diff --git a/samples/dnn/gpt2_inference.py b/samples/dnn/gpt2_inference.py
index 8f0ddcf7d7..f9a5a2b072 100644
--- a/samples/dnn/gpt2_inference.py
+++ b/samples/dnn/gpt2_inference.py
@@ -30,21 +30,17 @@ Run the script:
     python gpt2_inference.py --model=<path-to-onnx-model>  --prompt=<use-promt-of-the-same-length-used-while-exporting>
 '''
 
-
-
 import numpy as np
 import tiktoken
 import argparse
 import cv2 as cv
-from tqdm import tqdm
 
 def parse_args():
     parser = argparse.ArgumentParser(description='Use this script to run GPT-2 inference in OpenCV',
                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
     parser.add_argument('--model', type=str, required=True, help='Path to GPT-2 model ONNX model file.')
     parser.add_argument("--prompt", type=str, default="Hello, I'm a language model,", help="Prompt to start with.")
-    parser.add_argument("--max_seq_len", type=int, default=40, help="Number of tokens to continue.")
-    parser.add_argument("--batch_size", type=int, default=1, help="Number of batches.")
+    parser.add_argument("--max_seq_len", type=int, default=1024, help="Number of tokens to continue.")
     parser.add_argument("--seed", type=int, default=0, help="Random seed")
     return parser.parse_args()
 
@@ -53,21 +49,27 @@ def stable_softmax(logits):
     return exp_logits / np.sum(exp_logits, axis=-1, keepdims=True)
 
 
-def gpt2_inference(net, tokens, max_length, num_return_sequences=1):
+
+def gpt2_inference(net, tokens, max_length, tokenizer):
 
     print("Inferencing GPT-2 model...")
     x = np.array(tokens)
-    x = np.tile(x, (num_return_sequences, 1)).astype(np.int32)
+    x = np.tile(x, (1, 1)).astype(np.int32)
     pos = np.arange(0, len(x), dtype=np.int32)
 
-    counter = x.shape[1]
-    pbar = tqdm(total=max_length - counter, desc="Generating tokens")
-    while counter < max_length:
+    # warm up
+    net.setInputsNames(['input_ids', 'position_ids'])
+    net.setInput(x, 'input_ids')
+    net.setInput(pos, 'position_ids')
+    logits = net.forward()
+
+    stop_tokens = (50256, ) ## could be extended to include more stop tokens
+    print("\n", tokenizer.decode(tokens), sep="", end="")
+    while 0 < max_length and x[:, -1] not in stop_tokens:
 
         net.setInputsNames(['input_ids', 'position_ids'])
         net.setInput(x, 'input_ids')
         net.setInput(pos, 'position_ids')
-
         logits = net.forward()
 
         # logits is assumed to be (B, seq_length, vocab_size) and needs to be the last token's logits
@@ -87,23 +89,27 @@ def gpt2_inference(net, tokens, max_length, num_return_sequences=1):
         sampled_indices = [np.random.choice(topk_indices[i], p=topk_probs[i]) for i in range(len(topk_probs))]
         sampled_indices = np.array(sampled_indices).reshape(-1, 1)
 
+        # Decode and print the new token
+        new_word = tokenizer.decode([sampled_indices[0, 0]])
+
+        ## clean the prints from the previous line
+        print(new_word, end='', flush=True)
+
         # Append to the sequence
         x = np.concatenate((x, sampled_indices), axis=1)
         pos = np.arange(0, x.shape[1], dtype=np.int32) # shape (T)
 
-        counter += 1
-        pbar.update(1)
+        max_length -= 1
 
-    pbar.close()
-    print("Inference done!")
-    return x
+    print('\n')
 
 if __name__ == '__main__':
 
     args = parse_args()
+    print("Preparing GPT-2 model...")
+
     np.random.seed(args.seed)
     max_length = args.max_seq_len
-    num_return_sequences = args.batch_size
     prompt = args.prompt
 
     net = cv.dnn.readNet(args.model)
@@ -111,9 +117,4 @@ if __name__ == '__main__':
     enc = tiktoken.get_encoding('gpt2')
     tokens = enc.encode(prompt)
 
-    output = gpt2_inference(net, tokens, max_length, num_return_sequences)
-
-    for i in range(num_return_sequences):
-        tokens = output[i].tolist()
-        decoded = enc.decode(tokens)
-        print(">>>>", decoded)
+    gpt2_inference(net, tokens, max_length, enc)