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C2S-Scale-Gemma-2B is a state-of-the-art, open language model built upon the Gemma-2 2B architecture and fine-tuned for single-cell biology. Developed through the Cell2Sentence (C2S) framework, the model processes and understands single-cell RNA sequencing (scRNA-seq) data by treating it as a language. It converts high-dimensional scRNA-seq expression data into "cell sentences" - ordered sequences of gene names - enabling a wide range of biological analyses. This work is the result of a collaboration between Yale University, Google Research, and Google DeepMind to scale up C2S models. The C2S-Scale models were trained on Google's TPU v5s, which allowed for a significant increase in model size and capability. These models excel at tasks such as cell type prediction, tissue classification, and generating biologically meaningful cell representations. Below are code snippets to help you get started running the model locally on a GPU. The model can be used for various tasks, further described in the C2S-Scale paper.

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