Zen Sql
Parameters: 7B | Architecture: Zen 4 Architecture | Context: 32K | License: Apache 2.0 | Released: 2024-11-15
SQL specialist for complex query generation, schema design, query optimization, and database documentation.
Supports PostgreSQL, MySQL, SQLite, BigQuery, Snowflake, and more.
Base weights: zenlm/zen4
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("zenlm/zen4", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("zenlm/zen4")
messages = [{"role": "user", "content": "Your domain-specific prompt here"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(output[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))
The Zen LM Family
Joint research between Hanzo AI (Techstars '17), Zoo Labs Foundation (501c3), and Lux Partners Limited.
All weights Apache 2.0. Download, run locally, fine-tune, deploy commercially.
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Model tree for zenlm/zen-sql
Base model
zenlm/zen4
Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "zenlm/zen-sql"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zenlm/zen-sql", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'