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mrothroc
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Model Details
Model Provider
mrothroc
Model Tree
Input Modalities
Output Modalities
Supported Functionality
plain causal attention layers, model_dim 384, seq_len 512, nucleotide vocab 9
(A/C/G/T/N + BOS/EOS/PAD/MASK). A standard GPT-style stack, exported as a native Hugging Face
GPT2LMHeadModel.It loads with stock transformers as a GPT2LMHeadModel, no trust_remote_code. Token ids are BOS=1,
EOS=2, PAD=0, and A/C/G/T/N = 4/5/6/7/8.
python
import torchfrom transformers import AutoModelForCausalLMrepo = "mrothroc/dna-enhancers-attention-mixlab"model = AutoModelForCausalLM.from_pretrained(repo).eval() # GPT2LMHeadModelids2base = {4: "A", 5: "C", 6: "G", 7: "T", 8: "N"}out = model.generate(torch.tensor([[1]]), do_sample=True, max_length=60, temperature=1.0, top_k=0)print("".join(ids2base.get(t, "") for t in out[0].tolist() if t in ids2base)) # a DNA string
For classification, load with AutoModelForSequenceClassification and fine-tune. The full recipe is in the
cookbook below.
Full recipe (reproduce this baseline, then improve it by swapping the mixer to Mamba, all on a laptop): mixlab cookbook, genomics-mamba.
Please cite what this builds on: