PlasmidLM-kmer6

A 19.3M parameter autoregressive language model for plasmid DNA sequence generation, trained on ~100K plasmid sequences from Addgene.

Model Details

Property	Value
Parameters	19.3M
Architecture	Transformer decoder (dense MLP)
Hidden size	384
Layers	10
Attention heads	8
Intermediate size	1,536
Max sequence length	16,384 tokens
Tokenizer	k-mer (k=6, stride=3)
Vocab size	4,208

Training

• Data: ~100K plasmid sequences from Addgene, tokenized with k-mer (k=6, stride=3)
• Steps: 65,000
• Eval loss: 0.129
• Token accuracy: 97.4%

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("McClain/PlasmidLM-kmer6", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("McClain/PlasmidLM-kmer6", trust_remote_code=True)

# Condition on antibiotic resistance + origin of replication
prompt = "<BOS><AMR_KANAMYCIN><ORI_COLE1><SEP>"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.8, do_sample=True, top_p=0.95)
print(tokenizer.decode(outputs[0].tolist()))

The model generates plasmid DNA sequences conditioned on functional annotations (antibiotic resistance markers, origins of replication) provided as special tokens in the prompt.

Special Tokens

Token	Purpose
<BOS>	Beginning of sequence
<EOS>	End of sequence
<SEP>	Separator between prompt annotations and DNA sequence
<PAD>	Padding
<AMR_*>	Antibiotic resistance markers (e.g., <AMR_KANAMYCIN>, <AMR_AMPICILLIN>)
<ORI_*>	Origins of replication (e.g., <ORI_COLE1>, <ORI_P15A>)

Citation

If you use this model, please cite:

@misc{thiel2026plasmidlm,
  title={PlasmidLM: Language Models for Plasmid DNA Generation},
  author={Thiel, McClain},
  year={2026}
}