added details about inference example

README.md

@@ -19,7 +19,8 @@ Hugging Face: <https://huggingface.co/EleutherAI/pythia-160m>
 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 is trained on the immune tissue dataset from [Domínguez et al.](https://www.science.org/doi/10.1126/science.abl5197) on the following tasks:
+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
@@ -27,6 +28,8 @@ This model is trained on the immune tissue dataset from [Domínguez et al.](http
 # 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. 
+In order to generate full cells, the `max_length` generation parameter should be changed to 9200. 
+However, we recommend using an A100 GPU for inference speed and memory capacity if full cell generation is required. 
 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. 
 We refer to the paper and GitHub repository for instructions on how to transform expression vectors into cell sentences.
 
@@ -101,7 +104,9 @@ ccg = f"Enumerate the genes in a {cell_type} cell with nonzero expression, from
 # Prompts for other forms a generation.
 # ucg = "Display a cell's genes by expression level, in descending order."
 # cellsentence = "CELL_SENTENCE"
-# ctp = f"Identify the cell type most likely associated with these highly expressed genes listed in descending order. {cellsentence} Name the cell type connected to these genes, ranked from highest to lowest expression."
+# ctp = "Identify the cell type most likely associated with these highly expressed genes listed in descending order. "
+#  + cellsentence +
+#  "Name the cell type connected to these genes, ranked from highest to lowest expression."
 
 tokens = tokenizer(ccg, return_tensors='pt')
 input_ids = tokens['input_ids'].to(torch.device("cuda"))