Zen SQL

Parameters: 8B | Architecture: Qwen3 | Context: 32K | License: Apache 2.0

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.

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("zenlm/zen-sql", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-sql")
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))

Credit

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.

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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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