CENO-P-1B is the multi-species alignment (MSA) post-trained variant of the 1B CENO DNA foundation model, for variant effect prediction (VEP). It carries intra_encoding_pattern in its config and ships the MSA scoring path (modeling_ceno_p.py), which consumes a per-token seq_idx to score packed MSA inputs.

It is part of the CENO DNA foundation model family. Model code, the VEP pipeline, and a generation demo live in the companion CENO code repository. Run VEP via the TraitGym example there. This checkpoint is standalone-loadable with trust_remote_code=True — the model code is bundled here.

Model details


Family	CENO-P (MSA post-trained)
Training stage	MSA post-training (VEP)
Parameters	1.3B (1302.4M)
Precision	float32
`model_type`	`ceno`
Architecture class	`CENOPForCausalLM`
Auto-map (model)	`modeling_ceno_p.CENOPForCausalLM`
Auto-map (tokenizer)	`ceno_tokenizer.CENOCharLevelTokenizer`

Architecture

Property	Value
Hidden layers	38
Hidden size	1024
Attention heads	16
Intermediate size	4096
Experts (MoE)	8 (top-2 per token)
Vocabulary	512 (byte / character-level)

The backbone is a Mamba / Attention / Mixture-of-Experts hybrid (Nemotron-H architecture). The tokenizer is character-level, mapping DNA bases to their ASCII byte codes.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

ckpt = "CladeTeam/CENO-P-1B"
model = AutoModelForCausalLM.from_pretrained(ckpt, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(ckpt, trust_remote_code=True)

ids = tokenizer.encode("ATCGATCG", return_tensors="pt")
# out = model.generate(ids, max_new_tokens=128)   # needs a CUDA GPU (Mamba kernels)

The Mamba layers require CUDA kernels, so forward passes and generation need a GPU. Config, tokenizer, and weight loading are CPU-safe.

Intended use

Base checkpoints (CENO-*) — genomic-sequence generation and embedding extraction; downstream adaptation (fine-tuning, probing) for genomics tasks.
MSA checkpoints (CENO-P-*) — variant effect prediction (VEP) by scoring wild-type vs. variant sequences with delta log-likelihood. See the TraitGym VEP example in the CENO code repository.

License

Apache-2.0. The bundled model code is derived from NVIDIA's Nemotron-H Hugging Face implementation (Apache-2.0); the tokenizer is derived from the Arc Institute Evo2 CharLevelTokenizer (Apache-2.0). See the LICENSE and NOTICE files in this repository for full attribution.