initial")
model = AutoModelForCausalLM.from_pretrained("AuraWorxAI/weather-llm-initial", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AuraWorxAI/weather-llm-initial with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AuraWorxAI/weather-llm-initial"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AuraWorxAI/weather-llm-initial",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AuraWorxAI/weather-llm-initial

SGLang

How to use AuraWorxAI/weather-llm-initial 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 "AuraWorxAI/weather-llm-initial" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AuraWorxAI/weather-llm-initial",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "AuraWorxAI/weather-llm-initial" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AuraWorxAI/weather-llm-initial",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AuraWorxAI/weather-llm-initial with Docker Model Runner:
```
docker model run hf.co/AuraWorxAI/weather-llm-initial
```

weather-llm-initial / scripts /eval_prompts.py

NagacharanVemula

Initial code-first model repo publish.

78dea75 4 months ago

Raw

History Blame Contribute Delete

941 Bytes

	#!/usr/bin/env python3
	"""Run a small qualitative eval suite and write a JSON report."""

	from __future__ import annotations

	import argparse
	import json
	import sys
	from pathlib import Path

	ROOT = Path(__file__).resolve().parents[1]
	sys.path.insert(0, str(ROOT / "src"))

	from weather_llm.config_loader import repo_root_from
	from weather_llm.eval.harness import run_eval_suite, write_report


	def main() -> int:
	ap = argparse.ArgumentParser()
	ap.add_argument("--model_dir", type=Path, required=True)
	ap.add_argument("--out", type=Path, default=None)
	args = ap.parse_args()
	root = repo_root_from(ROOT)
	out = args.out or (root / "outputs/eval_report.json")
	report = run_eval_suite(args.model_dir.resolve())
	write_report(out, report)
	print(json.dumps(report, indent=2, ensure_ascii=False))
	return 0 if report["passed"] == report["total"] else 1


	if __name__ == "__main__":
	raise SystemExit(main())