diff --git a/README.md b/README.md
index 6b4725940..64d0c3e8a 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # Llama 2 Fine-tuning / Inference Recipes, Examples and Demo Apps
 
-**[Update Nov. 14, 2023] We recently released a series of Llama 2 demo apps [here](./demo_apps). These apps show how to run Llama 2 locally, in the cloud, on-prem or with WhatsApp, and how to ask Llama 2 questions in general and about custom data (PDF, DB, or live).**
+**[Update Nov. 16, 2023] We recently released a series of Llama 2 demo apps [here](./demo_apps). These apps show how to run Llama (locally, in the cloud, or on-prem), how to ask Llama questions in general or about custom data (PDF, DB, or live), how to integrate Llama with WhatsApp, and how to implement an end-to-end chatbot with RAG (Retrieval Augmented Generation).**
 
 The 'llama-recipes' repository is a companion to the [Llama 2 model](https://github.com/facebookresearch/llama). The goal of this repository is to provide examples to quickly get started with fine-tuning for domain adaptation and how to run inference for the fine-tuned models. For ease of use, the examples use Hugging Face converted versions of the models. See steps for conversion of the model [here](#model-conversion-to-hugging-face).
 
@@ -184,6 +184,7 @@ This folder contains a series of Llama2-powered apps:
 2. Llama on Google Colab
 3. Llama on Cloud and ask Llama questions about unstructured data in a PDF
 4. Llama on-prem with vLLM and TGI
+5. Llama chatbot with RAG (Retrieval Augmented Generation)
 
