Fix typos

Great space, thank you. Just fixing a few typos :)

app.py

@@ -6,7 +6,7 @@ from matplotlib.ticker import MultipleLocator
 INTRO = """# Harm's law 
 
 The Chinchilla scaling laws focus on optimally scaling training compute but often we also care about inference cost. 
-This tool follows [Harm de Vries' blog post](https://www.harmdevries.com/post/model-size-vs-compute-overhead/) and visualizes the tradeoff between training comput and inference cost (i.e. model size). 
+This tool follows [Harm de Vries' blog post](https://www.harmdevries.com/post/model-size-vs-compute-overhead/) and visualizes the tradeoff between training compute and inference cost (i.e. model size). 
 """
 
 ### CHINCHILLA PARAMS:
@@ -82,11 +82,11 @@ Your specificied setting corresponds to the following training compute budget.
 **Compute budget (TFLOPs): {C:.2E}**
 
 ## Chinchilla optimal:
-If you are optimizeing for model performance and ignore inference cost this is the optimal setting for training:
+If you are optimizing for model performance and ignore inference cost this is the optimal setting for training:
 
-**Optimal model size: {N_opt/Bn:.2f}B parametes**
+**Optimal model size: {N_opt/Bn:.2f}B parameters**
 
-**Optimal datset size: {D_opt/Bn:.2f}B tokens**
+**Optimal dataset size: {D_opt/Bn:.2f}B tokens**
 
 ## Your setting trade-off:
 Compared to the compute optimal model.