Zen Specialty
Collection
Vertical-specific finetunes — finance, medical, legal, sql, translate, scribe, designer, etc. • 15 items • Updated
Text Generation
Transformers
Safetensors
English
qwen3
zen
zenlm
hanzo-ai
sql
database
code-generation
text-generation-inference
How to use from
SGLangUse Docker images
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "zenlm/zen-sql" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "zenlm/zen-sql",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
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.
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.
HuggingFace · Chat · API · Docs
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Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "zenlm/zen-sql" \ --host 0.0.0.0 \ --port 30000# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zenlm/zen-sql", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'