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: 812 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 | #!/usr/bin/env python3
"""Verify GPU, PyTorch, and core dependencies for Vast.ai / local runs."""
from __future__ import annotations
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT / "src"))
def main() -> int:
import torch
print("python:", sys.version.split()[0])
print("torch:", torch.__version__)
print("cuda available:", torch.cuda.is_available())
if torch.cuda.is_available():
print("cuda device:", torch.cuda.get_device_name(0))
import transformers
import accelerate
import datasets
print("transformers:", transformers.__version__)
print("accelerate:", accelerate.__version__)
print("datasets:", datasets.__version__)
return 0
if __name__ == "__main__":
raise SystemExit(main())
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