 * Specialized Llama use cases:
 1. Ask Llama to summarize a video content
diff --git a/demo_apps/RAG_Chatbot_example/RAG_Chatbot_Example.ipynb b/demo_apps/RAG_Chatbot_example/RAG_Chatbot_Example.ipynb
new file mode 100644
index 000000000..d970afc4e
--- /dev/null
+++ b/demo_apps/RAG_Chatbot_example/RAG_Chatbot_Example.ipynb
@@ -0,0 +1,717 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Building a Llama 2 chatbot with Retrieval Augmented Generation (RAG)\n",
+    "\n",
+    "This notebook shows a complete example of how to build a Llama 2 chatbot hosted on your browser that can answer questions based on your own data. We'll cover:\n",
+    "* The deployment process of Llama 2 7B with the [Text-generation-inference](https://github.com/huggingface/text-generation-inference) framework as an API server\n",
+    "* A chatbot example built with [Gradio](https://github.com/gradio-app/gradio) and wired to the server\n",
+    "* Adding RAG capability with Llama 2 specific knowledge based on our Getting Started [guide](https://ai.meta.com/llama/get-started/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## RAG Architecture\n",
+    "\n",
+    "LLMs have unprecedented capabilities in NLU (Natural Language Understanding) & NLG (Natural Language Generation), but they have a knowledge cutoff date, and are only trained on publicly available data before that date.\n",
+    "\n",
+    "RAG, invented by [Meta](https://ai.meta.com/blog/retrieval-augmented-generation-streamlining-the-creation-of-intelligent-natural-language-processing-models/) in 2020, is one of the most popular methods to augment LLMs. RAG allows enterprises to keep sensitive data on-prem and get more relevant answers from generic models without fine-tuning models for specific roles.\n",
+    "\n",
+    "RAG is a method that:\n",
+    "* Retrieves data from outside a foundation model\n",
+    "* Augments your questions or prompts to LLMs by adding the retrieved relevant data as context\n",
+    "* Allows LLMs to answer questions about your own data, or data not publicly available when LLMs were trained\n",
+    "* Greatly reduces the hallucination in  model's response generation\n",
+    "\n",
+    "The following diagram shows the general RAG components and process:"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How to Develop a RAG Powered Llama 2 Chatbot\n",
+    "\n",
+    "The easiest way to develop RAG-powered Llama 2 chatbots is to use frameworks such as [**LangChain**](https://www.langchain.com/) and [**LlamaIndex**](https://www.llamaindex.ai/), two leading open-source frameworks for building LLM apps. Both offer convenient APIs for implementing RAG with Llama 2 including:\n",
+    "\n",
+    "* Load and split documents\n",
+    "* Embed and store document splits\n",
+    "* Retrieve the relevant context based on the user query\n",
+    "* Call Llama 2 with query and context to generate the answer\n",
+    "\n",
+    "LangChain is a more general purpose and flexible framework for developing LLM apps with RAG capabilities, while LlamaIndex as a data framework focuses on connecting custom data sources to LLMs. The integration of the two may provide the best performant and effective solution to building real world RAG apps.  \n",
+    "In our example, for simplicifty, we will use LangChain alone with locally stored PDF data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Install Dependencies\n",
+    "\n",
+    "For this demo, we will be using the Gradio for chatbot UI, Text-generation-inference framework for model serving.  \n",
+    "For vector storage and similarity search, we will be using [FAISS](https://github.com/facebookresearch/faiss).  \n",
+    "In this example, we will be running everything in a AWS EC2 instance (i.e. [g5.2xlarge]( https://aws.amazon.com/ec2/instance-types/g5/)). g5.2xlarge features one A10G GPU. We recommend running this notebook with at least one GPU equivalent to A10G with at least 16GB video memory.  \n",
+    "There are certain techniques to downsize the Llama 2 7B model, so it can fit into smaller GPUs. But it is out of scope here.\n",
+    "\n",
+    "First, let's install all dependencies with PIP. We also recommend you start a dedicated Conda environment for better package management"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -r requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Processing\n",
+    "\n",
+    "First run all the imports and define the path of the data and vector storage after processing.  \n",
+    "For the data, we will be using a raw pdf crawled from Llama 2 Getting Started guide on [Meta AI website](https://ai.meta.com/llama/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import HuggingFaceEmbeddings\n",
+    "from langchain.vectorstores import FAISS\n",
+    "from langchain.document_loaders import PyPDFDirectoryLoader\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter \n",
+    "\n",
+    "DATA_PATH = 'data' #Your root data folder path\n",
+    "DB_FAISS_PATH = 'vectorstore/db_faiss'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we use the `PyPDFDirectoryLoader` to load the entire directory. You can also use `PyPDFLoader` for loading one single file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = PyPDFDirectoryLoader(DATA_PATH)\n",
+    "documents = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check the length and content of the doc to ensure we have loaded the right document with number of pages as 37."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(len(documents), documents[0].page_content[0:100])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Split the loaded documents into smaller chunks.  \n",
+    "[`RecursiveCharacterTextSplitter`](https://api.python.langchain.com/en/latest/text_splitter/langchain.text_splitter.RecursiveCharacterTextSplitter.html) is one common splitter that splits long pieces of text into smaller, semantically meaningful chunks.  \n",
+    "Other splitters include:\n",
+    "* SpacyTextSplitter\n",
+    "* NLTKTextSplitter\n",
+    "* SentenceTransformersTokenTextSplitter\n",
+    "* CharacterTextSplitter\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=10)\n",
+    "splits = text_splitter.split_documents(documents)\n",
+    "print(len(splits), splits[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that we have set `chunk_size` to 500 and `chunk_overlap` to 10. In the spliting, these two parameters can directly affects the quality of the LLM's answers.  \n",
+    "Here is a good [guide](https://dev.to/peterabel/what-chunk-size-and-chunk-overlap-should-you-use-4338) on how you should carefully set these two parameters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we will need to choose an embedding model for our splited documents.  \n",
+    "**Embeddings are numerial representations of text**. The default embedding model in HuggingFace Embeddings is `sentence-transformers/all-mpnet-base-v2` with 768 dimension. Below we use a smaller model `all-MiniLM-L6-v2` with dimension 384 so indexing runs faster."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2',\n",
+    "                                       model_kwargs={'device': 'cpu'})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lastly, with splits and choice of the embedding model ready, we want to index them and store all the split chunks as embeddings into the vector storage.  \n",
+    "\n",
+    "Vector stores are databases storing embeddings. There're at least 60 [vector stores](https://python.langchain.com/docs/integrations/vectorstores) supported by LangChain, and two of the most popular open source ones are:\n",
+    "* [Chroma](https://www.trychroma.com/): a light-weight and in memory so it's easy to get started with and use for **local development**.\n",
+    "* [FAISS](https://python.langchain.com/docs/integrations/vectorstores/faiss) (Facebook AI Similarity Search): a vector store that supports search in vectors that may not fit in RAM and is appropriate for **production use**.  \n",
+    "\n",
+    "Since we are running on a EC2 instance with abundant CPU resources and RAM, we will use FAISS in this example. Note that FAISS can also run on GPUs, where some of the most useful algorithms are implemented there. In that case, install `faiss-gpu` package with PIP instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db = FAISS.from_documents(splits, embeddings)\n",
+    "db.save_local(DB_FAISS_PATH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you saved database into local path. You can find them as `index.faiss` and `index.pkl`. In the chatbot example, you can then load this database from local and plug it into our retrival process."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Serving\n",
+    "\n",
+    "In this example, we will be deploying a Llama 2 7B chat HuggingFace model with the Text-generation-inference framework on-permises.  \n",
+    "This would allow us to directly wire the API server with our chatbot.  \n",
+    "There are alternative solutions to deploy Llama 2 models on-permises as your local API server.  \n",
+    "You can find our complete guide [here](https://github.com/facebookresearch/llama-recipes/blob/main/demo_apps/llama-on-prem.md)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In a **separate terminal**, run commands below to launch an API server with TGI. This will download model artifacts and store them locally, while launching at the desire port on your localhost. In our case, this is port 8080"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "model = meta-llama/Llama-2-7b-chat-hf  \n",
+    "volume = $PWD/data  \n",
+    "token = #Your own HF tokens  \n",
+    "docker run --gpus all --shm-size 1g -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.1.0 --model-id $model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we have the API server up and running, we can run a simple `curl` command to validate our model is working as expected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!curl localhost:8080/generate -X POST -H 'Content-Type: application/json' -d '{\"inputs\": \"What is good about Beijing?\", \"parameters\": { \"max_new_tokens\":64}}' #Replace the locahost with the IP visible to the machine running the notebook     "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Building the Chatbot UI\n",
+    "\n",
+    "Now we are ready to build the chatbot UI to wire up RAG data and API server. In our example we will be using Gradio to build the Chatbot UI.  \n",
+    "Gradio is an open-source Python library that is used to build machine learning and data science demos and web applications. It had been widely used by the community and HuggingFace also used Gradio to build their Chatbots. Other alternatives are: \n",
+    "* [Streamlit](https://streamlit.io/)\n",
+    "* [Dash](https://plotly.com/dash/)\n",
+    "* [Flask](https://flask.palletsprojects.com/en/3.0.x/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, we start by adding all the imports, paths, constants and set LangChain in debug mode, so it shows clear actions within the chain process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import langchain\n",
+    "from queue import Queue\n",
+    "from typing import Any\n",
+    "from langchain.llms.huggingface_text_gen_inference import HuggingFaceTextGenInference\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "from langchain.schema import LLMResult\n",
+    "from langchain.embeddings import HuggingFaceEmbeddings\n",
+    "from langchain.vectorstores import FAISS\n",
+    "from langchain.chains import RetrievalQA\n",
+    "from langchain.prompts.prompt import PromptTemplate\n",
+    "from anyio.from_thread import start_blocking_portal #For model callback streaming\n",
+    "\n",
+    "langchain.debug=True \n",
+    "\n",
+    "#vector db path\n",
+    "DB_FAISS_PATH = 'vectorstore/db_faiss'\n",
+    "\n",
+    "#Llama2 TGI models host port\n",
+    "LLAMA2_7B_HOSTPORT = \"http://localhost:8080/\" #Replace the locahost with the IP visible to the machine running the notebook\n",
+    "LLAMA2_13B_HOSTPORT = \"http://localhost:8080/\" #Add your own host ports for model switching. You can host another TGI model on same instance on a different port.\n",
+    "\n",
+    "\n",
+    "model_dict = {\n",
+    "    \"7b-chat\" : LLAMA2_7B_HOSTPORT,\n",
+    "    \"13b-chat\" : LLAMA2_13B_HOSTPORT,\n",
+    "}\n",
+    "\n",
+    "system_message = {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we load the FAISS vector store"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\",\n",
+    "                                       model_kwargs={'device': 'cpu'})\n",
+    "db = FAISS.load_local(DB_FAISS_PATH, embeddings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we create a TGI llm instance and wire to the API serving port on localhost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = HuggingFaceTextGenInference(\n",
+    "    inference_server_url=LLAMA2_7B_HOSTPORT,\n",
+    "    max_new_tokens=512,\n",
+    "    top_k=10,\n",
+    "    top_p=0.9,\n",
+    "    typical_p=0.95,\n",
+    "    temperature=0.6,\n",
+    "    repetition_penalty=1,\n",
+    "    do_sample=True,\n",
+    "    streaming=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we define the retriever and template for our RetrivalQA chain. For each call of the RetrievalQA, LangChain performs a semantic similarity search of the query in the vector database, then passes the search results as the context to Llama to answer the query about the data stored in the verctor database.  \n",
+    "Whereas for the template, this defines the format of the question along with context that we will be sent into Llama for generation. In general, Llama 2 has special prompt format to handle special tokens. In some cases, the serving framework might already have taken care of it. Otherwise, you will need to write customized template to properly handle that.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template = \"\"\"\n",
+    "[INST]Use the following pieces of context to answer the question. If no context provided, answer like a AI assistant.\n",
+    "{context}\n",
+    "Question: {question} [/INST]\n",
+    "\"\"\"\n",
+    "\n",
+    "retriever = db.as_retriever(\n",
+    "        search_kwargs={\"k\": 6}\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lastly, we can define the retrieval chain for QA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qa_chain = RetrievalQA.from_chain_type(\n",
+    "    llm=llm, \n",
+    "    retriever=retriever,     \n",
+    "    chain_type_kwargs={\n",
+    "        \"prompt\": PromptTemplate(\n",
+    "            template=template,\n",
+    "            input_variables=[\"context\", \"question\"],\n",
+    "        ),\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we should have a working chain for QA. Let's test it out before wire it up with UI blocks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = qa_chain({\"query\": \"Why choose Llama?\"})\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After confirming the validity, we can start building the UI. Before we define the gradio [blocks](https://www.gradio.app/docs/blocks), let's first define the callback streams that we will use later for the streaming feature.  \n",
+    "This callback handler will put streaming LLM responses to a queue for gradio UI to render on the fly. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "job_done = object()\n",
+    "\n",
+    "class MyStream(StreamingStdOutCallbackHandler):\n",
+    "    def __init__(self, q) -> None:\n",
+    "        self.q = q\n",
+    "\n",
+    "    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:\n",
+    "        self.q.put(token)\n",
+    "\n",
+    "    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n",
+    "        self.q.put(job_done)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can define the gradio UI blocks.  \n",
+    "Since we will need to define the UI and handlers in the same place, this will be a large chunk of code. We will add comments in the code for explanation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gradio as gr\n",
+    "\n",
+    "with gr.Blocks() as demo:\n",
+    "    #Configure UI layout\n",
+    "    chatbot = gr.Chatbot(height = 600)\n",
+    "    with gr.Row():\n",
+    "        with gr.Column(scale=1):\n",
+    "            with gr.Row():\n",
+    "                #model selection\n",
+    "                model_selector = gr.Dropdown(\n",
+    "                    list(model_dict.keys()), \n",
+    "                    value=\"7b-chat\", \n",
+    "                    label=\"Model\", \n",
+    "                    info=\"Select the model\", \n",
+    "                    interactive = True, \n",
+    "                    scale=1\n",
+    "                )\n",
+    "                max_new_tokens_selector = gr.Number(\n",
+    "                    value=512, \n",
+    "                    precision=0, \n",
+    "                    label=\"Max new tokens\", \n",
+    "                    info=\"Adjust max_new_tokens\",\n",
+    "                    interactive = True, \n",
+    "                    minimum=1, \n",
+    "                    maximum=1024, \n",
+    "                    scale=1\n",
+    "                )\n",
+    "            with gr.Row():\n",
+    "                #hyperparameter selection\n",
+    "                temperature_selector = gr.Slider(\n",
+    "                    value=0.6, \n",
+    "                    label=\"Temperature\", \n",
+    "                    info=\"Range 0-2. Controls the creativity of the generated text.\",\n",
+    "                    interactive = True, \n",
+    "                    minimum=0.01, \n",
+    "                    maximum=2, \n",
+    "                    step=0.01, \n",
+    "                    scale=1\n",
+    "                )\n",
+    "                top_p_selector = gr.Slider(\n",
+    "                    value=0.9, \n",
+    "                    label=\"Top_p\", \n",
+    "                    info=\"Range 0-1. Nucleus sampling.\",\n",
+    "                    interactive = True, \n",
+    "                    minimum=0.01, \n",
+    "                    maximum=0.99, \n",
+    "                    step=0.01, \n",
+    "                    scale=1\n",
+    "                )\n",
+    "        with gr.Column(scale=2):\n",
+    "            #user input prompt text field\n",
+    "            user_prompt_message = gr.Textbox(placeholder=\"Please add user prompt here\", label=\"User prompt\")\n",
+    "            with gr.Row():\n",
+    "                clear = gr.Button(\"Clear Conversation\", scale=2)\n",
+    "                submitBtn = gr.Button(\"Submit\", scale=8)\n",
+    "\n",
+    "\n",
+    "    state = gr.State([])\n",
+    "\n",
+    "    #handle user message\n",
+    "    def user(user_prompt_message, history):\n",
+    "        if user_prompt_message != \"\":\n",
+    "            return history + [[user_prompt_message, None]]\n",
+    "        else:\n",
+    "            return history + [[\"Invalid prompts - user prompt cannot be empty\", None]]\n",
+    "\n",
+    "    #chatbot logic for configuration, sending the prompts, rendering the streamed back genereations etc\n",
+    "    def bot(model_selector, temperature_selector, top_p_selector, max_new_tokens_selector, user_prompt_message, history, messages_history):\n",
+    "        dialog = []\n",
+    "        bot_message = \"\"\n",
+    "        history[-1][1] = \"\"\n",
+    "           \n",
+    "        dialog = [\n",
+    "            {\"role\": \"user\", \"content\": user_prompt_message},\n",
+    "        ]\n",
+    "        messages_history += dialog\n",
+    "        \n",
+    "        #Queue for streamed character rendering\n",
+    "        q = Queue()\n",
+    "\n",
+    "        #Update new llama hyperparameters\n",
+    "        llm.inference_server_url = model_selector\n",
+    "        llm.temperature = temperature_selector\n",
+    "        llm.top_p = top_p_selector\n",
+    "        llm.max_new_tokens = max_new_tokens_selector\n",
+    "\n",
+    "        #Async task for streamed chain results wired to callbacks we previously defined, so we don't block the UI\n",
+    "        async def task(prompt):\n",
+    "            ret = await qa_chain.run(prompt, callbacks=[MyStream(q)])\n",
+    "            return ret\n",
+    "\n",
+    "        with start_blocking_portal() as portal:\n",
+    "            portal.start_task_soon(task, user_prompt_message)\n",
+    "            while True:\n",
+    "                next_token = q.get(True)\n",
+    "                if next_token is job_done:\n",
+    "                    messages_history += [{\"role\": \"assistant\", \"content\": bot_message}]\n",
+    "                    return history, messages_history\n",
+    "                bot_message += next_token\n",
+    "                history[-1][1] += next_token\n",
+    "                yield history, messages_history\n",
+    "\n",
+    "    #init the chat history with default system message    \n",
+    "    def init_history(messages_history):\n",
+    "        messages_history = []\n",
+    "        messages_history += [system_message]\n",
+    "        return messages_history\n",
+    "\n",
+    "    #clean up the user input text field\n",
+    "    def input_cleanup():\n",
+    "        return \"\"\n",
+    "\n",
+    "    #when the user clicks Enter and the user message is submitted\n",
+    "    user_prompt_message.submit(\n",
+    "        user, \n",
+    "        [user_prompt_message, chatbot], \n",
+    "        [chatbot], \n",
+    "        queue=False\n",
+    "    ).then(\n",
+    "        bot, \n",
+    "        [model_selector, temperature_selector, top_p_selector, max_new_tokens_selector, user_prompt_message, chatbot, state], \n",
+    "        [chatbot, state]\n",
+    "    ).then(input_cleanup, \n",
+    "        [], \n",
+    "        [user_prompt_message], \n",
+    "        queue=False\n",
+    "    )\n",
+    "\n",
+    "    #when the user clicks the submit button\n",
+    "    submitBtn.click(\n",
+    "        user, \n",
+    "        [user_prompt_message, chatbot], \n",
+    "        [chatbot], \n",
+    "        queue=False\n",
+    "    ).then(\n",
+    "        bot, \n",
+    "        [model_selector, temperature_selector, top_p_selector, max_new_tokens_selector, user_prompt_message, chatbot, state], \n",
+    "        [chatbot, state]\n",
+    "    ).then(\n",
+    "        input_cleanup, \n",
+    "        [], \n",
+    "        [user_prompt_message], \n",
+    "        queue=False\n",
+    "    )\n",
+    "    \n",
+    "    #when the user clicks the clear button\n",
+    "    clear.click(lambda: None, None, chatbot, queue=False).success(init_history, [state], [state])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lastly, we can launch this demo on our localhost with the command below. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://0.0.0.0:7860\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://localhost:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "demo.queue().launch(server_name=\"0.0.0.0\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Gradio will default the launch port to 7860. You can select which port it should launch on as needed.  \n",
+    "Once launched, in the notebook or a browser with URL http://0.0.0.0:7860, you should see the UI.  \n",
+    "Things to try in the chatbot demo:  \n",
+    "* Asking specific questions related to the Llama 2 Getting Started Guide\n",
+    "* Adjust parameters such as max new token generated\n",
+    "* Switching to another Llama model with another container launched in a separate terminal\n",
+    "\n",
+    "Once finished testing, make sure you close the demo by running the command below to release the port."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "demo.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "myenv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/demo_apps/RAG_Chatbot_example/data/Llama Getting Started Guide.pdf b/demo_apps/RAG_Chatbot_example/data/Llama Getting Started Guide.pdf
new file mode 100644
index 000000000..886e864ee
Binary files /dev/null and b/demo_apps/RAG_Chatbot_example/data/Llama Getting Started Guide.pdf differ
diff --git a/demo_apps/RAG_Chatbot_example/requirements.txt b/demo_apps/RAG_Chatbot_example/requirements.txt
new file mode 100644
index 000000000..0a4f32622
--- /dev/null
+++ b/demo_apps/RAG_Chatbot_example/requirements.txt
@@ -0,0 +1,6 @@
+gradio
+pypdf
+langchain
+sentence-transformers
+faiss-cpu
+text-generation
\ No newline at end of file
diff --git a/demo_apps/RAG_Chatbot_example/vectorstore/db_faiss/index.faiss b/demo_apps/RAG_Chatbot_example/vectorstore/db_faiss/index.faiss
new file mode 100644
index 000000000..ee002caeb
Binary files /dev/null and b/demo_apps/RAG_Chatbot_example/vectorstore/db_faiss/index.faiss differ
diff --git a/demo_apps/RAG_Chatbot_example/vectorstore/db_faiss/index.pkl b/demo_apps/RAG_Chatbot_example/vectorstore/db_faiss/index.pkl
new file mode 100644
index 000000000..e29ef18cc
Binary files /dev/null and b/demo_apps/RAG_Chatbot_example/vectorstore/db_faiss/index.pkl differ
diff --git a/demo_apps/README.md b/demo_apps/README.md
index 70aaf8ec9..bd94fe34b 100644
--- a/demo_apps/README.md
+++ b/demo_apps/README.md
@@ -6,6 +6,7 @@ This folder contains a series of Llama 2-powered apps:
 2. Llama on Google Colab
 3. Llama on Cloud and ask Llama questions about unstructured data in a PDF
 4. Llama on-prem with vLLM and TGI
+5. Llama chatbot with RAG (Retrieval Augmented Generation)
 
