vandijklab/C2S-Pythia-410m-cell-type-prediction
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Model: vandijklab/C2S-Pythia-410m-cell-type-prediction
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2026-07-11 10:45:09 +08:00
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---
language:
- en
base_model: EleutherAI/pythia-410m
library_name: transformers
tags:
- biology
- scRNAseq
license: cc-by-4.0
---
# Overview
This is the C2S-Pythia-410m-cell-type-prediction model, based on the Pythia-410m architecture developed by EleutherAI,
fine-tuned using Cell2Sentence (C2S) on a diverse set of single-cell RNA sequencing (scRNA-seq) datasets from CellxGene
and the Human Cell Atlas. Cell2Sentence is an innovative approach for adapting large language models (LLMs) to
single-cell biology by transforming scRNA-seq data into "cell sentences"—sequences of gene names ordered by
expression levels. This transformation enables LLMs to leverage their natural language processing capabilities for
various single-cell tasks, with a focus on cell type prediction in this model.
# Training Data
This model was trained on over 57 million human and mouse cells gathered from over 800 single-cell RNA sequencing
datasets from CellxGene and the Human Cell Atlas. This dataset covers a broad range of cell types and conditions
from multiple tissues in both human and mouse.
This model was trained with the top 200 genes per cell sentence.
# Tasks
This model is designed for:
- Cell type prediction: Predicting the cell type based on the "cell sentence" generated from scRNA-seq data.
# Cell2Sentence Links
- GitHub: https://github.com/vandijklab/cell2sentence (Note: Codebase has Apache 2.0 license, weights shared on HuggingFace are CC-by-4.0)
- Paper: https://www.biorxiv.org/content/10.1101/2023.09.11.557287v3
# Pythia Links
- Paper: https://arxiv.org/pdf/2304.01373
- Hugging Face: https://huggingface.co/EleutherAI/pythia-410m

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