CENO-P-300M
CENO-P-300M is the multi-species alignment (MSA) post-trained variant of the 300M 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 | 307.5M |
| 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 | 9 |
| Hidden size | 1024 |
| Attention heads | 8 |
| 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
from transformers import AutoModelForCausalLM, AutoTokenizer
ckpt = "CladeTeam/CENO-P-300M"
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.