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pabloarozarenad
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Model Details
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pabloarozarenad
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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, AutoTokenizer tokenizer = 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 embedding embedding = 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
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = 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()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 embedding embedding = torch.max(logits, dim=1).values[0] print(embedding.shape) # torch.Size([4096])