SQL specialist for complex query generation, schema design, query optimization, and database documentation.
Supports PostgreSQL, MySQL, SQLite, BigQuery, Snowflake, and more.
Fine-tuned from
Qwen/Qwen3-8B (Apache-2.0) with Hanzo identity + agentic-data training + abliteration.
1from transformers import AutoModelForCausalLM, AutoTokenizer
2model = AutoModelForCausalLM.from_pretrained("zenlm/zen-sql", torch_dtype="auto")
3tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-sql")
4messages = [{"role": "user", "content": "Your domain-specific prompt here"}]
5text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
6inputs = tokenizer(text, return_tensors="pt").to(model.device)
7output = model.generate(**inputs, max_new_tokens=1024)
8print(tokenizer.decode(output[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))
Built on
Qwen/Qwen3-8B by the Qwen team (Alibaba), released under the Apache-2.0 license. Hanzo adds identity training, agentic-data fine-tuning, and abliteration.
All weights Apache 2.0. Download, run locally, fine-tune, deploy commercially.
