diff --git a/README.md b/README.md
index 0f494bd55..afd7c9cde 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@
 
 Try it out via this [demo](https://bitnet-demo.azurewebsites.net/), or build and run it on your own [CPU](https://github.com/microsoft/BitNet?tab=readme-ov-file#build-from-source) or [GPU](https://github.com/microsoft/BitNet/blob/main/gpu/README.md).
 
-bitnet.cpp is the official inference framework for 1-bit LLMs (e.g., BitNet b1.58). It offers a suite of optimized kernels, that support **fast** and **lossless** inference of 1.58-bit models on CPU and GPU (NPU support will coming next).
+bitnet.cpp is the official inference framework for 1-bit LLMs (e.g., BitNet b1.58). It offers a suite of optimized kernels, that support **fast** and **lossless** inference of 1.58-bit models on CPU and GPU (NPU support will be coming next).
 
 The first release of bitnet.cpp is to support inference on CPUs. bitnet.cpp achieves speedups of **1.37x** to **5.07x** on ARM CPUs, with larger models experiencing greater performance gains. Additionally, it reduces energy consumption by **55.4%** to **70.0%**, further boosting overall efficiency. On x86 CPUs, speedups range from **2.37x** to **6.17x** with energy reductions between **71.9%** to **82.2%**. Furthermore, bitnet.cpp can run a 100B BitNet b1.58 model on a single CPU, achieving speeds comparable to human reading (5-7 tokens per second), significantly enhancing the potential for running LLMs on local devices. Please refer to the [technical report](https://arxiv.org/abs/2410.16144) for more details.
 
@@ -162,7 +162,7 @@ This project is based on the [llama.cpp](https://github.com/ggerganov/llama.cpp)
 - python>=3.9
 - cmake>=3.22
 - clang>=18
-    - For Windows users, install [Visual Studio 2022](https://visualstudio.microsoft.com/downloads/). In the installer, toggle on at least the following options(this also automatically installs the required additional tools like CMake):
+    - For Windows users, install [Visual Studio 2022](https://visualstudio.microsoft.com/downloads/). In the installer, toggle on at least the following options (this also automatically installs the required additional tools like CMake):
         -  Desktop-development with C++
         -  C++-CMake Tools for Windows
         -  Git for Windows
@@ -285,12 +285,12 @@ python utils/e2e_benchmark.py -m /path/to/model -n 200 -p 256 -t 4
    
 This command would run the inference benchmark using the model located at `/path/to/model`, generating 200 tokens from a 256 token prompt, utilizing 4 threads.  
 
-For the model layout that do not supported by any public model, we provide scripts to generate a dummy model with the given model layout, and run the benchmark on your machine:
+For model layouts that are not supported by any public model, we provide scripts to generate a dummy model with the given model layout, and run the benchmark on your machine:
 
 ```bash
 python utils/generate-dummy-bitnet-model.py models/bitnet_b1_58-large --outfile models/dummy-bitnet-125m.tl1.gguf --outtype tl1 --model-size 125M
 
-# Run benchmark with the generated model, use -m to specify the model path, -p to specify the prompt processed, -n to specify the number of token to generate
+# Run benchmark with the generated model, use -m to specify the model path, -p to specify the prompt processed, -n to specify the number of tokens to generate
 python utils/e2e_benchmark.py -m models/dummy-bitnet-125m.tl1.gguf -p 512 -n 128
 ```