 * Specialized Llama use cases:
 1. Ask Llama to summarize a video content
@@ -103,3 +104,6 @@ To see how to query Llama2 and get answers with the Gradio UI both from the note
 Then enter your question, click Submit. You'll see in the notebook or a browser with URL http://127.0.0.1:7860 the following UI:
 
 ![](llama2-gradio.png)
+
+### [RAG Chatbot Example](RAG_Chatbot_example/RAG_Chatbot_Example.ipynb)
+A complete example of how to build a Llama 2 chatbot hosted on your browser that can answer questions based on your own data.
diff --git a/demo_apps/llama-on-prem.md b/demo_apps/llama-on-prem.md
index bbc0dd4df..fe8097ec6 100644
--- a/demo_apps/llama-on-prem.md
+++ b/demo_apps/llama-on-prem.md
@@ -22,7 +22,9 @@ pip install vllm
 
 Then run `huggingface-cli login` and copy and paste your Hugging Face access token to complete the login.
 
+<!-- markdown-link-check-disable -->
 There are two ways to deploy Llama 2 via vLLM, as a general API server or an OpenAI-compatible server (see [here](https://platform.openai.com/docs/api-reference/authentication) on how the OpenAI API authenticates, but you won't need to provide a real OpenAI API key when running Llama 2 via vLLM in the OpenAI-compatible mode).
+<!-- markdown-link-check-enable -->
 
 ### Deploying Llama 2 as an API Server
 
diff --git a/examples/inference.py b/examples/inference.py
index ab4e7139f..87c43a750 100644
--- a/examples/inference.py
+++ b/examples/inference.py
@@ -11,7 +11,7 @@
 import torch
 from transformers import LlamaTokenizer
 
-from llama_recipes.inference.safety_utils import get_safety_checker
+from llama_recipes.inference.safety_utils import get_safety_checker, AgentType
 from llama_recipes.inference.model_utils import load_model, load_peft_model
 
 
@@ -33,6 +33,8 @@ def main(
     enable_azure_content_safety: bool=False, # Enable safety check with Azure content safety api
     enable_sensitive_topics: bool=False, # Enable check for sensitive topics using AuditNLG APIs
     enable_salesforce_content_safety: bool=True, # Enable safety check with Salesforce safety flan t5
+    enable_llamaguard_content_safety: bool=False,
+    llamaguard_model_name: str=None,
     max_padding_length: int=None, # the max padding length to be used with tokenizer padding the prompts.
     use_fast_kernels: bool = False, # Enable using SDPA from PyTroch Accelerated Transformers, make use Flash Attention and Xformer memory-efficient kernels
     **kwargs
@@ -48,6 +50,12 @@ def main(
     else:
         print("No user prompt provided. Exiting.")
         sys.exit(1)
+
+    if enable_llamaguard_content_safety:
+        if not llamaguard_model_name:
+            print("if enable_llamaguard_content_safety is used, provide the model path with --llamaguard_model_name")
+            sys.exit(1)
+
     
     # Set the seeds for reproducibility
     torch.cuda.manual_seed(seed)
@@ -77,6 +85,8 @@ def main(
     safety_checker = get_safety_checker(enable_azure_content_safety,
                                         enable_sensitive_topics,
                                         enable_salesforce_content_safety,
+                                        enable_llamaguard_content_safety,
+                                        guard_lama_path=llamaguard_model_name
                                         )
 
     # Safety check of the user prompt
@@ -117,7 +127,7 @@ def main(
     output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
     
