--- license: apache-2.0 tags: - pretrained - mistral - DNA - virus (both rna and dna) - biology - genomics --- # Model Card for Mistral-DNA-v1-138M-virus (mistral for DNA) The Mistral-DNA-v1-138M-virus Large Language Model (LLM) is a pretrained generative DNA text model with 17.31M parameters x 8 experts = 138.5M parameters. It is derived from Mistral-7B-v0.1 model, which was simplified for DNA: the number of layers and the hidden size were reduced. The model was pretrained using around 15071 viruses > 1kb. Virus genome database was downloaded from https://www.ncbi.nlm.nih.gov/labs/virus/vssi/#/virus?SeqType_s=Genome&VirusLineage_ss=taxid:10239&SourceDB_s=RefSeq. NB: the DNA sequence was used, not the RNA sequence. For full details of this model please read our [github repo](https://github.com/raphaelmourad/Mistral-DNA). ## Model Architecture Like Mistral-7B-v0.1, it is a transformer model, with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer ## Load the model from huggingface: ``` import torch from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RaphaelMourad/Mistral-DNA-v1-138M-virus", trust_remote_code=True) # Same as DNABERT2 model = AutoModel.from_pretrained("RaphaelMourad/Mistral-DNA-v1-138M-virus", trust_remote_code=True) ``` ## Calculate the embedding of a DNA sequence ``` dna = "TGATGATTGGCGCGGCTAGGATCGGCT" inputs = tokenizer(dna, return_tensors = 'pt')["input_ids"] hidden_states = model(inputs)[0] # [1, sequence_length, 256] # embedding with max pooling embedding_max = torch.max(hidden_states[0], dim=0)[0] print(embedding_max.shape) # expect to be 256 ``` ## Troubleshooting Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer. ## Notice Mistral-DNA-v1-138M-virus is a pretrained base model for virus genomes. ## Contact Raphaƫl Mourad. raphael.mourad@univ-tlse3.fr