Updated ReadME with model information

README.md

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-license: cc-by-nc-nd-4.0
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+---
+license: cc-by-nc-nd-4.0
+language:
+- en
+base_model: EleutherAI/pythia-410m
+library_name: transformers
+tags:
+- biology
+- scRNAseq
+---
+
+# Overview
+This is the C2S-Pythia-410m-cell-type-conditioned-cell-generation model, built on the Pythia-410m architecture developed 
+by EleutherAI, fine-tuned using Cell2Sentence (C2S) on a comprehensive collection of single-cell RNA sequencing 
+(scRNA-seq) datasets from CellxGene and the Human Cell Atlas. Cell2Sentence is a pioneering technique that adapts 
+large language models (LLMs) to single-cell biology by converting scRNA-seq data into "cell sentences" — ordered 
+sequences of gene names based on expression levels. This model is specifically trained for cell type-conditioned 
+single-cell generation, enabling the generation of realistic single-cell profiles conditioned on specified cell 
+types.
+
+# 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.
+
+# Tasks
+This model is designed for:
+
+- Cell type-conditioned single-cell generation: Generating single-cell profiles conditioned on specific cell types, allowing for the creation of synthetic cells that reflect the gene expression patterns of targeted cell types.
+
+
+# Cell2Sentence Links
+- GitHub: https://github.com/vandijklab/cell2sentence
+- 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