diff --git a/docs/extras/integrations/llms/llamacpp.ipynb b/docs/extras/integrations/llms/llamacpp.ipynb
index c7c3a46446..2ec6de39c9 100644
--- a/docs/extras/integrations/llms/llamacpp.ipynb
+++ b/docs/extras/integrations/llms/llamacpp.ipynb
@@ -4,12 +4,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Llama-cpp\n",
+    "# Llama.cpp\n",
     "\n",
-    "[llama-cpp](https://github.com/abetlen/llama-cpp-python) is a Python binding for [llama.cpp](https://github.com/ggerganov/llama.cpp). \n",
+    "[llama-cpp-python](https://github.com/abetlen/llama-cpp-python) is a Python binding for [llama.cpp](https://github.com/ggerganov/llama.cpp). \n",
     "It supports [several LLMs](https://github.com/ggerganov/llama.cpp).\n",
     "\n",
-    "This notebook goes over how to run `llama-cpp` within LangChain."
+    "This notebook goes over how to run `llama-cpp-python` within LangChain."
    ]
   },
   {
@@ -18,7 +18,7 @@
    "source": [
     "## Installation\n",
     "\n",
-    "There is a bunch of options how to install the llama-cpp package: \n",
+    "There are different options on how to install the llama-cpp package: \n",
     "- only CPU usage\n",
     "- CPU + GPU (using one of many BLAS backends)\n",
     "- Metal GPU (MacOS with Apple Silicon Chip) \n",
@@ -61,7 +61,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**IMPORTANT**: If you have already installed a cpu only version of the package, you need to reinstall it from scratch: consider the following command: "
+    "**IMPORTANT**: If you have already installed the CPU only version of the package, you need to reinstall it from scratch. Consider the following command: "
    ]
   },
   {
@@ -79,7 +79,7 @@
    "source": [
     "### Installation with Metal\n",
     "\n",
-    "`lama.cpp` supports Apple silicon first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks. Use the `FORCE_CMAKE=1` environment variable to force the use of cmake and install the pip package for the Metal support ([source](https://github.com/abetlen/llama-cpp-python/blob/main/docs/install/macos.md)).\n",
+    "`llama.cpp` supports Apple silicon first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks. Use the `FORCE_CMAKE=1` environment variable to force the use of cmake and install the pip package for the Metal support ([source](https://github.com/abetlen/llama-cpp-python/blob/main/docs/install/macos.md)).\n",
     "\n",
     "Example installation with Metal Support:"
    ]
@@ -143,7 +143,7 @@
     "\n",
     "#### Compiling and installing\n",
     "\n",
-    "In the same command prompt (anaconda prompt) you set the variables, you can cd into `llama-cpp-python` directory and run the following commands.\n",
+    "In the same command prompt (anaconda prompt) you set the variables, you can `cd` into `llama-cpp-python` directory and run the following commands.\n",
     "\n",
     "```\n",
     "python setup.py clean\n",
@@ -164,7 +164,9 @@
    "source": [
     "Make sure you are following all instructions to [install all necessary model files](https://github.com/ggerganov/llama.cpp).\n",
     "\n",
-    "You don't need an `API_TOKEN`!"
+    "You don't need an `API_TOKEN` as you will run the LLM locally.\n",
+    "\n",
+    "It is worth understanding which models are suitable to be used on the desired machine."
    ]
   },
   {
@@ -227,7 +229,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`Llama-v2`"
+    "Example using a LLaMA 2 7B model"
    ]
   },
   {
@@ -304,7 +306,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`Llama-v1`"
+    "Example using a LLaMA v1 model"
    ]
   },
   {
@@ -381,7 +383,7 @@
    "source": [
     "### GPU\n",
     "\n",
-    "If the installation with BLAS backend was correct, you will see an `BLAS = 1` indicator in model properties.\n",
+    "If the installation with BLAS backend was correct, you will see a `BLAS = 1` indicator in model properties.\n",
     "\n",
     "Two of the most important parameters for use with GPU are:\n",
     "\n",
@@ -473,22 +475,15 @@
     "llm_chain.run(question)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Metal\n",
     "\n",
-    "If the installation with Metal was correct, you will see an `NEON = 1` indicator in model properties.\n",
+    "If the installation with Metal was correct, you will see a `NEON = 1` indicator in model properties.\n",
     "\n",
-    "Two of the most important parameters for use with GPU are:\n",
+    "Two of the most important GPU parameters are:\n",
     "\n",
     "- `n_gpu_layers` - determines how many layers of the model are offloaded to your Metal GPU, in the most case, set it to `1` is enough for Metal\n",
     "- `n_batch` - how many tokens are processed in parallel, default is 8, set to bigger number.\n",
@@ -522,7 +517,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The rest are almost same as GPU, the console log will show the following log to indicate the Metal was enable properly.\n",
+    "The console log will show the following log to indicate Metal was enable properly.\n",
     "\n",
     "```\n",
     "ggml_metal_init: allocating\n",
@@ -530,7 +525,9 @@
     "...\n",
     "```\n",
     "\n",
-    "You also could check the `Activity Monitor` by watching the % GPU of the process, the % CPU will drop dramatically after turn on `n_gpu_layers=1`. Also for the first time call LLM, the performance might be slow due to the model compilation in Metal GPU."
+    "You also could check `Activity Monitor` by watching the GPU usage of the process, the CPU usage will drop dramatically after turn on `n_gpu_layers=1`. \n",
+    "\n",
+    "For the first call to the LLM, the performance may be slow due to the model compilation in Metal GPU."
    ]
   }
  ],