Instructions to use AuraWorxAI/weather-llm-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use AuraWorxAI/weather-llm-sft with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf AuraWorxAI/weather-llm-sft:Q4_K_M # Run inference directly in the terminal: llama cli -hf AuraWorxAI/weather-llm-sft:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf AuraWorxAI/weather-llm-sft:Q4_K_M # Run inference directly in the terminal: llama cli -hf AuraWorxAI/weather-llm-sft:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf AuraWorxAI/weather-llm-sft:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf AuraWorxAI/weather-llm-sft:Q4_K_M
Use Docker
docker model run hf.co/AuraWorxAI/weather-llm-sft:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use AuraWorxAI/weather-llm-sft with Ollama:
ollama run hf.co/AuraWorxAI/weather-llm-sft:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use AuraWorxAI/weather-llm-sft with Docker Model Runner:
docker model run hf.co/AuraWorxAI/weather-llm-sft:Q4_K_M
- Lemonade
How to use AuraWorxAI/weather-llm-sft with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AuraWorxAI/weather-llm-sft:Q4_K_M
Run and chat with the model
lemonade run user.weather-llm-sft-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Weather LLM (SFT)
Instruction-tuned decoder for U.S. historical weather language (not live forecasts).
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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
repo_id = "AuraWorxAI/weather-llm-sft"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
repo_id,
torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
device_map="auto" if torch.cuda.is_available() else None,
)
Prompt format (matches training): ### Instruction:\n...\n### Response:\n
trust_remote_code=True is required for the SentencePiece WeatherSpmTokenizer shipped in this repo.
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