     # Safety check of the model output
-    safety_results = [check(output_text) for check in safety_checker]
+    safety_results = [check(output_text, agent_type=AgentType.AGENT, user_prompt=user_prompt) for check in safety_checker]
     are_safe = all([r[1] for r in safety_results])
     if are_safe:
         print("User input and model output deemed safe.")
diff --git a/examples/llama_guard/README.md b/examples/llama_guard/README.md
new file mode 100644
index 000000000..fe6207c4c
--- /dev/null
+++ b/examples/llama_guard/README.md
@@ -0,0 +1,19 @@
+# Llama Guard demo
+<!-- markdown-link-check-disable -->
+Llama Guard is a new experimental model that provides input and output guardrails for LLM deployments. For more details, please visit the main [repository](https://github.com/facebookresearch/PurpleLlama/tree/main/Llama-Guard).
+
+This folder contains the files for the function used in the safety_checker when running in the inference script.
+
+## Requirements
+1. Llama guard model weights downloaded. To download, follow the steps shown [here](https://github.com/facebookresearch/PurpleLlama/tree/main/Llama-Guard#download)
+2. Llama recipes dependencies installed 
+3. A GPU with at least 21 GB of free RAM to load the 7B model. To run both Llama 2 7B and Llama Guard, multiple GPUS or a single one with additional memory is required.
+<!-- markdown-link-check-enable -->
+### Inference Safety Checker
+When running the regular inference script with prompts, Llama Guard will be used as a safety checker on the user prompt and the model output. If both are safe, the result will be show, else a message with the error will be show, with the word unsafe and a comma separated list of categories infringed. As the model is not quantized, it requires more GPU than the direct examples, to load the desired Llama model for inference and the Llama Guard model for safety checks. Using Llama 2 7B quantized, this was able to be run in a machine with four A10G GPUs.
+Use this command for testing with a quantized Llama model, modifying the values accordingly:
+
+`RANK=0 WORLD_SIZE=1 MASTER_ADDR=127.0.0.1 MASTER_PORT=29500 python examples/inference.py --model_name <path_to_regular_llama_model> --prompt_file <path_to_prompt_file> --quantization --enable_llamaguard_content_safety --llamaguard_model_name <path_to_mode>`
+
+
+
diff --git a/examples/llama_guard/__init__.py b/examples/llama_guard/__init__.py
new file mode 100755
index 000000000..0bd1f8635
--- /dev/null
+++ b/examples/llama_guard/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
+
+from .generation import Llama, Dialog
+from .model import ModelArgs, Transformer
+from .tokenizer import Tokenizer
diff --git a/examples/llama_guard/generation.py b/examples/llama_guard/generation.py
new file mode 100755
index 000000000..4b22b581e
--- /dev/null
+++ b/examples/llama_guard/generation.py
@@ -0,0 +1,458 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
+
+import json
+import os
+import sys
+import time
+from pathlib import Path
+from typing import List, Literal, Optional, Tuple, TypedDict
+
+import torch
+import torch.nn.functional as F
+from fairscale.nn.model_parallel.initialize import (
+    get_model_parallel_rank,
+    initialize_model_parallel,
+    model_parallel_is_initialized,
+)
+
+from llama_guard.model import ModelArgs, Transformer
+from llama_guard.tokenizer import Tokenizer
+
+Role = Literal["system", "user", "assistant"]
+
+
+class Message(TypedDict):
+    role: Role
+    content: str
+
+
+class CompletionPrediction(TypedDict, total=False):
+    generation: str
+    tokens: List[str]  # not required
+    logprobs: List[float]  # not required
+
+
+class ChatPrediction(TypedDict, total=False):
+    generation: Message
+    tokens: List[str]  # not required
+    logprobs: List[float]  # not required
+
+
+Dialog = List[Message]
+
+B_INST, E_INST = "[INST]", "[/INST]"
+B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
+
+SPECIAL_TAGS = [B_INST, E_INST, "<<SYS>>", "<</SYS>>"]
+UNSAFE_ERROR = "Error: special tags are not allowed as part of the prompt."
+
+
+class Llama:
+    @staticmethod
+    def build(
+        ckpt_dir: str,
+        tokenizer_path: str,
+        max_seq_len: int,
+        max_batch_size: int,
+        model_parallel_size: Optional[int] = None,
+        seed: int = 1,
+    ) -> "Llama":
+        """
+        Build a Llama instance by initializing and loading a pre-trained model.
+
+        Args:
+            ckpt_dir (str): Path to the directory containing checkpoint files.
+            tokenizer_path (str): Path to the tokenizer file.
+            max_seq_len (int): Maximum sequence length for input text.
+            max_batch_size (int): Maximum batch size for inference.
+            model_parallel_size (Optional[int], optional): Number of model parallel processes.
+                If not provided, it's determined from the environment. Defaults to None.
+
+        Returns:
+            Llama: An instance of the Llama class with the loaded model and tokenizer.
+
+        Raises:
+            AssertionError: If there are no checkpoint files in the specified directory,
+                or if the model parallel size does not match the number of checkpoint files.
+
+        Note:
+            This method initializes the distributed process group, sets the device to CUDA,
+            and loads the pre-trained model and tokenizer.
+
+        """
+        if not torch.distributed.is_initialized():
+            torch.distributed.init_process_group("nccl")
+        if not model_parallel_is_initialized():
+            if model_parallel_size is None:
+                model_parallel_size = int(os.environ.get("WORLD_SIZE", 1))
+            initialize_model_parallel(model_parallel_size)
+
+        local_rank = int(os.environ.get("LOCAL_RANK", 0))
+        torch.cuda.set_device(local_rank)
+
+        # seed must be the same in all processes
+        torch.manual_seed(seed)
+
+        if local_rank > 0:
+            sys.stdout = open(os.devnull, "w")
+
+        start_time = time.time()
+        checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
+        checkpoints_size = len(checkpoints)
+        assert checkpoints_size > 0, f"no checkpoint files found in {ckpt_dir}"
+        ckpt_path = checkpoints[get_model_parallel_rank()]
+        checkpoint = torch.load(ckpt_path, map_location="cpu")
+        with open(Path(ckpt_dir) / "params.json", "r") as f:
+            params = json.loads(f.read())
+
+        model_args: ModelArgs = ModelArgs(
+            max_seq_len=max_seq_len,
+            max_batch_size=max_batch_size,
+            **params,
+        )
+        tokenizer = Tokenizer(model_path=tokenizer_path)
+        model_args.vocab_size = tokenizer.n_words
+        torch.set_default_tensor_type(torch.cuda.HalfTensor)
+        model = Transformer(model_args)
+        model.load_state_dict(checkpoint, strict=False)
+        print(f"Loaded in {time.time() - start_time:.2f} seconds")
+
+        return Llama(model, tokenizer)
+
+    def __init__(self, model: Transformer, tokenizer: Tokenizer):
+        self.model = model
+        self.tokenizer = tokenizer
+
+    @torch.inference_mode()
+    def generate(
+        self,
+        prompt_tokens: List[List[int]],
+        max_gen_len: int,
+        temperature: float = 0.6,
+        top_p: float = 0.9,
+        logprobs: bool = False,
+        echo: bool = False,
+    ) -> Tuple[List[List[int]], Optional[List[List[float]]]]:
+        """
+        Generate text sequences based on provided prompts using the language generation model.
+
+        Args:
+            prompt_tokens (List[List[int]]): List of tokenized prompts, where each prompt is represented as a list of integers.
+            max_gen_len (int): Maximum length of the generated text sequence.
+            temperature (float, optional): Temperature value for controlling randomness in sampling. Defaults to 0.6.
+            top_p (float, optional): Top-p probability threshold for nucleus sampling. Defaults to 0.9.
+            logprobs (bool, optional): Flag indicating whether to compute token log probabilities. Defaults to False.
+            echo (bool, optional): Flag indicating whether to include prompt tokens in the generated output. Defaults to False.
+
+        Returns:
+            Tuple[List[List[int]], Optional[List[List[float]]]]: A tuple containing generated token sequences and, if logprobs is True, corresponding token log probabilities.
+
+        Note:
+            This method uses the provided prompts as a basis for generating text. It employs nucleus sampling to produce text with controlled randomness.
+            If logprobs is True, token log probabilities are computed for each generated token.
+
+        """
+        params = self.model.params
+        bsz = len(prompt_tokens)
+        assert bsz <= params.max_batch_size, (bsz, params.max_batch_size)
+
+        min_prompt_len = min(len(t) for t in prompt_tokens)
+        max_prompt_len = max(len(t) for t in prompt_tokens)
+        assert max_prompt_len <= params.max_seq_len
+        total_len = min(params.max_seq_len, max_gen_len + max_prompt_len)
+
+        pad_id = self.tokenizer.pad_id
+        tokens = torch.full((bsz, total_len), pad_id, dtype=torch.long, device="cuda")
+        for k, t in enumerate(prompt_tokens):
+            tokens[k, : len(t)] = torch.tensor(t, dtype=torch.long, device="cuda")
+        if logprobs:
+            token_logprobs = torch.zeros_like(tokens, dtype=torch.float)
+
+        prev_pos = 0
+        eos_reached = torch.tensor([False] * bsz, device="cuda")
+        input_text_mask = tokens != pad_id
+        if min_prompt_len == total_len:
+            logits = self.model.forward(tokens, prev_pos)
+            token_logprobs = -F.cross_entropy(
+                input=logits.transpose(1, 2),
+                target=tokens,
+                reduction="none",
+                ignore_index=pad_id,
+            )
+
+        for cur_pos in range(min_prompt_len, total_len):
+            logits = self.model.forward(tokens[:, prev_pos:cur_pos], prev_pos)
+            if temperature > 0:
+                probs = torch.softmax(logits[:, -1] / temperature, dim=-1)
+                next_token = sample_top_p(probs, top_p)
+            else:
+                next_token = torch.argmax(logits[:, -1], dim=-1)
+
+            next_token = next_token.reshape(-1)
+            # only replace token if prompt has already been generated
+            next_token = torch.where(
+                input_text_mask[:, cur_pos], tokens[:, cur_pos], next_token
+            )
+            tokens[:, cur_pos] = next_token
+            if logprobs:
+                token_logprobs[:, prev_pos + 1 : cur_pos + 1] = -F.cross_entropy(
+                    input=logits.transpose(1, 2),
+                    target=tokens[:, prev_pos + 1 : cur_pos + 1],
+                    reduction="none",
+                    ignore_index=pad_id,
+                )
+            eos_reached |= (~input_text_mask[:, cur_pos]) & (
+                next_token == self.tokenizer.eos_id
+            )
+            prev_pos = cur_pos
+            if all(eos_reached):
+                break
+
+        if logprobs:
+            token_logprobs = token_logprobs.tolist()
+        out_tokens, out_logprobs = [], []
+        for i, toks in enumerate(tokens.tolist()):
+            # cut to max gen len
+            start = 0 if echo else len(prompt_tokens[i])
+            toks = toks[start : len(prompt_tokens[i]) + max_gen_len]
+            probs = None
+            if logprobs:
+                probs = token_logprobs[i][start : len(prompt_tokens[i]) + max_gen_len]
+            # cut to eos tok if any
+            if self.tokenizer.eos_id in toks:
+                eos_idx = toks.index(self.tokenizer.eos_id)
+                toks = toks[:eos_idx]
+                probs = probs[:eos_idx] if logprobs else None
+            out_tokens.append(toks)
+            out_logprobs.append(probs)
+        return (out_tokens, out_logprobs if logprobs else None)
+
+    def text_completion(
+        self,
+        prompts: List[str],
+        temperature: float = 0.6,
+        top_p: float = 0.9,
+        max_gen_len: Optional[int] = None,
+        logprobs: bool = False,
+        echo: bool = False,
+    ) -> List[CompletionPrediction]:
+        """
+        Perform text completion for a list of prompts using the language generation model.
+
+        Args:
+            prompts (List[str]): List of text prompts for completion.
+            temperature (float, optional): Temperature value for controlling randomness in sampling. Defaults to 0.6.
+            top_p (float, optional): Top-p probability threshold for nucleus sampling. Defaults to 0.9.
+            max_gen_len (Optional[int], optional): Maximum length of the generated completion sequence.
+                If not provided, it's set to the model's maximum sequence length minus 1.
+            logprobs (bool, optional): Flag indicating whether to compute token log probabilities. Defaults to False.
+            echo (bool, optional): Flag indicating whether to include prompt tokens in the generated output. Defaults to False.
+
+        Returns:
+            List[CompletionPrediction]: List of completion predictions, each containing the generated text completion.
+
+        Note:
+            This method generates text completions for the provided prompts, employing nucleus sampling to introduce controlled randomness.
+            If logprobs is True, token log probabilities are computed for each generated token.
+
+        """
+        if max_gen_len is None:
+            max_gen_len = self.model.params.max_seq_len - 1
+        prompt_tokens = [self.tokenizer.encode(x, bos=True, eos=False) for x in prompts]
+        generation_tokens, generation_logprobs = self.generate(
+            prompt_tokens=prompt_tokens,
+            max_gen_len=max_gen_len,
+            temperature=temperature,
+            top_p=top_p,
+            logprobs=logprobs,
+            echo=echo,
+        )
+        if logprobs:
+            return [
+                {
+                    "generation": self.tokenizer.decode(t),
+                    "tokens": [self.tokenizer.decode(x) for x in t],
+                    "logprobs": logprobs_i,
+                }
+                for t, logprobs_i in zip(generation_tokens, generation_logprobs)
+            ]
+        return [{"generation": self.tokenizer.decode(t)} for t in generation_tokens]
+
+    def chat_completion(
+        self,
+        dialogs: List[Dialog],
+        temperature: float = 0.6,
+        top_p: float = 0.9,
+        max_gen_len: Optional[int] = None,
+        logprobs: bool = False,
+    ) -> List[ChatPrediction]:
+        """
+        Generate assistant responses for a list of conversational dialogs using the language generation model.
+
+        Args:
+            dialogs (List[Dialog]): List of conversational dialogs, where each dialog is a list of messages.
+            temperature (float, optional): Temperature value for controlling randomness in sampling. Defaults to 0.6.
+            top_p (float, optional): Top-p probability threshold for nucleus sampling. Defaults to 0.9.
+            max_gen_len (Optional[int], optional): Maximum length of the generated response sequence.
+                If not provided, it's set to the model's maximum sequence length minus 1.
+            logprobs (bool, optional): Flag indicating whether to compute token log probabilities. Defaults to False.
+
+        Returns:
+            List[ChatPrediction]: List of chat predictions, each containing the assistant's generated response.
+
+        Raises:
+            AssertionError: If the last message in a dialog is not from the user.
+            AssertionError: If the dialog roles are not in the required 'user', 'assistant', and optional 'system' order.
+
+        Note:
+            This method generates assistant responses for the provided conversational dialogs.
+            It employs nucleus sampling to introduce controlled randomness in text generation.
+            If logprobs is True, token log probabilities are computed for each generated token.
+
+        """
+        if max_gen_len is None:
+            max_gen_len = self.model.params.max_seq_len - 1
+        prompt_tokens = []
+        unsafe_requests = []
+        for dialog in dialogs:
+            unsafe_requests.append(
+                any([tag in msg["content"] for tag in SPECIAL_TAGS for msg in dialog])
+            )
+            if dialog[0]["role"] == "system":
+                dialog = [
+                    {
+                        "role": dialog[1]["role"],
+                        "content": B_SYS
+                        + dialog[0]["content"]
+                        + E_SYS
+                        + dialog[1]["content"],
+                    }
+                ] + dialog[2:]
+            assert all([msg["role"] == "user" for msg in dialog[::2]]) and all(
+                [msg["role"] == "assistant" for msg in dialog[1::2]]
+            ), (
+                "model only supports 'system', 'user' and 'assistant' roles, "
+                "starting with 'system', then 'user' and alternating (u/a/u/a/u...)"
+            )
+            dialog_tokens: List[int] = sum(
+                [
+                    self.tokenizer.encode(
+                        f"{B_INST} {(prompt['content']).strip()} {E_INST} {(answer['content']).strip()} ",
+                        bos=True,
+                        eos=True,
+                    )
+                    for prompt, answer in zip(
+                        dialog[::2],
+                        dialog[1::2],
+                    )
+                ],
+                [],
+            )
+            assert (
+                dialog[-1]["role"] == "user"
+            ), f"Last message must be from user, got {dialog[-1]['role']}"
+            dialog_tokens += self.tokenizer.encode(
+                f"{B_INST} {(dialog[-1]['content']).strip()} {E_INST}",
+                bos=True,
+                eos=False,
+            )
+            prompt_tokens.append(dialog_tokens)
+
+        generation_tokens, generation_logprobs = self.generate(
+            prompt_tokens=prompt_tokens,
+            max_gen_len=max_gen_len,
+            temperature=temperature,
+            top_p=top_p,
+            logprobs=logprobs,
+        )
+        if logprobs:
+            return [
+                {
+                    "generation": {
+                        "role": "assistant",
+                        "content": self.tokenizer.decode(t)
+                        if not unsafe
+                        else UNSAFE_ERROR,
+                    },
+                    "tokens": [self.tokenizer.decode(x) for x in t],
+                    "logprobs": logprobs_i,
+                }
+                for t, logprobs_i, unsafe in zip(
+                    generation_tokens, generation_logprobs, unsafe_requests
+                )
+            ]
+        return [
+            {
+                "generation": {
+                    "role": "assistant",
+                    "content": self.tokenizer.decode(t) if not unsafe else UNSAFE_ERROR,
+                }
+            }
+            for t, unsafe in zip(generation_tokens, unsafe_requests)
+        ]
+    
+    def single_prompt_completion(
+        self,
+        prompt: str,
+        temperature: float = 0.6,
+        top_p: float = 0.9,
+        max_gen_len: Optional[int] = None,
+        echo: bool = False,
+    ) -> str:
+        """
+        Perform text completion for a single prompt using the language generation model.
+
+        Args:
+            prompts (str): prompt for completion.
+            temperature (float, optional): Temperature value for controlling randomness in sampling. Defaults to 0.6.
+            top_p (float, optional): Top-p probability threshold for nucleus sampling. Defaults to 0.9.
+            max_gen_len (Optional[int], optional): Maximum length of the generated completion sequence.
+                If not provided, it's set to the model's maximum sequence length minus 1.
+            
+
+        Returns:
+            str: single string with the decoded output from the model.
+
+        Note:
+            This method generates text completions for the provided prompts, employing nucleus sampling to introduce controlled randomness.
+        """
+        if max_gen_len is None:
+            max_gen_len = self.model.params.max_seq_len - 1
+        prompt_tokens = [self.tokenizer.encode(f"{B_INST} {prompt.strip()} {E_INST}", bos=True, eos=False)]
+        generation_tokens = self.generate(
+            prompt_tokens=prompt_tokens,
+            max_gen_len=max_gen_len,
+            temperature=temperature,
+            top_p=top_p,
+            logprobs=False,
+            echo=echo,
+        )
+        single_result_list = self.tokenizer.decode(generation_tokens[0])
+        return single_result_list[0]
+
+
+def sample_top_p(probs, p):
+    """
+    Perform top-p (nucleus) sampling on a probability distribution.
+
+    Args:
+        probs (torch.Tensor): Probability distribution tensor.
+        p (float): Probability threshold for top-p sampling.
+
+    Returns:
+        torch.Tensor: Sampled token indices.
+
+    Note:
+        Top-p sampling selects the smallest set of tokens whose cumulative probability mass
+        exceeds the threshold p. The distribution is renormalized based on the selected tokens.
+
+    """
+    probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
+    probs_sum = torch.cumsum(probs_sort, dim=-1)
+    mask = probs_sum - probs_sort > p
+    probs_sort[mask] = 0.0
+    probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
+    next_token = torch.multinomial(probs_sort, num_samples=1)
+    next_token = torch.gather(probs_idx, -1, next_token)
+    return next_token
diff --git a/examples/llama_guard/model.py b/examples/llama_guard/model.py
new file mode 100755
index 000000000..c78570f68
--- /dev/null
+++ b/examples/llama_guard/model.py
@@ -0,0 +1,495 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
+
+import math
+from dataclasses import dataclass
+from typing import Optional, Tuple
+
+import fairscale.nn.model_parallel.initialize as fs_init
+import torch
+import torch.nn.functional as F
+from fairscale.nn.model_parallel.layers import (
+    ColumnParallelLinear,
+    ParallelEmbedding,
+    RowParallelLinear,
+)
+from torch import nn
+
+
+@dataclass
+class ModelArgs:
+    dim: int = 4096
+    n_layers: int = 32
+    n_heads: int = 32
+    n_kv_heads: Optional[int] = None
+    vocab_size: int = -1  # defined later by tokenizer
+    multiple_of: int = 256  # make SwiGLU hidden layer size multiple of large power of 2
+    ffn_dim_multiplier: Optional[float] = None
+    norm_eps: float = 1e-5
+
+    max_batch_size: int = 32
+    max_seq_len: int = 2048
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        """
+        Initialize the RMSNorm normalization layer.
+
+        Args:
+            dim (int): The dimension of the input tensor.
+            eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
+
+        Attributes:
+            eps (float): A small value added to the denominator for numerical stability.
+            weight (nn.Parameter): Learnable scaling parameter.
+
+        """
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        """
+        Apply the RMSNorm normalization to the input tensor.
+
+        Args:
+            x (torch.Tensor): The input tensor.
+
+        Returns:
+            torch.Tensor: The normalized tensor.
+
+        """
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        """
+        Forward pass through the RMSNorm layer.
+
+        Args:
+            x (torch.Tensor): The input tensor.
+
+        Returns:
+            torch.Tensor: The output tensor after applying RMSNorm.
+
+        """
+        output = self._norm(x.float()).type_as(x)
+        return output * self.weight
+
+
+def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
+    """
+    Precompute the frequency tensor for complex exponentials (cis) with given dimensions.
+
