Update README.md

README.md

@@ -12,10 +12,6 @@ This is the smallest GPT-2 model (124m) from OpenAi finetuned on approximately 2
 
 The model was trained with a learning rate of 1e-4, with a warmup of 1024 steps, then decaying to 0. There were 4400 total steps during training at a batch size of 512 examples with a context length of 1024. The batch size and context length are the same as the pre-training of GPT2 itself. Training took a total of 1.18e+18 FLOs over the course of 79.32 hours locally with a 12gb RTX3060. Final train loss was 2.73.
 
-The resulting model achieves a puplexity of 339.38, making it competative with Cerebras-590m with only 21% of the parameters, and much better than the original GPT-2 which scores 491.57!
-
-(metric explanation here: https://twitter.com/aicrumb/status/1650350363898265601 , tldr it's a joke but only kind of)
-
 ### Evaluation of GPT2023
 
 *(in progress)*
@@ -30,6 +26,10 @@ The resulting model achieves a puplexity of 339.38, making it competative with C
 | gpt2 (124m) | **62.89** | **51.61** | 40.06 | 32.56 | **19.03** | 75 | **43.27** |
 | gpt2023 (124m) | 62.02 | 49.64 | **34.55** | **33.98** | 18.94 | **76.1** | 36.54 |
 
+The resulting model achieves a puplexity of 339.38, making it competative with Cerebras-590m with only 21% of the parameters, and much better than the original GPT-2 which scores 491.57!
+
+(metric explanation here: https://twitter.com/aicrumb/status/1650350363898265601 , tldr it's a joke but only kind of)
+
 
 ### Model description