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/zen4-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/zen4-sql",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
Zen4 Sql
Parameters: 7B | Architecture: Zen 4 Architecture | Context: 32K | License: Apache 2.0 | Released: 2025-04-01
SQL generation — complex queries, schema design, optimization.
Base weights: zenlm/zen4
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("zenlm/zen4", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("zenlm/zen4")
The Zen LM Family
Joint research collaboration:
- Hanzo AI (Techstars '17) — AI infrastructure, API gateway, inference optimization
- Zoo Labs Foundation (501c3) — Open AI research, ZIPs governance, decentralized training
- Lux Partners Limited — Compute coordination and settlement layer
All weights Apache 2.0. Download, run locally, fine-tune, deploy commercially.
HuggingFace · Chat free · API · Docs
Model tree for zenlm/zen4-sql
Base model
zenlm/zen4
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/zen4-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/zen4-sql", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'