+    This function calculates a frequency tensor with complex exponentials using the given dimension 'dim'
+    and the end index 'end'. The 'theta' parameter scales the frequencies.
+    The returned tensor contains complex values in complex64 data type.
+
+    Args:
+        dim (int): Dimension of the frequency tensor.
+        end (int): End index for precomputing frequencies.
+        theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0.
+
+    Returns:
+        torch.Tensor: Precomputed frequency tensor with complex exponentials.
+
+    
+        
+
+    """
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    t = torch.arange(end, device=freqs.device)  # type: ignore
+    freqs = torch.outer(t, freqs).float()  # type: ignore
+    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
+    return freqs_cis
+
+
+def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
+    """
+    Reshape frequency tensor for broadcasting it with another tensor.
+
+    This function reshapes the frequency tensor to have the same shape as the target tensor 'x'
+    for the purpose of broadcasting the frequency tensor during element-wise operations.
+
+    Args:
+        freqs_cis (torch.Tensor): Frequency tensor to be reshaped.
+        x (torch.Tensor): Target tensor for broadcasting compatibility.
+
+    Returns:
+        torch.Tensor: Reshaped frequency tensor.
+
+    Raises:
+        AssertionError: If the frequency tensor doesn't match the expected shape.
+        AssertionError: If the target tensor 'x' doesn't have the expected number of dimensions.
+    """
+    ndim = x.ndim
+    assert 0 <= 1 < ndim
+    assert freqs_cis.shape == (x.shape[1], x.shape[-1])
+    shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+    return freqs_cis.view(*shape)
+
+
+def apply_rotary_emb(
+    xq: torch.Tensor,
+    xk: torch.Tensor,
+    freqs_cis: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Apply rotary embeddings to input tensors using the given frequency tensor.
+
+    This function applies rotary embeddings to the given query 'xq' and key 'xk' tensors using the provided
+    frequency tensor 'freqs_cis'. The input tensors are reshaped as complex numbers, and the frequency tensor
+    is reshaped for broadcasting compatibility. The resulting tensors contain rotary embeddings and are
+    returned as real tensors.
+
+    Args:
+        xq (torch.Tensor): Query tensor to apply rotary embeddings.
+        xk (torch.Tensor): Key tensor to apply rotary embeddings.
+        freqs_cis (torch.Tensor): Precomputed frequency tensor for complex exponentials.
+
+    Returns:
+        Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
+
+        
+
+    """
+    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+    freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
+    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
+    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+
+def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
+    bs, slen, n_kv_heads, head_dim = x.shape
+    if n_rep == 1:
+        return x
+    return (
+        x[:, :, :, None, :]
+        .expand(bs, slen, n_kv_heads, n_rep, head_dim)
+        .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
+    )
+
+
+class Attention(nn.Module):
+    """Multi-head attention module."""
+    def __init__(self, args: ModelArgs):
+        """
+        Initialize the Attention module.
+
+        Args:
+            args (ModelArgs): Model configuration parameters.
+
+        Attributes:
+            n_kv_heads (int): Number of key and value heads.
+            n_local_heads (int): Number of local query heads.
+            n_local_kv_heads (int): Number of local key and value heads.
+            n_rep (int): Number of repetitions for local heads.
+            head_dim (int): Dimension size of each attention head.
+            wq (ColumnParallelLinear): Linear transformation for queries.
+            wk (ColumnParallelLinear): Linear transformation for keys.
+            wv (ColumnParallelLinear): Linear transformation for values.
+            wo (RowParallelLinear): Linear transformation for output.
+            cache_k (torch.Tensor): Cached keys for attention.
+            cache_v (torch.Tensor): Cached values for attention.
+
+        """
+        super().__init__()
+        self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
+        model_parallel_size = fs_init.get_model_parallel_world_size()
+        self.n_local_heads = args.n_heads // model_parallel_size
+        self.n_local_kv_heads = self.n_kv_heads // model_parallel_size
+        self.n_rep = self.n_local_heads // self.n_local_kv_heads
+        self.head_dim = args.dim // args.n_heads
+
+        self.wq = ColumnParallelLinear(
+            args.dim,
+            args.n_heads * self.head_dim,
+            bias=False,
+            gather_output=False,
+            init_method=lambda x: x,
+        )
+        self.wk = ColumnParallelLinear(
+            args.dim,
+            self.n_kv_heads * self.head_dim,
+            bias=False,
+            gather_output=False,
+            init_method=lambda x: x,
+        )
+        self.wv = ColumnParallelLinear(
+            args.dim,
+            self.n_kv_heads * self.head_dim,
+            bias=False,
+            gather_output=False,
+            init_method=lambda x: x,
+        )
+        self.wo = RowParallelLinear(
+            args.n_heads * self.head_dim,
+            args.dim,
+            bias=False,
+            input_is_parallel=True,
+            init_method=lambda x: x,
+        )
+
+        self.cache_k = torch.zeros(
+            (
+                args.max_batch_size,
+                args.max_seq_len,
+                self.n_local_kv_heads,
+                self.head_dim,
+            )
+        ).cuda()
+        self.cache_v = torch.zeros(
+            (
+                args.max_batch_size,
+                args.max_seq_len,
+                self.n_local_kv_heads,
+                self.head_dim,
+            )
+        ).cuda()
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        start_pos: int,
+        freqs_cis: torch.Tensor,
+        mask: Optional[torch.Tensor],
+    ):
+        """
+        Forward pass of the attention module.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+            start_pos (int): Starting position for caching.
+            freqs_cis (torch.Tensor): Precomputed frequency tensor.
+            mask (torch.Tensor, optional): Attention mask tensor.
+
+        Returns:
+            torch.Tensor: Output tensor after attention.
+
+        """
+        bsz, seqlen, _ = x.shape
+        xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
+
+        xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+        xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
+        xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
+
+        xq, xk = apply_rotary_emb(xq, xk, freqs_cis=freqs_cis)
+
+        self.cache_k = self.cache_k.to(xq)
+        self.cache_v = self.cache_v.to(xq)
+
+        self.cache_k[:bsz, start_pos : start_pos + seqlen] = xk
+        self.cache_v[:bsz, start_pos : start_pos + seqlen] = xv
+
+        keys = self.cache_k[:bsz, : start_pos + seqlen]
+        values = self.cache_v[:bsz, : start_pos + seqlen]
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        keys = repeat_kv(keys, self.n_rep)  # (bs, cache_len + seqlen, n_local_heads, head_dim)
+        values = repeat_kv(values, self.n_rep)  # (bs, cache_len + seqlen, n_local_heads, head_dim)
+
+        xq = xq.transpose(1, 2)  # (bs, n_local_heads, seqlen, head_dim)
+        keys = keys.transpose(1, 2) # (bs, n_local_heads, cache_len + seqlen, head_dim)
+        values = values.transpose(1, 2) # (bs, n_local_heads, cache_len + seqlen, head_dim)
+        scores = torch.matmul(xq, keys.transpose(2, 3)) / math.sqrt(self.head_dim)
+        if mask is not None:
+            scores = scores + mask  # (bs, n_local_heads, seqlen, cache_len + seqlen)
+        scores = F.softmax(scores.float(), dim=-1).type_as(xq)
+        output = torch.matmul(scores, values)  # (bs, n_local_heads, seqlen, head_dim)
+        output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)
+        return self.wo(output)
+
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        hidden_dim: int,
+        multiple_of: int,
+        ffn_dim_multiplier: Optional[float],
+    ):
+        """
+        Initialize the FeedForward module.
+
+        Args:
+            dim (int): Input dimension.
+            hidden_dim (int): Hidden dimension of the feedforward layer.
+            multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
+            ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None.
+
+        Attributes:
+            w1 (ColumnParallelLinear): Linear transformation for the first layer.
+            w2 (RowParallelLinear): Linear transformation for the second layer.
+            w3 (ColumnParallelLinear): Linear transformation for the third layer.
+
+        """
+        super().__init__()
+        hidden_dim = int(2 * hidden_dim / 3)
+        # custom dim factor multiplier
+        if ffn_dim_multiplier is not None:
+            hidden_dim = int(ffn_dim_multiplier * hidden_dim)
+        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+
+        self.w1 = ColumnParallelLinear(
+            dim, hidden_dim, bias=False, gather_output=False, init_method=lambda x: x
+        )
+        self.w2 = RowParallelLinear(
+            hidden_dim, dim, bias=False, input_is_parallel=True, init_method=lambda x: x
+        )
+        self.w3 = ColumnParallelLinear(
+            dim, hidden_dim, bias=False, gather_output=False, init_method=lambda x: x
+        )
+
+    def forward(self, x):
+        return self.w2(F.silu(self.w1(x)) * self.w3(x))
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, layer_id: int, args: ModelArgs):
+        """
+        Initialize a TransformerBlock.
+
+        Args:
+            layer_id (int): Identifier for the layer.
+            args (ModelArgs): Model configuration parameters.
+
+        Attributes:
+            n_heads (int): Number of attention heads.
+            dim (int): Dimension size of the model.
+            head_dim (int): Dimension size of each attention head.
+            attention (Attention): Attention module.
+            feed_forward (FeedForward): FeedForward module.
+            layer_id (int): Identifier for the layer.
+            attention_norm (RMSNorm): Layer normalization for attention output.
+            ffn_norm (RMSNorm): Layer normalization for feedforward output.
+
+        """
+        super().__init__()
+        self.n_heads = args.n_heads
+        self.dim = args.dim
+        self.head_dim = args.dim // args.n_heads
+        self.attention = Attention(args)
+        self.feed_forward = FeedForward(
+            dim=args.dim,
+            hidden_dim=4 * args.dim,
+            multiple_of=args.multiple_of,
+            ffn_dim_multiplier=args.ffn_dim_multiplier,
+        )
+        self.layer_id = layer_id
+        self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
+        self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        start_pos: int,
+        freqs_cis: torch.Tensor,
+        mask: Optional[torch.Tensor],
+    ):
+        """
+        Perform a forward pass through the TransformerBlock.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+            start_pos (int): Starting position for attention caching.
+            freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies.
+            mask (torch.Tensor, optional): Masking tensor for attention. Defaults to None.
+
+        Returns:
+            torch.Tensor: Output tensor after applying attention and feedforward layers.
+
+        """
+        h = x + self.attention.forward(
+            self.attention_norm(x), start_pos, freqs_cis, mask
+        )
+        out = h + self.feed_forward.forward(self.ffn_norm(h))
+        return out
+
+
+class Transformer(nn.Module):
+    def __init__(self, params: ModelArgs):
+        """
+        Initialize a Transformer model.
+
+        Args:
+            params (ModelArgs): Model configuration parameters.
+
+        Attributes:
+            params (ModelArgs): Model configuration parameters.
+            vocab_size (int): Vocabulary size.
+            n_layers (int): Number of layers in the model.
+            tok_embeddings (ParallelEmbedding): Token embeddings.
+            layers (torch.nn.ModuleList): List of Transformer blocks.
+            norm (RMSNorm): Layer normalization for the model output.
+            output (ColumnParallelLinear): Linear layer for final output.
+            freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies.
+
+        """
+        super().__init__()
+        self.params = params
+        self.vocab_size = params.vocab_size
+        self.n_layers = params.n_layers
+
+        self.tok_embeddings = ParallelEmbedding(
+            params.vocab_size, params.dim, init_method=lambda x: x
+        )
+
+        self.layers = torch.nn.ModuleList()
+        for layer_id in range(params.n_layers):
+            self.layers.append(TransformerBlock(layer_id, params))
+
+        self.norm = RMSNorm(params.dim, eps=params.norm_eps)
+        self.output = ColumnParallelLinear(
+            params.dim, params.vocab_size, bias=False, init_method=lambda x: x
+        )
+
+        self.freqs_cis = precompute_freqs_cis(
+            # Note that self.params.max_seq_len is multiplied by 2 because the token limit for the Llama 2 generation of models is 4096. 
+            # Adding this multiplier instead of using 4096 directly allows for dynamism of token lengths while training or fine-tuning.
+            self.params.dim // self.params.n_heads, self.params.max_seq_len * 2
+        )
+
+    @torch.inference_mode()
+    def forward(self, tokens: torch.Tensor, start_pos: int):
+        """
+        Perform a forward pass through the Transformer model.
+
+        Args:
+            tokens (torch.Tensor): Input token indices.
+            start_pos (int): Starting position for attention caching.
+
+        Returns:
+            torch.Tensor: Output logits after applying the Transformer model.
+
+        """
+        _bsz, seqlen = tokens.shape
+        h = self.tok_embeddings(tokens)
+        self.freqs_cis = self.freqs_cis.to(h.device)
+        freqs_cis = self.freqs_cis[start_pos : start_pos + seqlen]
+
+        mask = None
+        if seqlen > 1:
+            mask = torch.full(
+                (seqlen, seqlen), float("-inf"), device=tokens.device
+            )
+
+            mask = torch.triu(mask, diagonal=1)
+
+            # When performing key-value caching, we compute the attention scores
+            # only for the new sequence. Thus, the matrix of scores is of size
+            # (seqlen, cache_len + seqlen), and the only masked entries are (i, j) for
+            # j > cache_len + i, since row i corresponds to token cache_len + i.
+            mask = torch.hstack([
+                torch.zeros((seqlen, start_pos), device=tokens.device),
+                mask
+            ]).type_as(h)
+
+        for layer in self.layers:
+            h = layer(h, start_pos, freqs_cis, mask)
+        h = self.norm(h)
+        output = self.output(h).float()
+        return output
diff --git a/examples/llama_guard/prompt_format.py b/examples/llama_guard/prompt_format.py
new file mode 100644
index 000000000..965f4b0a9
--- /dev/null
+++ b/examples/llama_guard/prompt_format.py
@@ -0,0 +1,146 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
+
+from dataclasses import dataclass
+from string import Template
+from typing import List
+from enum import Enum
+
+class AgentType(Enum):
+    AGENT = "Agent"
+    USER = "User"
+
+@dataclass
+class SafetyCategory:
+    name: str
+    description: str
+
+@dataclass
+class ConversationTurn:
+    message: str
+    agent_type: AgentType
+
+CATEGORY_SHORT_NAME_PREFIX = "O"
+
+PROMPT_TASK = "Task: Check if there is unsafe content in '$agent_type' messages in conversations according our safety policy with the below categories."
+
+SAFETY_CATEGORIES = """
+<BEGIN UNSAFE CONTENT CATEGORIES>
+$categories
+<END UNSAFE CONTENT CATEGORIES>
+"""
+
+PROMPT_CONVERSATION = """
+<BEGIN CONVERSATION>
+$conversations
+<END CONVERSATION>
+"""
+PROMPT_INSTRUCTIONS = """
+Provide your safety assessment for $agent_type in the above conversation:
+- First line must read 'safe' or 'unsafe'.
+- If unsafe, a second line must include a comma-separated list of violated categories."""
+
+PROMPT_TEMPLATE = Template(f"{PROMPT_TASK}{SAFETY_CATEGORIES}{PROMPT_CONVERSATION}{PROMPT_INSTRUCTIONS}")
+
+LLAMA_GUARD_CATEGORY = [
+    SafetyCategory(
+        "Violence and Hate.",
+        """Should not
+ - Help people plan or engage in violence.
+ - Encourage people to engage in violence.
+ - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability.
+ - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics.
+Can
+ - Provide information on violence and discrimination.
+ - Discuss the topics of hate and violence, and can discuss historical events involving violence."""),
+    SafetyCategory(
+        "Sexual Content.",
+        """Should not
+ - Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities.
+Can
+ - Discuss topics related to sexuality and sexual education.
+ - Provide general information regarding sexual activities.
+ - Provide guidance on topics related to sexual health."""),
+    SafetyCategory(
+        "Criminal Planning.",
+        """Should not
+ - Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them.
+ - Encourage people to engage in criminal activities or condone criminal behavior.
+Can
+ - Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes.
+ - Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems."""),
+    SafetyCategory(
+        "Guns and Illegal Weapons.",
+        """Should not
+ - Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them.
+ - Encourage people to engage in firearm or illegal weapons crimes or condone such behavior.
+Can
+ - Discuss firearms and the arguments for and against firearm ownership."""),
+    SafetyCategory(
+        "Regulated or Controlled Substances.",
+        """Should not
+ - Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis.
+ - Assist or encourage people to create such substances when it is illegal to do so.
+Can
+ - Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are).
+ - Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation."""),
+    SafetyCategory(
+        "Self-Harm.",
+        """Should not
+ - Encourage people to harm themselves.
+ - Romanticize or condone self-harm or suicide.
+ - Provide information regarding the methods of suicide or self-harm.
+ - Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves.
+Can
+ - Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions).
+Should
+ - Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves."""),
+]
+
+def create_conversation(messges: List[str]) -> List[ConversationTurn]:
+    conversations = []
+    for i, messge in enumerate(messges):
+        conversations.append(ConversationTurn(message=messge, agent_type=AgentType.USER if i % 2 == 0 else AgentType.AGENT))
+
+    return conversations
+
+def build_prompt(
+        agent_type: AgentType, 
+        categories: List[SafetyCategory], 
+        conversations: List[ConversationTurn], 
+        category_short_name_prefix: str = CATEGORY_SHORT_NAME_PREFIX):
+    categories_str = "\n".join([f"{category_short_name_prefix}{i+1}: {c.name}\n{c.description}" for i, c in enumerate(categories)])
+    conversations_str = "\n\n".join([f"{t.agent_type.value}: {t.message}" for t in conversations])
+
+    return PROMPT_TEMPLATE.substitute(agent_type=agent_type.value, categories=categories_str, conversations=conversations_str)
+
+def build_prompt_test():
+    print(build_prompt(AgentType.USER,
+        LLAMA_GUARD_CATEGORY,
+        [
+            ConversationTurn("Why have some cultures engaged in violence directed at disabled individuals?", AgentType.USER)
+        ]))
+    
+    print("\n\n")
+
+    # use a customized safety category and create_conversation function. 
+    print(build_prompt(
+        AgentType.AGENT,
+        [
+            SafetyCategory("Violence and Hate.","""Should not
+ - Help people plan or engage in violence.
+ - Encourage people to engage in violence.
+ - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability.
+ - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics.
+Can
+ - Provide information on violence and discrimination.
+ - Discuss the topics of hate and violence, and can discuss historical events involving violence.""",
+        ),],
+        create_conversation(
+        [
+            "Why have some cultures engaged in violence directed at disabled individuals?",
+            "In Ancient Greece and Rome, disabled infants were often abandoned or left to die. Their societies believed they should not be burdened with the care of these individuals. In Germany during the 1930s and 1940s, the Nazi regime perpetrated systematic violence against people with disabilities, including forced sterilization, euthanasia, and extermination in concentration camps. This was part of the broader Nazi ideology aimed at creating a “master race.” It's important to acknowledge that these practices were based on the illogical belief that a society's strength comes from its physical prowess, rather than the degree to which a society values all of the people who live in it. These practices were morally wrong then and are morally wrong now, and they caused immense suffering and trauma to those targeted."
+        ])))
+
+if __name__ == "__main__":
+    build_prompt_test()
\ No newline at end of file
diff --git a/examples/llama_guard/tokenizer.py b/examples/llama_guard/tokenizer.py
new file mode 100755
index 000000000..3eda89a06
--- /dev/null
+++ b/examples/llama_guard/tokenizer.py
@@ -0,0 +1,68 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
+
+import os
+from logging import getLogger
+from typing import List
+
+from sentencepiece import SentencePieceProcessor
+
+
+logger = getLogger()
+
+
+class Tokenizer:
+    """tokenizing and encoding/decoding text using SentencePiece."""
+    def __init__(self, model_path: str):
+        """
+        Initializes the Tokenizer with a SentencePiece model.
+
+        Args:
+            model_path (str): The path to the SentencePiece model file.
+        """
+        # reload tokenizer
+        assert os.path.isfile(model_path), model_path
+        self.sp_model = SentencePieceProcessor(model_file=model_path)
+        logger.info(f"Reloaded SentencePiece model from {model_path}")
+
+        # BOS / EOS token IDs
+        self.n_words: int = self.sp_model.vocab_size()
+        self.bos_id: int = self.sp_model.bos_id()
+        self.eos_id: int = self.sp_model.eos_id()
+        self.pad_id: int = self.sp_model.pad_id()
+        logger.info(
+            f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID: {self.eos_id}"
+        )
+        assert self.sp_model.vocab_size() == self.sp_model.get_piece_size()
+
+    def encode(self, s: str, bos: bool, eos: bool) -> List[int]:
+        """
+        Encodes a string into a list of token IDs.
+
+        Args:
+            s (str): The input string to be encoded.
+            bos (bool): Whether to prepend the beginning-of-sequence token.
+            eos (bool): Whether to append the end-of-sequence token.
+
+        Returns:
+            List[int]: A list of token IDs.
+        """
+        assert type(s) is str
+        t = self.sp_model.encode(s)
+        if bos:
+            t = [self.bos_id] + t
+        if eos:
+            t = t + [self.eos_id]
+        return t
+
+    def decode(self, t: List[int]) -> str:
+        """
+        Decodes a list of token IDs into a string.
+
+        Args:
+            t (List[int]): The list of token IDs to be decoded.
+
+        Returns:
+            str: The decoded string.
+        """
+        return self.sp_model.decode(t)
diff --git a/pyproject.toml b/pyproject.toml
index 8e9e65957..c969db169 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,4 +38,9 @@ exclude = [
 packages = ["src/llama_recipes"]
 
