diff --git a/README.md b/README.md
index e2b01da..9b689ba 100644
--- a/README.md
+++ b/README.md
@@ -18,7 +18,7 @@ This is the TorchDrug implementation of [ULTRA], a foundation model for KG reaso
 ## Installation ##
 
 You may install the dependencies via either conda or pip. 
-Ultra (TorchDrug) is compatible with Python 3.7/3.8/3.9, PyTorch 1.13 and PyG 2.3 (CUDA 11.7 or later wnen running on GPUs). If you are on a Mac, you may omit the CUDA toolkit requirements (tested with PyTorch 2.0 with the relevant `torch-scatter` version on Mac M2).
+Ultra (TorchDrug) is compatible with Python 3.7/3.8/3.9, PyTorch 1.13 and PyG 2.3 (CUDA 11.7 or later when running on GPUs). If you are on a Mac, you may omit the CUDA toolkit requirements (tested with PyTorch 2.0 with the relevant `torch-scatter` version on Mac M2).
 
 ### From Conda ###
 
@@ -143,7 +143,7 @@ To run ULTRA with multiple GPUs, use the following commands (eg, 4 GPUs per node
 python -m torch.distributed.launch --nproc_per_node=4 script/run_full.py -c config/transductive/pretrain.yaml --gpus [0,1,2,3]
 ```
 
-Multi-node setup might work as well(not tested):
+Multi-node setup might work as well (not tested):
 ```bash
 python -m torch.distributed.launch --nnodes=4 --nproc_per_node=4 script/pretrain.py -c config/transductive/pretrain.yaml --gpus [0,1,2,3,0,1,2,3,0,1,2,3,0,1,2,3]
 ```
diff --git a/ultra/util.py b/ultra/util.py
index 1cb50be..6a30762 100644
--- a/ultra/util.py
+++ b/ultra/util.py
@@ -266,7 +266,7 @@ def safe_load(solver, checkpoint, fix_reasoner=False, drop_optimizer=True):
             if not fix_reasoner:
                 solver.optimizer.load_state_dict(state["optimizer"])
         except:
-            print("\n\n\n\nwarninig: loaded state dict has a different number of parameter groups\n\n\n\n")
+            print("\n\n\n\nwarning: loaded state dict has a different number of parameter groups\n\n\n\n")
 
         for state in solver.optimizer.state.values():
             for k, v in state.items():
@@ -322,4 +322,4 @@ def clean_save(solver, checkpoint):
         }
         torch.save(state, checkpoint)
 
-    comm.synchronize()
\ No newline at end of file
+    comm.synchronize()