Instructions to use zenlm/zen-1-14b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zenlm/zen-1-14b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zenlm/zen-1-14b")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zenlm/zen-1-14b", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use zenlm/zen-1-14b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zenlm/zen-1-14b" # 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-1-14b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/zenlm/zen-1-14b
- SGLang
How to use zenlm/zen-1-14b 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-1-14b" \ --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-1-14b", "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-1-14b" \ --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-1-14b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use zenlm/zen-1-14b with Docker Model Runner:
docker model run hf.co/zenlm/zen-1-14b
Zen 1 14B
Originally released: 2023-12-01 Parameters: 14B | Architecture: Zen 1 Architecture | Context: 4K | License: Apache 2.0
First generation of the Zen LM family (Q4 2023). Dense transformer architecture trained on multilingual data: code, science, mathematics, and general knowledge.
Research Lineage
| Generation | Release | Architecture | Key Advancement |
|---|---|---|---|
| Zen 1 | Q4 2023 | Zen 1 Architecture | Foundation, dense transformer, 4K context |
| Zen 2 | Q2 2024 | Zen 2 Architecture | 128K context window |
| Zen 3 | Q3–Q4 2024 | Zen 3 Architecture | Sparse MoE routing, multimodal |
| Zen 4 | Q1 2025 | Zen 4 Architecture | 256K–1M context, extended families |
| Zen 5 | 2025+ | Zen MoDE | Next-generation frontier |
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.
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