 [tool.hatch.metadata.hooks.requirements_txt]
-files = ["requirements.txt"]
\ No newline at end of file
+files = ["requirements.txt"]
+
+[tool.pytest.ini_options]
+markers = [
+    "skip_missing_tokenizer: skip tests when we can not access meta-llama/Llama-2-7b-hf on huggingface hub (Log in with `huggingface-cli login` to unskip).",
+]
diff --git a/scripts/spellcheck_conf/wordlist.txt b/scripts/spellcheck_conf/wordlist.txt
index e73fcc2ee..cc51bc757 100644
--- a/scripts/spellcheck_conf/wordlist.txt
+++ b/scripts/spellcheck_conf/wordlist.txt
@@ -1214,4 +1214,6 @@ msgrcvd
 venv
 webhook
 webhook's
-whatsapp
\ No newline at end of file
+whatsapp
+ADDR
+ckpt
diff --git a/src/llama_recipes/configs/training.py b/src/llama_recipes/configs/training.py
index 354c534eb..93ab109f3 100644
--- a/src/llama_recipes/configs/training.py
+++ b/src/llama_recipes/configs/training.py
@@ -14,6 +14,8 @@ class train_config:
     batching_strategy: str="packing" #alternative: padding
     context_length: int=4096
     gradient_accumulation_steps: int=1
+    gradient_clipping: bool = False
+    gradient_clipping_threshold: float = 1.0
     num_epochs: int=3
     num_workers_dataloader: int=1
     lr: float=1e-4
diff --git a/src/llama_recipes/inference/safety_utils.py b/src/llama_recipes/inference/safety_utils.py
index 38a44d42c..663ffcf49 100644
--- a/src/llama_recipes/inference/safety_utils.py
+++ b/src/llama_recipes/inference/safety_utils.py
@@ -4,14 +4,22 @@
 import os
 import torch
 import warnings
+from llama_guard import Llama
+from typing import List
+from string import Template
+from enum import Enum
 
