diff --git a/README.md b/README.md
index 916a37d..73c3bdc 100644
--- a/README.md
+++ b/README.md
@@ -90,7 +90,7 @@ num_layers=20,
 model_dims=1280,
 ```
 
-1. The context_len here can be set as the max context length **of the model**. You can provide shorter series to the `tfm.forecast()` function and the model will handle it. Currently the model handles a max context length of 512, which can be increased in later releases. The input time series can have **any context length**. Padding / truncation will be handled by the inference code if needed.
+1. The context_len here can be set as the max context length **of the model**. You can provide a shorter series to the `tfm.forecast()` function and the model will handle it. Currently, the model handles a max context length of 512, which can be increased in later releases. The input time series can have **any context length**. Padding / truncation will be handled by the inference code if needed.
 
 2. The horizon length can be set to anything. We recommend setting it to the largest horizon length you would need in the forecasting tasks for your application. We generally recommend horizon length <= context length but it is not a requirement in the function call.
 
@@ -100,9 +100,9 @@ We provide APIs to forecast from either array inputs or `pandas` dataframe. Both
 
 In particular regarding the frequency, TimesFM expects a categorical indicator valued in {0, 1, 2}:
 
-- **0** (default): high frequency, long horizon time series. We recommend to use this for time series up to daily granularity.
-- **1**: medium frequency time series. We recommend to use this for weekly and monthly data.
-- **2**: low frequency, short horizon time series. We recommend to use this for anything beyond monthly, e.g. quarterly or yearly.
+- **0** (default): high frequency, long horizon time series. We recommend using this for time series up to daily granularity.
+- **1**: medium frequency time series. We recommend using this for weekly and monthly data.
+- **2**: low frequency, short horizon time series. We recommend using this for anything beyond monthly, e.g. quarterly or yearly.
 
 This categorical value should be directly provided with the array inputs. For dataframe inputs, we convert the conventional letter coding of frequencies to our expected categories, that
 
@@ -156,10 +156,4 @@ forecast_df = tfm.forecast_on_df(
     freq="M",  # monthly
     value_name="y",
     num_jobs=-1,
-)
-```
-
-
-
-
-
+)```
diff --git a/src/timesfm.py b/src/timesfm.py
index b795450..e10ebc3 100644
--- a/src/timesfm.py
+++ b/src/timesfm.py
@@ -231,7 +231,7 @@ class TimesFm:
     Args:
       checkpoint_path: path to the checkpoint directory.
       checkpoint_type: type of PAX checkpoint
-      step: step of the checkpoint to load. If `None`, load lastest checkpoint.
+      step: step of the checkpoint to load. If `None`, load latest checkpoint.
     """
     #  Initialize the model weights.
     self._logging("Constructing model weights.")