diff --git a/README.md b/README.md
index 8ddef7c..243da36 100644
--- a/README.md
+++ b/README.md
@@ -37,7 +37,7 @@ This creates a `train.bin` and `val.bin` in that data directory. Now it is time
 $ python train.py config/train_shakespeare_char.py
 ```
 
-If you peak inside it, you'll see that we're training a GPT with a context size of up to 256 characters, 384 feature channels, and it is a 6-layer Transformer with 6 heads in each layer. On one A100 GPU this training run takes about 3 minutes and the best validation loss is 1.4697. Based on the configuration, the model checkpoints are being written into the `--out_dir` directory `out-shakespeare-char`. So once the training finishes we can sample from the best model by pointing the sampling script at this directory:
+If you peek inside it, you'll see that we're training a GPT with a context size of up to 256 characters, 384 feature channels, and it is a 6-layer Transformer with 6 heads in each layer. On one A100 GPU this training run takes about 3 minutes and the best validation loss is 1.4697. Based on the configuration, the model checkpoints are being written into the `--out_dir` directory `out-shakespeare-char`. So once the training finishes we can sample from the best model by pointing the sampling script at this directory:
 
 ```
 $ python sample.py --out_dir=out-shakespeare-char