---
license: apache-2.0
language:
- en
library_name: transformers
tags:
- genomics
- dna
- mamba
- hybrid
- biology
---

# HybriDNA-300M

HybriDNA is a hybrid Mamba-Attention model for DNA sequence modeling. This is the 300M parameter variant.

## Model Description

HybriDNA combines the efficiency of Mamba state space models with the expressiveness of attention mechanisms in a hybrid architecture. The model alternates between Mamba and Attention layers to achieve both computational efficiency and strong sequence modeling capabilities.

### Architecture

- **Parameters**: ~300M
- **Hidden Size**: 1024
- **Layers**: 24 (hybrid Mamba + Attention)
- **Attention Heads**: 32
- **Key-Value Heads**: 8 (Grouped Query Attention)
- **Mamba Version**: Mamba-2
- **Vocabulary**: 12 tokens (A, C, G, T, N + special tokens)
- **Max Sequence Length**: 131,074 bp

## Installation

```bash
pip install transformers torch mamba-ssm causal-conv1d flash-attn
```

## Usage

### Text Generation

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Mishamq/HybriDNA-300M"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)

prompt = "ACGTACGT"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
```

### Embeddings

```python
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "Mishamq/HybriDNA-300M"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(model_name, trust_remote_code=True)

sequence = "ACGTACGTACGTACGT"
inputs = tokenizer(sequence, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
    embeddings = outputs.last_hidden_state
```

## Model Variants

| Model | Parameters | Hidden Size | Layers |
|-------|------------|-------------|--------|
| [HybriDNA-300M](https://huggingface.co/Mishamq/HybriDNA-300M) | 300M | 1024 | 24 |
| HybriDNA-3B | 3B | 4096 | 16 |
| HybriDNA-7B | 7B | 4096 | 32 |

## Citation

If you use HybriDNA in your research, please cite:

```bibtex
@article{ma2025hybridna,
  title={HybriDNA: A Hybrid Transformer-Mamba2 Long-Range DNA Language Model},
  author={Ma, Mingqian and Liu, Guoqing and Cao, Chuan and Deng, Pan and Dao, Tri and Gu, Albert and Jin, Peiran and Yang, Zhao and Xia, Yingce and Luo, Renqian and others},
  journal={arXiv preprint arXiv:2502.10807},
  year={2025}
}
```

## License

Apache 2.0