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--- |
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license: apache-2.0 |
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tags: |
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- pretrained |
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- mistral |
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- dna |
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- biology |
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- genomics |
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--- |
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# Model Card for Mistral-DNA-v0.1 (mistral for DNA) |
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The Mistral-DNA-v0.1 Large Language Model (LLM) is a pretrained generative DNA text model with 1.64M parameters x 64 experts = 105M parameters. |
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It is derived from Mistral-7B-v0.1 model, which was simplified for DNA: the number of layers and the hidden size were reduced. |
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The model was pretrained using the human genome hg38 with 200b DNA sequences. |
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*This version v0.1 of Mistral-DNA corresponds to a pretty simple model, which was primarly designed for low computational resources (the aim was not to get the best accuracy results).* |
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For full details of this model please read our [github repo](https://github.com/raphaelmourad/Mistral-DNA). |
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## Model Architecture |
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Like Mistral-7B-v0.1, it is a transformer model, with the following architecture choices: |
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- Grouped-Query Attention |
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- Sliding-Window Attention |
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- Byte-fallback BPE tokenizer |
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## Load the model from huggingface: |
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``` |
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import torch |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("RaphaelMourad/Mistral-DNA-v0.1", trust_remote_code=True) # Same as DNABERT2 |
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model = AutoModel.from_pretrained("RaphaelMourad/Mistral-DNA-v0.1", trust_remote_code=True) |
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``` |
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## Calculate the embedding of a DNA sequence |
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``` |
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dna = "TGATGATTGGCGCGGCTAGGATCGGCT" |
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inputs = tokenizer(dna, return_tensors = 'pt')["input_ids"] |
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hidden_states = model(inputs)[0] # [1, sequence_length, 256] |
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# embedding with max pooling |
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embedding_max = torch.max(hidden_states[0], dim=0)[0] |
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print(embedding_max.shape) # expect to be 256 |
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``` |
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## Troubleshooting |
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Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer. |
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## Notice |
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Mistral-DNA is a pretrained base model for DNA. |
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## Contact |
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Raphaël Mourad. raphael.mourad@univ-tlse3.fr |
