tribeblend-etl-compact

# Download the model
huggingface-cli download TribeBlend/tribeblend-etl-compact --local-dir ./tribeblend-etl-compact

# Run with llama.cpp
./llama-cli -m ./tribeblend-etl-compact/tribeblend-etl-compact-q4_k_m.gguf \
  -p "Transform this data: filter rows where sales > 1000 and group by region" \
  -n 256

from llama_cpp import Llama

llm = Llama(
    model_path="./tribeblend-etl-compact-q4_k_m.gguf",
    n_ctx=4096,
    n_threads=4
)

response = llm.create_chat_completion(
    messages=[
        {"role": "system", "content": "You are a data transformation assistant."},
        {"role": "user", "content": "How do I join two tables on customer_id?"}
    ]
)

print(response["choices"][0]["message"]["content"])