diff --git a/sample.py b/sample.py
index 401153f..1ace1f5 100644
--- a/sample.py
+++ b/sample.py
@@ -3,7 +3,7 @@
 import pandas as pd
 import torch
 import torch.nn.functional as F
-from rdkit import RDLogger
+from rdkit import RDLogger, Chem
 
 import model
 from utils import *
@@ -102,7 +102,8 @@ def get_sample(net: model.CharRNN, size: int, prime: str = 'B', top_k: Optional[
     return ''.join(chars)
 
 
-def get_sample_frame(net: model.CharRNN, size: int, prime: str = 'B', top_k: Optional[int] = None) -> pd.DataFrame:
+def get_sample_frame(net: model.CharRNN, size: int, prime: str = 'B', top_k: Optional[int] = None,
+                     verbose: bool = True) -> pd.DataFrame:
     """
     Wrapper for sampling the net, splitting the output into SMILES string, converting to
     RDKit mols, checking validty, and computing descriptors
@@ -112,6 +113,7 @@ def get_sample_frame(net: model.CharRNN, size: int, prime: str = 'B', top_k: Opt
         size (int): Sample this many characters
         prime (str): Prime net with string
         top_k (Optional[int]): Pick from top K characters
+        verbose (bool)
 
     Returns:
         sample (pd.DataFrame)
@@ -120,16 +122,39 @@ def get_sample_frame(net: model.CharRNN, size: int, prime: str = 'B', top_k: Opt
     net.eval()
     sample = get_sample(net, size=size, prime=prime, top_k=top_k).split('\n')
     sample = pd.DataFrame(sample, columns=['SMILES'])
-    sample['set'] = 'prior'
     sample['ROMol'] = sample.SMILES.map(Chem.MolFromSmiles)
     sample = sample[sample.ROMol.notna()]
 
     num_valid = sample.ROMol.notna().sum()
     num_invalid = sample.shape[0] - num_valid
-    print(f'Valid molecules {num_valid}/{num_valid + num_invalid}')
+    if verbose:
+        print(f'Valid molecules {num_valid}/{num_valid + num_invalid}')
 
     # Compute descriptors of samples
     for desc in descriptors:
         sample[desc] = sample.ROMol.map(descriptors[desc])
 
     return sample
+
+
+if __name__ == '__main__':
+    import argparse
+
+    parser = argparse.ArgumentParser(description='Sample trained SmilesLSTM model')
+    parser.add_argument('-m', '--model', help='Trained model checkpoint (.pt file)', required=True)
+    parser.add_argument('--hidden', help='Hidden units (default: %(default)d)', required=False, default=56, type=int)
+    parser.add_argument('--layers', help='Layers (default: %(default)d)', required=False, default=2, type=int)
+    parser.add_argument('-s', '--size', help='Sample this many characters (default: %(default)d)', required=False,
+                        default=100000,
+                        type=int)
+    parser.add_argument('-o', '--output', help='Output CSV file', required=True, type=str)
+    args = parser.parse_args()
+
+    train_on_gpu = torch.cuda.is_available()
+    device = 'cuda' if train_on_gpu else 'cpu'
+
+    net = model.CharRNN(chars, n_hidden=args.hidden, n_layers=args.layers)
+    net.load_state_dict(torch.load(args.model, map_location=torch.device(device)))
+
+    net_sample = get_sample_frame(net, size=args.size, verbose=False)
+    net_sample.drop(columns=['ROMol']).to_csv(args.output, index=False)