Instructions to use CladeTeam/CENO-base-1b-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CladeTeam/CENO-base-1b-preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CladeTeam/CENO-base-1b-preview")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CladeTeam/CENO-base-1b-preview", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CladeTeam/CENO-base-1b-preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CladeTeam/CENO-base-1b-preview" # 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-base-1b-preview", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CladeTeam/CENO-base-1b-preview
- SGLang
How to use CladeTeam/CENO-base-1b-preview 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-base-1b-preview" \ --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-base-1b-preview", "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-base-1b-preview" \ --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-base-1b-preview", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CladeTeam/CENO-base-1b-preview with Docker Model Runner:
docker model run hf.co/CladeTeam/CENO-base-1b-preview
Configuration Parsing Warning:Invalid JSON for config file config.json
CENO Base 1B (Preview)
Base CENO language model. This is a preview release.
CENO is derived from NVIDIA's Nemotron-H (Apache-2.0). The custom Transformers
remote code in this repository (configuration_ceno.py, modeling_ceno.py) is a
rename of the upstream Nemotron-H implementation; the model weights are unchanged.
This repository includes custom Transformers remote code for CENOForCausalLM
and CENOCharLevelTokenizer. Load with trust_remote_code=True.
Files
model.safetensors: model weightsconfig.json: model config withauto_mapgeneration_config.json: generation configconfiguration_ceno.py,modeling_ceno.py: custom model codeceno_tokenizer.py,tokenizer_config.json,special_tokens_map.json,vocab.json: tokenizer files
Loading
from transformers import AutoModelForCausalLM, AutoTokenizer
repo_id = "CladeTeam/CENO-base-1b-preview"
model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
The model code depends on PyTorch and the Mamba/Triton stack used by Nemotron-H.
The bundled config sets use_mamba_kernels=true; if mamba-ssm and
causal-conv1d are not installed, set config.use_mamba_kernels=False before
loading to fall back to the pure-PyTorch Mamba path.
Intended Use
A general-purpose DNA language model for sequence modeling tasks such as likelihood scoring, representation learning, and downstream finetuning on species- or task-specific DNA data.
License
This model and its bundled code are released under the Apache License 2.0, inheriting the license of the upstream Nemotron-H model code (Copyright 2024 AI21 Labs Ltd. and the HuggingFace Inc. team; Copyright (c) 2025 NVIDIA CORPORATION). Modifications for CENO by CladeTeam.
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