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license: apache-2.0
library_name: transformers
tags:
- dna
- genomics
- dna-language-model
- mamba
- moe
pipeline_tag: text-generation
---
# CENO-80M-base
**CENO-80M-base** is the base pretraining checkpoint of the 80M **CENO** DNA foundation model — a causal language model over genomic sequence built on a
Nemotron-H **Mamba / Attention / Mixture-of-Experts** hybrid backbone (no 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](https://github.com/CladeTeam/CENO).
This checkpoint is standalone-loadable with `trust_remote_code=True` — the model code is
bundled here.
## Model details
| | |
|---|---|
| Family | CENO (base) |
| Training stage | Base pretraining (stage 2) |
| Parameters | 79.3M |
| Precision | bfloat16 |
| `model_type` | `ceno` |
| Architecture class | `CENOForCausalLM` |
| Auto-map (model) | `modeling_ceno.CENOForCausalLM` |
| Auto-map (tokenizer) | `ceno_tokenizer.CENOCharLevelTokenizer` |
## Architecture
| Property | Value |
|---|---|
| Hidden layers | 9 |
| Hidden size | 512 |
| Attention heads | 8 |
| Intermediate size | 2048 |
| 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-80M-base"
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](https://github.com/CladeTeam/CENO).
## 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.
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