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license: apache-2.0 |
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##Overview |
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This model uses Cell2Sentence fine-tuning on the Pythia-160m model developed by EleutherAI. |
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Cell2Sentence Links: |
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GitHub: <https://github.com/vandijklab/cell2sentence-ft> |
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Paper: <https://www.biorxiv.org/content/10.1101/2023.09.11.557287v3> |
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Pythia Links |
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GitHub: <https://github.com/EleutherAI/pythia> |
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Paper: <https://arxiv.org/abs/2304.01373> |
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Hugging Face: <https://huggingface.co/EleutherAI/pythia-160m> |
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##Model Details |
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Cell2Sentence is a novel method for adapting large language models to single-cell transcriptomics. |
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We transform single-cell RNA sequencing data into sequences of gene names ordered by expression level, termed "cell sentences". |
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For more details, we refer to the paper linked above. |
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This model is trained on the immune tissue dataset from [Domínguez et al.](https://www.science.org/doi/10.1126/science.abl5197) on the following tasks: |
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1. conditional cell generation |
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2. unconditional cell generation |
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3. cell type prediction |
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