Text Generation
Transformers
Safetensors
English
qwen3
zen
zenlm
hanzo-ai
multilingual
translation
CJK
text-generation-inference
Instructions to use zenlm/zen-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zenlm/zen-multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zenlm/zen-multilingual")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-multilingual") model = AutoModelForCausalLM.from_pretrained("zenlm/zen-multilingual", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use zenlm/zen-multilingual with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zenlm/zen-multilingual" # 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-multilingual", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/zenlm/zen-multilingual
- SGLang
How to use zenlm/zen-multilingual with SGLang:
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-multilingual" \ --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-multilingual", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use 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-multilingual" \ --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-multilingual", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use zenlm/zen-multilingual with Docker Model Runner:
docker model run hf.co/zenlm/zen-multilingual
Commit History
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docs: credit upstream Qwen3-8B (Apache-2.0) in NOTICE
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docs: honest attribution — base_model Qwen/Qwen3-8B + license + credit; drop fabricated 32B/Zen-3-arch/2024 claims
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Add LICENSE and NOTICE files for license compliance
hanzo-dev6bdb7cc
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Add NOTICE: Copyright Zen Authors
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fix(readme): body:sub('huggingface\\.co/zenlm/zen-next-80b-instruct')x1;sub('"zenlm/zen-next-80b-instruct"')x2;sub('zenlm/zen-next-80b-instruct')x1
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weights: server-side copy from zenlm/zen-pro
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readme: retarget base_model to zenlm/zen-pro (prior upstream removed in 2026-05 cleanup)
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feat: AI safety research positioning
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fix: remove abliterated tag, clean branding
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feat: initial release 2024-12-01
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initial commit
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