diff --git a/README.md b/README.md
index dffb656..ccdcd55 100644
--- a/README.md
+++ b/README.md
@@ -64,7 +64,7 @@ Hardware: Single A100 80GB GPU with CPU RAM above 64GB
 | bigscience/bloomz-7b1 (7B params) | OOM GPU | 32GB GPU / 3.8GB CPU | 18.1GB GPU / 35GB CPU |
 
 Performance of PEFT-LoRA tuned [`bigscience/T0_3B`](https://huggingface.co/bigscience/T0_3B) on [`ought/raft/twitter_complaints`](https://huggingface.co/datasets/ought/raft/viewer/twitter_complaints) leaderboard. 
-A point to note is that we didn't try to sequeeze performance by playing around with input instruction templates, LoRA hyperparams and other training related hyperparams. Also, we didn't use the larger 13B [mt0-xxl](https://huggingface.co/bigscience/mt0-xxl) model.
+A point to note is that we didn't try to squeeze performance by playing around with input instruction templates, LoRA hyperparams and other training related hyperparams. Also, we didn't use the larger 13B [mt0-xxl](https://huggingface.co/bigscience/mt0-xxl) model.
 So, we are already seeing comparable performance to SoTA with parameter efficient tuning. Also, the final checkpoint size is just `19MB` in comparison to `11GB` size of the backbone [`bigscience/T0_3B`](https://huggingface.co/bigscience/T0_3B) model.
 
 |   Submission Name        | Accuracy |
@@ -81,7 +81,7 @@ GPU memory required by different settings during training is given below. The fi
 
 Hardware: Single A100 80GB GPU with CPU RAM above 64GB
 
-|   Model         | Full Finetuning | PEFT-LoRA  | PEFT-LoRA with Gradient Checkpoitning  |
+|   Model         | Full Finetuning | PEFT-LoRA  | PEFT-LoRA with Gradient Checkpointing  |
 | --------- | ---- | ---- | ---- |
 | CompVis/stable-diffusion-v1-4 | 27.5GB GPU / 3.97GB CPU | 15.5GB GPU / 3.84GB CPU | 8.12GB GPU / 3.77GB CPU | 
 
@@ -148,7 +148,7 @@ Another example is fine-tuning [`roberta-large`](https://huggingface.co/roberta-
 
 ## PEFT + 🤗 Accelerate
 
-PEFT models work with 🤗 Accelerate out of the box. Use 🤗 Accelerate for Distributed training on various hardware such as GPUs, Apple Silicon devices etc during training.
+PEFT models work with 🤗 Accelerate out of the box. Use 🤗 Accelerate for Distributed training on various hardware such as GPUs, Apple Silicon devices, etc during training.
 Use 🤗 Accelerate for inferencing on consumer hardware with small resources.
 
 ### Example of PEFT model training using 🤗 Accelerate's DeepSpeed integration