update readme

README.md

@@ -2,7 +2,7 @@
 
 [![Downloads](https://static.pepy.tech/badge/liger-kernel)](https://pepy.tech/project/liger-kernel) [![PyPI version](https://badge.fury.io/py/liger-kernel.svg)](https://badge.fury.io/py/liger-kernel) [![PyPI version](https://badge.fury.io/py/liger-kernel-nightly.svg)](https://badge.fury.io/py/liger-kernel-nightly)
 
-**Liger (Linkedin GPU Efficient Runtime)Kernel** is a collection of Triton-native kernels designed specifically for LLM training. It aims to be **ease of use**, **exact**, **time- and memory-efficient**, and **lightweight**. We welcome contributions from the community to gather the best kernels for LLM training.
+**Liger (Linkedin GPU Efficient Runtime) Kernel** is a collection of Triton-native kernels designed specifically for LLM training. It aims to be **ease of use**, **exact**, **time- and memory-efficient**, and **lightweight**. We welcome contributions from the community to gather the best kernels for LLM training.
 
 ## 🌟 Overview
 
@@ -19,7 +19,7 @@ Gain +20% throughput and reduce memory usage by 60%. Achieve longer context leng
 > 2. HuggingFace models start to OOM at a 4K context length, whereas Liger Kernel scales up to 16K.  
 > 3. **Fused Linear Cross Entropy Loss** is enabled to significantly reduce memory usage.
 
-### ✨ Patch HF model with one line or take individual kernels
+### ✨ Patch HF model with one line or use individual kernels
 
 | 🛠️ Patch Existing HF Model               | 🧩 Compose Your Own Model       |
 |--------------------------|-------------------------|