diff --git a/sample.py b/sample.py
index f02308e..8b42c8f 100644
--- a/sample.py
+++ b/sample.py
@@ -9,7 +9,8 @@ import tiktoken
 from model import GPTConfig, GPT
 
 # -----------------------------------------------------------------------------
-out_dir = 'out'
+init_from = 'resume' # either 'resume' (from an out_dir) or a gpt2 variant (e.g. 'gpt2-xl')
+out_dir = 'out' # ignored if init_from is not 'resume'
 start = "\n" # or "<|endoftext|>" or whatever you like
 num_samples = 10 # number of samples to draw
 max_new_tokens = 500 # number of tokens generated in each sample
@@ -31,16 +32,22 @@ ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torc
 ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(device_type=device_type, dtype=ptdtype)
 
 # model
-ckpt_path = os.path.join(out_dir, 'ckpt.pt')
-checkpoint = torch.load(ckpt_path, map_location=device)
-gptconf = GPTConfig(**checkpoint['model_args'])
-model = GPT(gptconf)
-state_dict = checkpoint['model']
-unwanted_prefix = '_orig_mod.'
-for k,v in list(state_dict.items()):
-    if k.startswith(unwanted_prefix):
-        state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
-model.load_state_dict(state_dict)
+if init_from == 'resume':
+    # init from a model saved in a specific directory
+    ckpt_path = os.path.join(out_dir, 'ckpt.pt')
+    checkpoint = torch.load(ckpt_path, map_location=device)
+    gptconf = GPTConfig(**checkpoint['model_args'])
+    model = GPT(gptconf)
+    state_dict = checkpoint['model']
+    unwanted_prefix = '_orig_mod.'
+    for k,v in list(state_dict.items()):
+        if k.startswith(unwanted_prefix):
+            state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
+    model.load_state_dict(state_dict)
+elif init_from.startswith('gpt2'):
+    # init from a given GPT-2 model
+    model = GPT.from_pretrained(init_from, dict(dropout=0.0))
+
 model.eval()
 model.to(device)
 if compile:
@@ -48,7 +55,7 @@ if compile:
 
 # look for the meta pickle in case it is available in the dataset folder
 load_meta = False
-if 'config' in checkpoint and 'dataset' in checkpoint['config']: # older checkpoints might not have these...
+if init_from == 'resume' and 'config' in checkpoint and 'dataset' in checkpoint['config']: # older checkpoints might not have these...
     meta_path = os.path.join('data', checkpoint['config']['dataset'], 'meta.pkl')
     load_meta = os.path.exists(meta_path)
 if load_meta: