Instructions to use CladeTeam/CENO-rice-cds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CladeTeam/CENO-rice-cds with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CladeTeam/CENO-rice-cds")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CladeTeam/CENO-rice-cds", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CladeTeam/CENO-rice-cds with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CladeTeam/CENO-rice-cds" # 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-rice-cds", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CladeTeam/CENO-rice-cds
- SGLang
How to use CladeTeam/CENO-rice-cds 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-rice-cds" \ --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-rice-cds", "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-rice-cds" \ --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-rice-cds", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CladeTeam/CENO-rice-cds with Docker Model Runner:
docker model run hf.co/CladeTeam/CENO-rice-cds
Configuration Parsing Warning:Invalid JSON for config file config.json
CENO Rice CDS 1-Epoch Finetune
CENO model finetuned for one epoch on Oryza sativa CDS sequences.
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.
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 filestraining_metrics.json: finetuning metrics
Loading
from transformers import AutoModelForCausalLM, AutoTokenizer
repo_id = "CladeTeam/CENO-rice-cds"
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=false, using the pure-PyTorch Mamba
fallback so no mamba-ssm/causal-conv1d install is required.
Training
Finetuned for 1 epoch on rice CDS with learning_rate=5e-5, effective_batch_size=64, bf16, max_length=8192.
Training Metrics
{
"species": "rice",
"train_loss": 10.05208391170438,
"eval_loss": 1.21553373336792,
"learning_rate": 5e-05,
"epochs": 1,
"epoch_losses": [
{
"epoch": 0.9987473903966597,
"eval_loss": 1.21553373336792
},
{
"epoch": 0.9987473903966597,
"eval_loss": 1.21553373336792
}
],
"n_gpu": 8,
"effective_batch_size": 64
}
Intended Use
These models are released to reproduce HTT/polyQ sequence scoring experiments. The average log-likelihood scores reflect sequence-model likelihood, not biological fitness or pathogenicity.
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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