diff --git a/examples/gpt2.py b/examples/gpt2.py
index 5a818a9c..b36e0d14 100644
--- a/examples/gpt2.py
+++ b/examples/gpt2.py
@@ -9,8 +9,8 @@ from tinygrad.nn import Embedding, Linear, LayerNorm
 from tinygrad.shape.symbolic import Variable
 from tinygrad.jit import TinyJit
 import tiktoken
-from tinygrad.nn.state import torch_load, load_state_dict
-from tinygrad.helpers import GlobalCounters, Timing, DEBUG, getenv, fetch, colored, CI
+from tinygrad.nn.state import torch_load, load_state_dict, get_state_dict
+from tinygrad.helpers import GlobalCounters, Timing, DEBUG, getenv, fetch, colored, dtypes
 
 MAX_CONTEXT = getenv("MAX_CONTEXT", 128)
 
@@ -154,8 +154,10 @@ if __name__ == "__main__":
   parser.add_argument('--model_size', type=str, default="gpt2-medium", help="Size of model to use [gpt2, gpt2-medium, gpt2-large, gpt2-xl]")
   parser.add_argument('--timing', action='store_true', help="Print timing per token")
   parser.add_argument('--seed', type=int, help="Set the random seed")
-  parser.add_argument('--batch_size', type=int, default=1, help="Size of model to use [gpt2, gpt2-medium, gpt2-large, gpt2-xl]")
+  parser.add_argument('--batch_size', type=int, default=1, help="Set the input batch size")
   parser.add_argument('--benchmark', type=int, default=-1, help="Benchmark GPT with the given number of tokens")
+  parser.add_argument('--fp16', action='store_true', help="Cast the weights to float16")
+  parser.add_argument('--noshow', action='store_true', help="Don't show the output")
   args = parser.parse_args()
 
   if args.seed is not None:
@@ -165,11 +167,16 @@ if __name__ == "__main__":
   print(f"using {args.model_size}")
   gpt2 = GPT2.build(args.model_size)
 
+  if args.fp16:
+    for l in get_state_dict(gpt2).values():
+      l.assign(l.cast(dtypes.float16).realize())
+
   if args.benchmark != -1:
     gpt2.model(Tensor.rand(args.batch_size, args.benchmark), Variable("a", 0, MAX_CONTEXT).bind(0)).realize()
   else:
-    print('Generating text...')
     texts = gpt2.greedy_until(args.prompt, args.count, args.temperature, timing=args.timing, batch_size=args.batch_size)
-    if len(texts) == 1: print(texts[0])
-    else:
-      for i,text in enumerate(texts): print(colored(f"Response {i}:", "green"), text)
\ No newline at end of file
+    if not args.noshow:
+      print('Generating text...')
+      if len(texts) == 1: print(texts[0])
+      else:
+        for i,text in enumerate(texts): print(colored(f"Response {i}:", "green"), text)
\ No newline at end of file