Text Generation
Transformers
Safetensors
ceno
dna
genomics
msa
variant-effect-prediction
mamba
Mixture of Experts
custom_code
Instructions to use CladeTeam/CENO-P-600M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CladeTeam/CENO-P-600M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CladeTeam/CENO-P-600M", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("CladeTeam/CENO-P-600M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CladeTeam/CENO-P-600M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CladeTeam/CENO-P-600M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CladeTeam/CENO-P-600M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CladeTeam/CENO-P-600M
- SGLang
How to use CladeTeam/CENO-P-600M 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 "CladeTeam/CENO-P-600M" \ --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": "CladeTeam/CENO-P-600M", "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 "CladeTeam/CENO-P-600M" \ --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": "CladeTeam/CENO-P-600M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CladeTeam/CENO-P-600M with Docker Model Runner:
docker model run hf.co/CladeTeam/CENO-P-600M
| CENO Checkpoint — CENO-P-600M | |
| Copyright (c) 2025-2026 CENO Authors. All rights reserved. | |
| This directory bundles a trained model checkpoint (CENO-P (multi-species alignment, MSA) variant) together with a | |
| copy of the CENO model code (configuration, modeling, and tokenizer modules). | |
| The bundled model code is derived from NVIDIA's Nemotron-H HuggingFace | |
| implementation, which is licensed under the Apache License, Version 2.0. | |
| Nemotron-H is © NVIDIA Corporation. The CENO model code is a derivative work | |
| distributed under the same Apache License, Version 2.0, included in this | |
| directory as the `LICENSE` file. | |
| The tokenizer module (`ceno_tokenizer.py`) is derived from Arc Institute's | |
| Evo2 CharLevelTokenizer, which is licensed under the Apache License, Version 2.0. | |
| All upstream copyright notices and license terms (NVIDIA; Arc Institute) are | |
| preserved in the corresponding source files as required by the Apache License, | |
| Version 2.0. | |