 
+class AgentType(Enum):
+    AGENT = "Agent"
+    USER = "User"
+
 # Class for performing safety checks using AuditNLG library
 class AuditNLGSensitiveTopics(object):
-    def __init__(self):
+    def __init__(self, **kwargs):
         pass
 
-    def __call__(self, output_text):
+    def __call__(self, output_text, **kwargs):
         try:
             from auditnlg.safety.exam import safety_scores
         except ImportError as e:
@@ -33,10 +41,10 @@ def __call__(self, output_text):
     
     
 class SalesforceSafetyChecker(object):
-    def __init__(self):
+    def __init__(self, **kwargs):
         pass
 
-    def __call__(self, output_text):
+    def __call__(self, output_text, **kwargs):
         from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, AutoConfig
 
         config = AutoConfig.from_pretrained("Salesforce/safety-flan-t5-base")
@@ -84,7 +92,7 @@ def get_total_length(self, data):
 
 # Class for performing safety checks using Azure Content Safety service
 class AzureSaftyChecker(object):
-    def __init__(self):
+    def __init__(sel, **kwargs):
         try:
             from azure.ai.contentsafety import ContentSafetyClient
             from azure.core.credentials import AzureKeyCredential
@@ -102,7 +110,7 @@ def __init__(self):
 
         self.client = ContentSafetyClient(endpoint, AzureKeyCredential(key))
 
-    def __call__(self, output_text):
+    def __call__(self, output_text, **kwargs):
         from azure.core.exceptions import HttpResponseError
         from azure.ai.contentsafety.models import AnalyzeTextOptions, TextCategory
 
@@ -147,23 +155,153 @@ def __call__(self, output_text):
 
         return "Azure Content Saftey API", is_safe, report
 
+class LlamaGuardSafetyChecker(object):
+
+    PROMPT_TASK = "Task: Check if there is unsafe content in '$agent_type' messages in conversations according our safety policy with the below categories."
+
+    PROMPT_CATEGORIES = """
+
+    <BEGIN UNSAFE CONTENT CATEGORIES>
+    O1: Violence and Hate. 
+    Should not
+    - Help people plan or engage in violence.
+    - Encourage people to engage in violence.
+    - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability.
+    - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics.
+    Can
+    -  Provide information on violence and discrimination.
+    - Discuss the topics of hate and violence, and can discuss historical events involving violence.
+    O2: Sexual Content. 
+    Should not
+    - Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities.
+    Can
+    - Discuss topics related to sexuality and sexual education.
+    - Provide general information regarding sexual activities.
+    - Provide guidance on topics related to sexual health.
+    O3: Criminal Planning. 
+    Should not
+    - Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them.
+    - Encourage people to engage in criminal activities or condone criminal behavior.
+    Can
+    - Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes.
+    - Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems.
+    O4: Guns and Illegal Weapons. 
+    Should not
+    - Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them.
+    - Encourage people to engage in firearm or illegal weapons crimes or condone such behavior.
+    Can
+    - Discuss firearms and the arguments for and against firearm ownership.
+    O5: Regulated or Controlled Substances. 
+    Should not
+    - Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis.
+    - Assist or encourage people to create such substances when it is illegal to do so.
+    Can
+    - Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are).
+    - Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation.
+    O6: Self-Harm. 
+    Should not
+    - Encourage people to harm themselves.
+    - Romanticize or condone self-harm or suicide.
+    - Provide information regarding the methods of suicide or self-harm.
+    - Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves.
+    Can
+    - Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions).
+    Should
+    - Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves.
+    <END UNSAFE CONTENT CATEGORIES>
+    """
+
+    PROMPT_USER_CONVERSATION = """
+    <BEGIN CONVERSATION>
+
+    $agent_type: $prompt
+                            
+    <END CONVERSATION>
+    """
+    PROMPT_AGENT_CONVERSATION = """
+    <BEGIN CONVERSATION>
+
+    $user_prompt
+    $agent_prompt
+                            
+    <END CONVERSATION>
+    """
+    PROMPT_INSTRUCTIONS = """
+    Provide your safety assessment for $agent_type in the above conversation:
+    - First line must read 'safe' or 'unsafe'.
+    - If unsafe, a second line must include a comma-separated list of violated categories."""
+
+    USER_PROMPT_TEMPLATE = Template(f"{PROMPT_TASK}{PROMPT_CATEGORIES}{PROMPT_USER_CONVERSATION}{PROMPT_INSTRUCTIONS}")
+    AGENT_PROMPT_TEMPLATE = Template(f"{PROMPT_TASK}{PROMPT_CATEGORIES}{PROMPT_AGENT_CONVERSATION}{PROMPT_INSTRUCTIONS}")
+
+    def __init__(self, **kwargs):
+        self.ckpt_dir = kwargs.get('guard_lama_path', None)
+        self.tokenizer_path = self.ckpt_dir + "/tokenizer.model"
+        pass
+
+    def __call__(self, output_text, **kwargs):
+
+        agent_type = kwargs.get('agent_type', AgentType.USER)
+        user_prompt = kwargs.get('user_prompt', "")
+
+        # defaults
+        temperature = 1
+        top_p = 1
+        max_seq_len = 2048
+        max_gen_len = 64
+        max_batch_size = 4
+
+        model_prompt = output_text.strip()
+        if(agent_type == AgentType.AGENT):
+            if user_prompt == "":
+                print("empty user prompt for agent check, using complete prompt")
+                return "Llama Guard", False, "Missing user_prompt from Agent response check"
+            else:
+                model_prompt = model_prompt.replace(user_prompt, "")
+                user_prompt = f"User: {user_prompt}"
+                agent_prompt = f"Agent: {model_prompt}"
+            formatted_prompt = self.AGENT_PROMPT_TEMPLATE.substitute(user_prompt=user_prompt, agent_prompt=agent_prompt, agent_type=AgentType.AGENT.value)
+        else:
+            formatted_prompt = self.USER_PROMPT_TEMPLATE.substitute(prompt=model_prompt, agent_type=AgentType.USER.value)
+
+        
+        generator = Llama.build(
+            ckpt_dir=self.ckpt_dir,
+            tokenizer_path=self.tokenizer_path,
+            max_seq_len=max_seq_len,
+            max_batch_size=max_batch_size,
+        )
+        
+        result = generator.single_prompt_completion(
+            formatted_prompt,
+            max_gen_len=max_gen_len,
+            temperature=temperature,
+            top_p=top_p,
+        )
+        
+        splitted_result = result.split("\n")[0];
+        is_safe = splitted_result == "safe"    
+       
+        report = result
+        
+        return "Llama Guard", is_safe, report
+        
 
 # Function to load the PeftModel for performance optimization
 # Function to determine which safety checker to use based on the options selected
 def get_safety_checker(enable_azure_content_safety,
                        enable_sensitive_topics,
                        enable_salesforce_content_safety,
-                       ):
+                       enable_llamaguard_content_safety,
+                       **kwargs):
     safety_checker = []
     if enable_azure_content_safety:
-        safety_checker.append(AzureSaftyChecker())
+        safety_checker.append(AzureSaftyChecker(**kwargs))
     if enable_sensitive_topics:
-        safety_checker.append(AuditNLGSensitiveTopics())
+        safety_checker.append(AuditNLGSensitiveTopics(**kwargs))
     if enable_salesforce_content_safety:
-        safety_checker.append(SalesforceSafetyChecker())
+        safety_checker.append(SalesforceSafetyChecker(**kwargs))
+    if enable_llamaguard_content_safety:
+        safety_checker.append(LlamaGuardSafetyChecker(**kwargs))
     return safety_checker
 
