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pip install transformers torch mamba-ssm causal-conv1d flash-attn1from transformers import AutoTokenizer, AutoModelForCausalLM
2
3model_name = "Mishamq/HybriDNA-300M"
4tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
5model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
6
7prompt = "ACGTACGT"
8inputs = tokenizer(prompt, return_tensors="pt")
9outputs = model.generate(**inputs, max_new_tokens=64)
10print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])1from transformers import AutoTokenizer, AutoModel
2import torch
3
4model_name = "Mishamq/HybriDNA-300M"
5tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
6model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
7
8sequence = "ACGTACGTACGTACGT"
9inputs = tokenizer(sequence, return_tensors="pt")
10
11with torch.no_grad():
12 outputs = model(**inputs)
13 embeddings = outputs.last_hidden_state| Model | Parameters | Hidden Size | Layers |
|---|---|---|---|
| HybriDNA-300M | 300M | 1024 | 24 |
| HybriDNA-3B | 3B | 4096 | 16 |
| HybriDNA-7B | 7B | 4096 | 32 |
1@article{ma2025hybridna,
2 title={HybriDNA: A Hybrid Transformer-Mamba2 Long-Range DNA Language Model},
3 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},
4 journal={arXiv preprint arXiv:2502.10807},
5 year={2025}
6}