Update README.md

README.md

@@ -75,25 +75,21 @@ Data for the fine-tuning comes from 2 sources: (1) mannual collection and (2) sy
 The data is structured in an Alpaca format, with each training example consisting of Prompt (description of task, JSON schema, and one-shot example), Input (an elite's biographical text), and Output (JSON record).  
 
 ### Training Procedure
-
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-
 #### Training Hyperparameters
 
 - **Training regime:** <!-- bf16 non-mixed precision --> bf16 non-mixed precision
 
 #### Speeds, Sizes, Times [optional]
-
 <!-- This section provides information about throughput, start/end time, etc. -->
-
-Num Epochs = 3 | Total steps = 99  
-Batch size per device = 2 | Gradient accumulation steps = 4  
-Data Parallel GPUs = 1 | Total batch size (2 x 4 x 1) = 8  
-Trainable parameters = 83,886,080/8,000,000,000 (1.05% trained)  
-38.48 minutes used for training.  
-Peak reserved memory = 10.107 GB.  
-Peak reserved memory for training = 4.189 GB.  
+- Num Epochs = 3 | Total steps = 99  
+- Batch size per device = 2 | Gradient accumulation steps = 4  
+- Data Parallel GPUs = 1 | Total batch size (2 x 4 x 1) = 8  
+- Trainable parameters = 83,886,080/8,000,000,000 (1.05% trained)  
+- 38.48 minutes used for training.  
+- Peak reserved memory = 10.107 GB.  
+- Peak reserved memory for training = 4.189 GB.  
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/67b5c53dd6a178c46d7f3767/mARFkSRyxxliZXLyc36kt.png)