Instructions to use zenlm/zen-5-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zenlm/zen-5-coder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zenlm/zen-5-coder")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zenlm/zen-5-coder", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use zenlm/zen-5-coder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zenlm/zen-5-coder" # 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-5-coder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/zenlm/zen-5-coder
- SGLang
How to use zenlm/zen-5-coder 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-5-coder" \ --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-5-coder", "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-5-coder" \ --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-5-coder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use zenlm/zen-5-coder with Docker Model Runner:
docker model run hf.co/zenlm/zen-5-coder
fix: Zen MoDE = Mixture of Diverse Experts
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README.md
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# Zen 5 Coder
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> **Parameters**: TBA | **Architecture**: Zen MoDE | **Context**: 256K | **Status**: In training
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Zen 5 Coder — next-
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**Zen MoDE**
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---
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## The Zen LM Family
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Joint research
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- **Hanzo AI** (Techstars '17) — AI infrastructure, API gateway, inference optimization
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- **Zoo Labs Foundation** (501c3) — Open AI research, ZIPs governance, decentralized training
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- **Lux Partners Limited** — Compute coordination and settlement layer
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All weights Apache 2.0. Download, run locally, fine-tune, deploy commercially.
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[HuggingFace](https://huggingface.co/zenlm) · [Chat
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# Zen 5 Coder
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> **Parameters**: TBA | **Architecture**: Zen MoDE (Mixture of Diverse Experts) | **Context**: 256K | **Status**: In training
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Zen 5 Coder — next-generation code specialist. Currently in training.
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**Zen MoDE** is our next-generation architecture: Mixture of Diverse Experts with sparse activation, extended context, and enhanced multi-step reasoning. First introduced with Zen 5.
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[Learn more → zenlm.org](https://zenlm.org)
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---
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## The Zen LM Family
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Joint research between **Hanzo AI** (Techstars '17), **Zoo Labs Foundation** (501c3), and **Lux Partners Limited**.
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All weights Apache 2.0. Download, run locally, fine-tune, deploy commercially.
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[HuggingFace](https://huggingface.co/zenlm) · [Chat](https://hanzo.chat) · [API](https://api.hanzo.ai) · [Docs](https://zenlm.org)
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