Text Generation
Transformers
Safetensors
llama
causal-lm
weather
supervised-fine-tuning
text-generation-inference
Instructions to use AuraWorxAI/weather-llm-initial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 1,477 Bytes
78dea75 | 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 | #!/usr/bin/env python3
"""Stream English Wikipedia articles into chunked JSONL (Hugging Face `datasets`)."""
from __future__ import annotations
import argparse
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT / "src"))
from tqdm import tqdm
from weather_llm.config_loader import load_yaml, repo_root_from
from weather_llm.data.jsonl_utils import write_jsonl
from weather_llm.data.wiki import wikipedia_to_chunks
def main() -> int:
ap = argparse.ArgumentParser()
ap.add_argument("--config", type=Path, default=None)
args = ap.parse_args()
root = repo_root_from(ROOT)
cfg_path = args.config or (root / "configs/data_default.yaml")
cfg = load_yaml(cfg_path)
wiki = cfg["wikipedia"]
paths = cfg["paths"]
out_path = root / paths["processed"] / "wikipedia.jsonl"
stream = wikipedia_to_chunks(
dataset_name=str(wiki["dataset_name"]),
dataset_config=str(wiki["dataset_config"]),
max_articles=int(wiki["max_articles"]),
min_article_chars=int(wiki["min_article_chars"]),
chunk_target_chars=int(wiki["chunk_target_chars"]),
chunk_overlap_chars=int(wiki["chunk_overlap_chars"]),
)
rows = []
for row in tqdm(stream, desc="wiki chunks"):
rows.append(row)
write_jsonl(out_path, iter(rows))
print("Wrote", len(rows), "chunks to", out_path)
return 0
if __name__ == "__main__":
raise SystemExit(main())
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