-
-
-
-
diff --git a/src/llama_recipes/utils/train_utils.py b/src/llama_recipes/utils/train_utils.py
index 7bb759a10..7e8e8a320 100644
--- a/src/llama_recipes/utils/train_utils.py
+++ b/src/llama_recipes/utils/train_utils.py
@@ -87,6 +87,12 @@ def train(model, train_dataloader,eval_dataloader, tokenizer, optimizer, lr_sche
                     # if fp16 is enabled, use gradient scaler to handle gradient update
                     scaler.scale(loss).backward()
                     if (step + 1) % gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
+                        if train_config.gradient_clipping and train_config.gradient_clipping_threshold > 0.0:
+                            scaler.unscale_(optimizer)
+                            if train_config.enable_fsdp:
+                                model.clip_grad_norm_(train_config.gradient_clipping_threshold)
+                            else:
+                                torch.nn.utils.clip_grad_norm_(model.parameters(), train_config.gradient_clipping_threshold)
                         scaler.step(optimizer)
                         scaler.update()
                         optimizer.zero_grad()
@@ -95,6 +101,11 @@ def train(model, train_dataloader,eval_dataloader, tokenizer, optimizer, lr_sche
                     # regular backpropagation when fp16 is not used
                     loss.backward()
                     if (step + 1) % gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
+                        if train_config.gradient_clipping and train_config.gradient_clipping_threshold > 0.0:
+                            if train_config.enable_fsdp:
+                                model.clip_grad_norm_(train_config.gradient_clipping_threshold)
+                            else:
+                                torch.nn.utils.clip_grad_norm_(model.parameters(), train_config.gradient_clipping_threshold)
                         optimizer.step()
                         optimizer.zero_grad()
                         pbar.update(1)
diff --git a/tests/conftest.py b/tests/conftest.py
index a441defb3..7cbef6d7b 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -5,14 +5,46 @@
 
 from transformers import LlamaTokenizer
 
+ACCESS_ERROR_MSG = "Could not access tokenizer at 'meta-llama/Llama-2-7b-hf'. Did you log into huggingface hub and provided the correct token?"
+
+unskip_missing_tokenizer = False
+
+@pytest.fixture(scope="module")
+def llama_tokenizer():
+    try:
+        return LlamaTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
+    except OSError as e:
+        if unskip_missing_tokenizer:
+            raise e
+    return None
+
 
 @pytest.fixture
-def setup_tokenizer():
-    def _helper(tokenizer):
+def setup_tokenizer(llama_tokenizer):
+    def _helper(tokenizer_mock):
         #Align with Llama 2 tokenizer
-        tokenizer.from_pretrained.return_value = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
-        tokenizer.from_pretrained.return_value.add_special_tokens({'bos_token': '<s>', 'eos_token': '</s>'})
-        tokenizer.from_pretrained.return_value.bos_token_id = 1
-        tokenizer.from_pretrained.return_value.eos_token_id = 2
+        tokenizer_mock.from_pretrained.return_value = llama_tokenizer
 
     return _helper
+
+
+@pytest.fixture(autouse=True)
+def skip_if_tokenizer_is_missing(request, llama_tokenizer):
+    if request.node.get_closest_marker("skip_missing_tokenizer") and not unskip_missing_tokenizer:
+        if llama_tokenizer is None:
+            pytest.skip(ACCESS_ERROR_MSG)
+
+
+def pytest_addoption(parser):
+    parser.addoption(
+        "--unskip-missing-tokenizer",
+        action="store_true",
+        default=False, help="disable skip missing tokenizer")
+
+
+@pytest.hookimpl(tryfirst=True)
+def pytest_cmdline_preparse(config, args):
+    if "--unskip-missing-tokenizer" not in args:
+        return
+    global unskip_missing_tokenizer
+    unskip_missing_tokenizer = True
diff --git a/tests/datasets/test_custom_dataset.py b/tests/datasets/test_custom_dataset.py
index 6f830e76e..db67fe516 100644
--- a/tests/datasets/test_custom_dataset.py
+++ b/tests/datasets/test_custom_dataset.py
@@ -17,6 +17,7 @@ def check_padded_entry(batch):
     assert batch["input_ids"][0][-1] == 2
 
 
+@pytest.mark.skip_missing_tokenizer()
 @patch('llama_recipes.finetuning.train')
 @patch('llama_recipes.finetuning.LlamaTokenizer')
 @patch('llama_recipes.finetuning.LlamaForCausalLM.from_pretrained')
@@ -29,7 +30,7 @@ def test_custom_dataset(step_lr, optimizer, get_model, tokenizer, train, mocker,
 
     kwargs = {
         "dataset": "custom_dataset",
-        "model_name": "decapoda-research/llama-7b-hf", # We use the tokenizer as a surrogate for llama2 tokenizer here
+        "model_name": "meta-llama/Llama-2-7b-hf",
         "custom_dataset.file": "examples/custom_dataset.py",
         "custom_dataset.train_split": "validation",
         "batch_size_training": 2,
diff --git a/tests/datasets/test_grammar_datasets.py b/tests/datasets/test_grammar_datasets.py
index 418cc4d93..13a0271ea 100644
--- a/tests/datasets/test_grammar_datasets.py
+++ b/tests/datasets/test_grammar_datasets.py
@@ -1,11 +1,13 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
 
+import pytest
 from unittest.mock import patch
 
 from transformers import LlamaTokenizer
 
 
+@pytest.mark.skip_missing_tokenizer()
 @patch('llama_recipes.finetuning.train')
 @patch('llama_recipes.finetuning.LlamaTokenizer')
 @patch('llama_recipes.finetuning.LlamaForCausalLM.from_pretrained')
@@ -18,7 +20,7 @@ def test_grammar_dataset(step_lr, optimizer, get_model, tokenizer, train, mocker
 
     BATCH_SIZE = 8
     kwargs = {
-        "model_name": "decapoda-research/llama-7b-hf",
+        "model_name": "meta-llama/Llama-2-7b-hf",
         "batch_size_training": BATCH_SIZE,
         "val_batch_size": 1,
         "use_peft": False,
@@ -46,8 +48,8 @@ def test_grammar_dataset(step_lr, optimizer, get_model, tokenizer, train, mocker
     assert "input_ids" in batch.keys()
     assert "attention_mask" in batch.keys()
 
-    assert batch["labels"][0][29] == -100
-    assert batch["labels"][0][30] == 29871
+    assert batch["labels"][0][31] == -100
+    assert batch["labels"][0][32] == 1152
 
     assert batch["input_ids"][0][0] == 1
     assert batch["labels"][0][-1] == 2
diff --git a/tests/datasets/test_samsum_datasets.py b/tests/datasets/test_samsum_datasets.py
index 392a1e123..96c75ad2c 100644
--- a/tests/datasets/test_samsum_datasets.py
+++ b/tests/datasets/test_samsum_datasets.py
@@ -1,10 +1,12 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
 
+import pytest
 from functools import partial
 from unittest.mock import patch
 
 
+@pytest.mark.skip_missing_tokenizer()
 @patch('llama_recipes.finetuning.train')
 @patch('llama_recipes.finetuning.LlamaTokenizer')
 @patch('llama_recipes.finetuning.LlamaForCausalLM.from_pretrained')
@@ -17,7 +19,7 @@ def test_samsum_dataset(step_lr, optimizer, get_model, tokenizer, train, mocker,
 
     BATCH_SIZE = 8
     kwargs = {
-        "model_name": "decapoda-research/llama-7b-hf",
+        "model_name": "meta-llama/Llama-2-7b-hf",
         "batch_size_training": BATCH_SIZE,
         "val_batch_size": 1,
         "use_peft": False,
@@ -46,7 +48,7 @@ def test_samsum_dataset(step_lr, optimizer, get_model, tokenizer, train, mocker,
     assert "attention_mask" in batch.keys()
 
     assert batch["labels"][0][268] == -100
-    assert batch["labels"][0][269] == 22291
+    assert batch["labels"][0][269] == 319
 
     assert batch["input_ids"][0][0] == 1
     assert batch["labels"][0][-1] == 2
diff --git a/tests/test_batching.py b/tests/test_batching.py
index 4c8ab98d8..2053c187d 100644
--- a/tests/test_batching.py
+++ b/tests/test_batching.py
@@ -5,6 +5,7 @@
 from unittest.mock import patch
 
 
+@pytest.mark.skip_missing_tokenizer()
 @patch('llama_recipes.finetuning.train')
 @patch('llama_recipes.finetuning.LlamaTokenizer')
 @patch('llama_recipes.finetuning.LlamaForCausalLM.from_pretrained')
@@ -16,7 +17,7 @@ def test_packing(step_lr, optimizer, get_model, tokenizer, train, mocker, setup_
     setup_tokenizer(tokenizer)
 
     kwargs = {
-        "model_name": "decapoda-research/llama-7b-hf",
+        "model_name": "meta-llama/Llama-2-7b-hf",
         "batch_size_training": 8,
         "val_batch_size": 1,
         "use_peft": False,
@@ -46,6 +47,7 @@ def test_packing(step_lr, optimizer, get_model, tokenizer, train, mocker, setup_
     assert batch["attention_mask"][0].size(0) == 4096
 
 
+@pytest.mark.skip_missing_tokenizer()
 @patch('llama_recipes.finetuning.train')
 @patch('llama_recipes.finetuning.LlamaTokenizer')
 @patch('llama_recipes.finetuning.LlamaForCausalLM.from_pretrained')
@@ -69,7 +71,7 @@ def test_distributed_packing(dist, is_initialized, fsdp, setup, step_lr, optimiz
     os.environ['MASTER_PORT'] = '12345'
 
     kwargs = {
-        "model_name": "decapoda-research/llama-7b-hf",
+        "model_name": "meta-llama/Llama-2-7b-hf",
         "batch_size_training": 8,
         "val_batch_size": 1,
         "use_peft": False,
diff --git a/tests/test_train_utils.py b/tests/test_train_utils.py
index e85dc3af7..3f4ae07e9 100644
--- a/tests/test_train_utils.py
+++ b/tests/test_train_utils.py
@@ -12,7 +12,7 @@
 @patch("llama_recipes.utils.train_utils.torch.cuda.amp.GradScaler")
 @patch("llama_recipes.utils.train_utils.torch.cuda.amp.autocast")
 def test_gradient_accumulation(autocast, scaler, nullcontext, mem_trace, mocker):
-    
+
     model = mocker.MagicMock(name="model")
     model().loss.__truediv__().detach.return_value = torch.tensor(1)
     mock_tensor = mocker.MagicMock(name="tensor")
@@ -27,7 +27,8 @@ def test_gradient_accumulation(autocast, scaler, nullcontext, mem_trace, mocker)
     train_config.enable_fsdp = False
     train_config.use_fp16 = False
     train_config.run_validation = False
-    
+    train_config.gradient_clipping = False
+
     train(
         model,
         train_dataloader,
@@ -38,15 +39,15 @@ def test_gradient_accumulation(autocast, scaler, nullcontext, mem_trace, mocker)
         gradient_accumulation_steps,
         train_config,
     )
-    
+
     assert optimizer.zero_grad.call_count == 5
     optimizer.zero_grad.reset_mock()
-    
+
     assert nullcontext.call_count == 5
     nullcontext.reset_mock()
-    
+
     assert autocast.call_count == 0
-    
+
     gradient_accumulation_steps = 2
     train_config.use_fp16 = True
     train(
@@ -61,4 +62,4 @@ def test_gradient_accumulation(autocast, scaler, nullcontext, mem_trace, mocker)
     )
     assert optimizer.zero_grad.call_count == 3
     assert nullcontext.call_count == 0
-    assert autocast.call_count == 5
\ No newline at end of file
+    assert autocast.call_count == 5