diff --git a/models/.template.yaml b/models/.template.yaml
index 64206f1..8c845ab 100755
--- a/models/.template.yaml
+++ b/models/.template.yaml
@@ -120,7 +120,7 @@ path:
 
 # afaik all units here are measured in **steps** (i.e. one batch of batch_size is 1 unit)
 train: # CHANGEME: ALL OF THESE PARAMETERS SHOULD BE EXPERIMENTED WITH
-  niter: 50000
+  niter: ${iterations}
   warmup_iter: -1
   mega_batch_factor: 4    # <-- Gradient accumulation factor. If you are running OOM, increase this to [2,4,8].
   val_freq: 500
@@ -139,8 +139,8 @@ eval:
       out: [gen, codebook_commitment_loss]
 
 logger: 
-  print_freq: 100
-  save_checkpoint_freq: 500 # CHANGEME: especially you should increase this it's really slow
+  print_freq: ${print_rate}
+  save_checkpoint_freq: ${save_rate} # CHANGEME: especially you should increase this it's really slow
   visuals: [gen, mel]
-  visual_debug_rate: 500
+  visual_debug_rate: ${print_rate}
   is_mel_spectrogram: true
\ No newline at end of file
diff --git a/src/utils.py b/src/utils.py
index 9339a82..3ce6da9 100755
--- a/src/utils.py
+++ b/src/utils.py
@@ -498,9 +498,10 @@ def setup_tortoise(restart=False):
 	print("TorToiSe initialized, ready for generation.")
 	return tts
 
-def save_training_settings( batch_size=None, learning_rate=None, print_rate=None, save_rate=None, name=None, dataset_name=None, dataset_path=None, validation_name=None, validation_path=None, output_name=None ):
+def save_training_settings( iterations=None, batch_size=None, learning_rate=None, print_rate=None, save_rate=None, name=None, dataset_name=None, dataset_path=None, validation_name=None, validation_path=None, output_name=None ):
 	settings = {
-		"batch_size": batch_size if batch_size else 128,
+		"iterations": iterations if iterations else 500,
+		"batch_size": batch_size if batch_size else 64,
 		"learning_rate": learning_rate if learning_rate else 1e-5,
 		"print_rate": print_rate if print_rate else 50,
 		"save_rate": save_rate if save_rate else 50,
diff --git a/src/webui.py b/src/webui.py
index 1186b1e..2cca424 100755
--- a/src/webui.py
+++ b/src/webui.py
@@ -201,7 +201,7 @@ def read_generate_settings_proxy(file, saveAs='.temp'):
 def prepare_dataset_proxy( voice, language, progress=gr.Progress(track_tqdm=True) ):
 	return prepare_dataset( get_voices(load_latents=False)[voice], outdir=f"./training/{voice}/", language=language, progress=progress )
 
-def save_training_settings_proxy( batch_size, learning_rate, print_rate, save_rate, voice ):
+def save_training_settings_proxy( iterations, batch_size, learning_rate, print_rate, save_rate, voice ):
 	name = f"{voice}-finetune"
 	dataset_name = f"{voice}-train"
 	dataset_path = f"./training/{voice}/train.txt"
@@ -217,7 +217,7 @@ def save_training_settings_proxy( batch_size, learning_rate, print_rate, save_ra
 
 	out_name = f"{voice}/train.yaml"
 
-	return save_training_settings(batch_size, learning_rate, print_rate, save_rate, name, dataset_name, dataset_path, validation_name, validation_path, out_name )
+	return save_training_settings(iterations, batch_size, learning_rate, print_rate, save_rate, name, dataset_name, dataset_path, validation_name, validation_path, out_name )
 
 def update_voices():
 	return (
@@ -346,7 +346,8 @@ def setup_gradio():
 				with gr.Row():
 					with gr.Column():
 						training_settings = [
-							gr.Slider(label="Batch Size", value=128),
+							gr.Slider(label="Iterations", minimum=0, maximum=5000, value=500),
+							gr.Slider(label="Batch Size", minimum=2, maximum=128, value=64),
 							gr.Slider(label="Learning Rate", value=1e-5, minimum=0, maximum=1e-4, step=1e-6),
 							gr.Number(label="Print Frequency", value=50),
 							gr.Number(label="Save Frequency", value=50),