update readme

README.md

@@ -3,7 +3,15 @@ license: cc-by-nc-nd-4.0
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 # Overview
 
-This model uses Cell2Sentence fine-tuning on the Pythia-160m model developed by EleutherAI.
+This is the the Pythia-160m model developed by EleutherAI fine-tuned using Cell2Sentence on *full* scRNA-seq cells.
+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". 
+For more details, we refer to the paper linked below. 
+This model was trained on the immune tissue dataset from [Domínguez et al.](https://www.science.org/doi/10.1126/science.abl5197) 
+using 8 A100 40GB GPUs for approximately 20 hours on the following tasks:
+1. conditional cell generation
+2. unconditional cell generation
+3. cell type prediction
 
 ## Cell2Sentence Links:  
 GitHub: <https://github.com/vandijklab/cell2sentence-ft>  
@@ -14,17 +22,6 @@ GitHub: <https://github.com/EleutherAI/pythia>
 Paper: <https://arxiv.org/abs/2304.01373>  
 Hugging Face: <https://huggingface.co/EleutherAI/pythia-160m>
 
-# 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". 
-For more details, we refer to the paper linked above. 
-This model was trained on the immune tissue dataset from [Domínguez et al.](https://www.science.org/doi/10.1126/science.abl5197) using 8 A100 40GB GPUs 
-on the following tasks:
-1. conditional cell generation
-2. unconditional cell generation
-3. cell type prediction
-
 # 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.