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pabloarozarenad
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Model Details
Model Provider
pabloarozarenad
Model Tree
RaphaelMourad/Mistral-DNA-v1-422M-hg38
Input Modalities
Output Modalities
Supported Functionality
Vir2vec is a viral genomic language model (gLM) designed to produce fixed-length, genome-level embeddings that can be fine-tuned across downstream tasks such as viral discrimination, host-range prediction, and variant typing. For more details and training scripts check GitHub
Vir2vec embeddings are intended for tasks including (but not limited to):
All Vir2vec model scales produce fixed-length 4,096-dimensional genome-level embeddings via max-pooled vocabulary logits:
main branch)revision="138M")revision="17M")python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizertokenizer = AutoTokenizer.from_pretrained("pabloarozarenad/Vir2vec", trust_remote_code=True) # Add revision=138M or revision=17M to change model size. 422M is default.model = AutoModelForCausalLM.from_pretrained("pabloarozarenad/Vir2vec", trust_remote_code=True) # Add revision=138M or revision=17M to change model size. 422M is default.model.eval()
python
dna = "ACGTAGCATCGCGATGACTGCATCACT"inputs = tokenizer(dna, return_tensors="pt")with torch.no_grad():# Forward pass to obtain vocabulary logits [batch, seq_len, 4096]outputs = model(**inputs)logits = outputs.logits# Max-pool logits over token positions -> 4,096-dimensional embeddingembedding = torch.max(logits, dim=1).values[0]print(embedding.shape) # torch.Size([4096])
Vir2vec can be loaded upon request, subject to providing an institutional email address, a brief description of the intended use, and the associated IRB protocol number. For more info contact pablo.arozarenadonel01@universitadipavia.it