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")

Notebooks
Google Colab
Kaggle
Local Apps

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 — SFT (run1 stage2, step 2500)

Supervised fine-tuning checkpoint: run1_stage2/checkpoint-2500. This repository contains both the published model artifacts and supporting project code files.

Model Details

Model type: Causal language model for text generation (transformers).
Training flow: weather-domain pretraining followed by supervised fine-tuning (SFT).
Checkpoint lineage: this release corresponds to the uploaded run1_stage2/checkpoint-2500 model.
Tokenizer: Hub-compatible tokenizer files are included in this repository.
Context length: configured by uploaded tokenizer/model files (model_max_length=2048).

Data Sources

NOAA-derived historical weather data processed into natural-language records.
Optional support text from Wikipedia used during corpus building.
Synthetic and grounded instruction-response examples used for SFT dataset construction.

Code in Files Section

Code under src/, scripts/, and configs/ in the HF Files section is used for:

data building and preprocessing,
tokenizer training,
model pretraining and SFT training workflows,
reproducibility of training/evaluation steps.

Intended Use

Instruction-following weather-analysis style generation.
Educational and research usage for domain-focused LLM workflows.

Out-of-Scope / Limitations

Not for real-time weather operations or safety-critical decision-making.
Outputs may be incorrect, incomplete, or outdated and should be independently validated.
Not a replacement for authoritative meteorological services.

Inference

There are two inference entrypoints, and they are intentionally different:

Hugging Face Hub inference (loads by model repo ID).
Project-local inference script (loads by local model directory path used during local training workflow).

Hugging Face Hub Snippet

from transformers import AutoModelForCausalLM, AutoTokenizer

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

Requires transformers and sentencepiece.

Project-Local Inference Snippet

python -m weather_llm.inference.generate \
  --model_dir artifacts/checkpoints/sft/run1_stage2 \
  --prompt "Compare summer weather patterns in Arizona vs Washington."

The HF snippet loads from a Hub model ID, while the local snippet loads from a filesystem path produced in local training runs.

Contact

Maintainer: AuraWorxAI

Downloads last month: 3

Safetensors

Model size

1B params

Tensor type

F32