edited readme formatting

README.md

@@ -1,25 +1,20 @@
 ---
-license: apache-2.0
+license: cc-by-nc-nd-4.0
 ---
-##Overview
+# Overview
 
 This model uses Cell2Sentence fine-tuning on the Pythia-160m model developed by EleutherAI.
 
-Cell2Sentence Links:
-
-GitHub: <https://github.com/vandijklab/cell2sentence-ft>
-
+## Cell2Sentence Links:  
+GitHub: <https://github.com/vandijklab/cell2sentence-ft>  
 Paper: <https://www.biorxiv.org/content/10.1101/2023.09.11.557287v3>
 
-Pythia Links
-
-GitHub: <https://github.com/EleutherAI/pythia>
-
-Paper: <https://arxiv.org/abs/2304.01373>
-
+## Pythia Links:  
+GitHub: <https://github.com/EleutherAI/pythia>  
+Paper: <https://arxiv.org/abs/2304.01373>  
 Hugging Face: <https://huggingface.co/EleutherAI/pythia-160m>
 
-##Model Details
+# Model Details
 
 Cell2Sentence is a novel method for adapting large language models to single-cell transcriptomics. 
 We transform single-cell RNA sequencing data into sequences of gene names ordered by expression level, termed "cell sentences". 
@@ -29,7 +24,7 @@ This model is trained on the immune tissue dataset from [Domínguez et al.](http
 2. unconditional cell generation
 3. cell type prediction
 
-##Sample Code
+# Sample Code
 
 We provide an example of how to use the model to conditionally generate a cell equipped with a post-processing function to remove duplicate and invalid genes. 
 Unconditional cell generation and cell type prediction prompts are included as well, but we do not include an example cell sentence to format the prompt.