C2S-Scale-Gemma-2-2B

Model overview

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.

Key features

特点摘要待依据来源补齐，不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

Frequently asked questions

Sources & traceability

许可证：cc-by-4.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。