---
license: apache-2.0
library_name: transformers
tags:
  - dna
  - genomics
  - dna-language-model
  - mamba
  - moe
pipeline_tag: text-generation
---

# CENO-600M-base

**CENO-600M-base** is the base pretraining checkpoint of the 600M **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 | 630.8M |
| 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 | 20 |
| 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-600M-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.


<!-- CENO_INFERENCE_CURRENT -->
## Current inference implementation

Model weights and token IDs are unchanged. Current source and usage instructions
are maintained at [CladeTeam/CENO](https://github.com/CladeTeam/CENO).
Use the updated model revision instead of historical diagnostic archives.

CUDA BF16 inference uses the corrected Mamba, MoE, normalization and attention
arithmetic selected by this model's explicit reference configuration. Loading
uses the checkpoint dtype by default; explicit caller dtype overrides are honored.
Use `torch_dtype="auto"`, `.cuda().eval()` and `torch.inference_mode()` for raw
forward calls. `generate()` enters no-grad mode automatically.

The HF precision path is single-GPU, unpadded, and permits at most 131072 tokens
per prefill. Use the repository's vLLM entry for larger prefills. CPU, explicit
FP32 and explicit eager attention retain ordinary compatible behavior and are
not covered by the BF16 bitwise-reference claim. Different Megatron CP references
are not interchangeable. The long 1M reference evidence belongs to CENO-1B-1m;
short multi-size checks do not imply the same long coverage for every model.

The tested HF stack is PyTorch 2.7.0a0 (NVIDIA 25.03), Transformers 4.48.3,
mamba-ssm 2.2.4, causal-conv1d 1.2.2.post1 and FlashAttention 2.7.3. HF and vLLM
use separate environments. Numerical correctness is not a guarantee of
repetition-free or biologically functional generation.
<!-- /CENO_INFERENCE_CURRENT -->