Update README.md

README.md

@@ -43,12 +43,13 @@ ReidLM, like all large language models, has inherent biases and limitations that
 
  This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. 
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.--->
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.-->
 
 ## Getting Started with the Model
 
 Use the code below to get started with the model.
 ## Use with Transformers AutoModelForCausalLM
+
 ```
 import transformers
 import torch
@@ -69,11 +70,9 @@ generated_text = generate_text(prompt)
 print(generated_text)
 ```
 
-<br>
-
 ## Training Details
 
-
+<!-- -->
 ### Training Data
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
@@ -86,18 +85,18 @@ print(generated_text)
 
 #### Training Hyperparameters
 
-    num_train_epochs=3, <br>
-    per_device_train_batch_size=4,<br>
-    gradient_accumulation_steps=2,<br>
-    optim="paged_adamw_8bit",<br>
-    save_steps=1000,<br>
-    logging_steps=30,<br>
-    learning_rate=2e-4,<br>
-    weight_decay=0.01,<br>
-    fp16=True,<br>
-    max_grad_norm=1.0,<br>
-    warmup_ratio=0.1<br><!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
+  num_train_epochs=3, <br>
+  per_device_train_batch_size=4,<br>
+  gradient_accumulation_steps=2,<br>
+  optim="paged_adamw_8bit",<br>
+  save_steps=1000,<br>
+  logging_steps=30,<br>
+  learning_rate=2e-4,<br>
+  weight_decay=0.01,<br>
+  fp16=True,<br>
+  max_grad_norm=1.0,<br>
+  warmup_ratio=0.1<br>
+  <!-- -->
 <!---#### Speeds, Sizes, Times [optional]
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->