diff --git a/demo_apps/HelloLlamaLocal.ipynb b/demo_apps/HelloLlamaLocal.ipynb
index bc15527d980edeb6e1d5c78d409ab1e946eea4cb..facaeb9b4903c8e0738096c80dd62e9840c8adf8 100644
--- a/demo_apps/HelloLlamaLocal.ipynb
+++ b/demo_apps/HelloLlamaLocal.ipynb
@@ -152,7 +152,7 @@
    "id": "73df46d9",
    "metadata": {},
    "source": [
-    "Next, initialize the langchain CallBackManager. This handles callbacks from Langchain and for this example we will use token-wise streaming so the answer gets generated token by token when Llama is answering your question."
+    "Next, initialize the langchain `CallBackManager`. This handles callbacks from Langchain and for this example we will use token-wise streaming so the answer gets generated token by token when Llama is answering your question."
    ]
   },
   {
@@ -173,7 +173,10 @@
    "source": [
     "\n",
     "Set up the Llama 2 model. \n",
-    "Replace `<path-to-llama-gguf-file>` with the path either to your downloaded quantized model file [here](https://drive.google.com/file/d/1afPv3HOy73BE2MoYCgYJvBDeQNa9rZbj/view?usp=sharing), or to the ggml-model-q4_0.gguf file built with the following commands:\n",
+    "\n",
+    "Replace `<path-to-llama-gguf-file>` with the path either to your downloaded quantized model file [here](https://drive.google.com/file/d/1afPv3HOy73BE2MoYCgYJvBDeQNa9rZbj/view?usp=sharing), \n",
+    "\n",
+    "or to the `ggml-model-q4_0.gguf` file built with the following commands:\n",
     "\n",
     "```bash\n",
     "git clone https://github.com/ggerganov/llama.cpp\n",
@@ -181,6 +184,7 @@
     "python3 -m pip install -r requirements.txt\n",
     "python convert.py <path_to_your_downloaded_llama-2-13b_model>\n",
     "./quantize <path_to_your_downloaded_llama-2-13b_model>/ggml-model-f16.gguf <path_to_your_downloaded_llama-2-13b_model>/ggml-model-q4_0.gguf q4_0\n",
+    "\n",
     "```\n",
     "For more info see https://python.langchain.com/docs/integrations/llms/llamacpp"
    ]
@@ -209,6 +213,7 @@
    "metadata": {},
    "source": [
     "With the model set up, you are now ready to ask some questions. \n",
+    "\n",
     "Here is an example of the simplest way to ask the model some general questions."
    ]
   },
@@ -251,7 +256,8 @@
    "id": "545cb6aa",
    "metadata": {},
    "source": [
-    "Alternatively, you can sue LangChain's PromptTemplate for some flexibility in your prompts and questions.\n",
+    "Alternatively, you can sue LangChain's `PromptTemplate` for some flexibility in your prompts and questions.\n",
+    "\n",
     "For more information on LangChain's prompt template visit this [link](https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/)"
    ]
   },
@@ -367,7 +373,8 @@
    "id": "37f77909",
    "metadata": {},
    "source": [
-    "One way we can fix the hallucinations is to use RAG, to augment it with more recent or custom data that holds the info for it to answer correctly.\n",
+    "One way we can fix the hallucinations is to use RAG, to augment it with more recent or custom data that holds the information for it to answer correctly.\n",
+    "\n",
     "First we load the Llama2 paper using LangChain's [PDF loader](https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf)"
    ]
   },
@@ -417,7 +424,7 @@
     "For this example we will use [Chroma](https://python.langchain.com/docs/integrations/vectorstores/chroma) which is light-weight and in memory so it's easy to get started with.\n",
     "For other vector stores especially if you need to store a large amount of data - see https://python.langchain.com/docs/integrations/vectorstores\n",
     "\n",
-    "We will also import the HuggingFaceEmbeddings and RecursiveCharacterTextSplitter to assist in storing the documents."
+    "We will also import the `HuggingFaceEmbeddings` and `RecursiveCharacterTextSplitter` to assist in storing the documents."
    ]
   },
   {
@@ -443,7 +450,7 @@
    "metadata": {},
    "source": [
     "\n",
-    "To store the documents, we will need to split them into chunks using [`RecursiveCharacterTextSplitter`](https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter) and create vector representations of these chunks using [`HuggingFaceEmbeddings`](https://www.google.com/search?q=langchain+hugging+face+embeddings&sca_esv=572890011&ei=ARUoZaH4LuumptQP48ah2Ac&oq=langchian+hugg&gs_lp=Egxnd3Mtd2l6LXNlcnAiDmxhbmdjaGlhbiBodWdnKgIIADIHEAAYgAQYCjIHEAAYgAQYCjIHEAAYgAQYCjIHEAAYgAQYCjIHEAAYgAQYCjIHEAAYgAQYCjIHEAAYgAQYCjIHEAAYgAQYCjIHEAAYgAQYCjIHEAAYgAQYCkjeHlC5Cli5D3ABeAGQAQCYAV6gAb4CqgEBNLgBAcgBAPgBAcICChAAGEcY1gQYsAPiAwQYACBBiAYBkAYI&sclient=gws-wiz-serp) to them before storing them into our vector database. \n"
+    "To store the documents, we will need to split them into chunks using [`RecursiveCharacterTextSplitter`](https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter) and create vector representations of these chunks using [`HuggingFaceEmbeddings`](https://www.google.com/search?q=langchain+hugging+face+embeddings&sca_esv=572890011&ei=ARUoZaH4LuumptQP48ah2Ac&oq=langchian+hugg&gs_lp=Egxnd3Mtd2l6LXNlcnAiDmxhbmdjaGlhbiBodWdnKgIIADIHEAAYgAQYCjIHEAAYgAQYCjIHEAAYgAQYCjIHEAAYgAQYCjIHEAAYgAQYCjIHEAAYgAQYCjIHEAAYgAQYCjIHEAAYgAQYCjIHEAAYgAQYCjIHEAAYgAQYCkjeHlC5Cli5D3ABeAGQAQCYAV6gAb4CqgEBNLgBAcgBAPgBAcICChAAGEcY1gQYsAPiAwQYACBBiAYBkAYI&sclient=gws-wiz-serp) on them before storing them into our vector database. \n"
    ]
   },
   {
@@ -524,6 +531,7 @@
    "metadata": {},
    "source": [
     "For each question, LangChain performs a semantic similarity search of it in the vector db, then passes the search results as the context to the model to answer the question.\n",
+    "\n",
     "It takes close to 2 minutes to return the result (but using other vector stores other than Chroma such as FAISS can take longer) because Llama2 is running on a local Mac. \n",
     "To get much faster results, you can use a cloud service with GPU used for inference - see HelloLlamaCloud for a demo."
    ]