Master the art of building Llama 2 chatbots

Table of Contents

1. Introduction
2. About Llama2
3. Features of Llama2
4. Building Your Own Llama2 Chatbot
5. Requirements
6. App Overview
7. Generating Your Own Replicate API Token
8. Code Walkthrough
   • Authentication Mechanism for API Token
   • Chat History and Clearing Function
   • Generating Llama2 LOM Model Response
   • User Prompt Input
   • Revolving Loop for Model Response
9. Customization with Llama2 Pro
10. Conclusion
11. Resources

Building Your Own Llama2 Chatbot in Python using Streamlit and Replicate

In this article, we will explore how to build your own Llama2 chatbot in Python using Streamlit and Replicate. Llama2 is an open-source large language model introduced by Meta AI, and it is available for both research and commercial usage. We will walk through the process step-by-step and explain the code involved. So, without further ado, let's dive in.

Introduction

Llama2 is a powerful large language model that was released on July 18th. It is designed to democratize access to language models and ensure responsible use of AI. In this tutorial, we will leverage the capabilities of Llama2 to build a chatbot using Python and Streamlit.

About Llama2

Llama2 outperforms other open-source large language models in reasoning, coding proficiency, and knowledge tests. It has been trained on a vast amount of data, totaling two trillion tokens, which includes over one million new human annotations and fine-tuning for chat completions. Llama2 comes in three sizes: 7 billion parameters, 13 billion parameters, and 70 billion parameters, all available on the Replicate platform.

Features of Llama2

1. High-performance: Llama2 outperforms other open-source models in various language tasks.
2. Large Context length: Llama2 supports a context length of up to 4096 tokens.
3. Transparent and responsible usage: Llama2 follows responsible AI guidelines and ensures transparent usage.
4. Available in multiple sizes: Llama2 offers three different sizes, providing flexibility Based on your requirements.
5. Permissive license: Llama2's version 2 has a more permissive license, allowing both research and commercial usage.

Building Your Own Llama2 Chatbot

To build your own Llama2 chatbot, you will need the following:

1. Replicate API token: You can generate your own API token by signing up on the Replicate platform and following the provided instructions.
2. Python libraries: You will need Streamlit and Replicate, which can be installed via the requirements.txt file.

Once you have the necessary tools, you can proceed to Create your app. The app will consist of an input box for the API token, a chat input box for user Prompts, and a display area for the chat history and generated responses.

Requirements

Make sure you have the following Python libraries installed:

• Streamlit: A low-code web framework for creating interactive web apps.
• Replicate: A library that allows you to access and use the Llama2 language model.

To install these libraries, refer to the requirements.txt file provided in the code repository.

App Overview

The Llama2 chatbot app consists of two versions: Standard and Pro. The Standard Version offers basic functionality, while the Pro Version provides additional customization options. Both versions use Streamlit as the underlying web framework.

The app allows users to enter their Replicate API token and prompt messages. The input is then used to make an API call to Replicate, which generates a response using the Llama2 model. The generated response is displayed in the app.

Generating Your Own Replicate API Token

To generate your own Replicate API token, follow these steps:

1. Sign in to the Replicate platform using your GitHub account.
2. Go to the user settings and click on "API token."
3. Create a new token by providing a name and clicking on "Create token."
4. Copy the generated token. Note that you may be provided with a trial key initially, but you will eventually need to link your credit card information for continued usage.

Once you have the API token, you can proceed with integrating it into your app.

Code Walkthrough

The code for the Llama2 chatbot app spans approximately 68 lines. Let's walk through the key sections:

Authentication Mechanism for API Token

The code includes an authentication mechanism for the API token. It checks whether the token is present in the secrets (environment variables) or if the user needs to provide it.

Chat History and Clearing Function

The app keeps track of the chat history using session state. It allows users to clear the chat history with the "Clear Chat History" button. The chat history is displayed using Streamlit's st.chat and st.write functions.

Generating Llama2 LOM Model Response

The code includes a custom function for generating the Llama2 LOM model response. It uses the replicate.run function, specified with the desired Llama2 model size (e.g., 13 billion parameters). The function takes the user's prompt message as input and generates a response. The generated response is printed out token by token and then displayed in the app.

User Prompt Input

The app includes an input box where users can enter their prompt messages. The prompt message is stored in the session state and added to the chat history. The code differentiates between user and assistant messages using the "row" parameter.

Revolving Loop for Model Response

The code includes a revolving loop to generate the model response when a new question is asked. It iterates through the chat history and generates responses based on the user's input. The responses are saved to the chat history and displayed in the app.

Customization with Llama2 Pro

Llama2 Pro offers additional customization options. It allows users to select different versions of Llama2 and customize the model parameters using a slider. The Pro Version provides more flexibility for those who want to fine-tune their chatbot.

Conclusion

In this tutorial, we learned how to build a Llama2 chatbot using Python, Streamlit, and Replicate. Llama2 is a powerful language model that can be easily integrated into your own applications. With the increasing importance of AI-driven chatbots, leveraging tools like Llama2 can enhance user experiences and provide valuable services. Feel free to explore the resources provided in the article to further enrich your understanding and build your own incredible app.

Resources

• Demo App: Llama2 Chatbot
• Blog Article: Building Your Own Llama2 Chatbot
• Replicate Platform: Official Website
• Llama2 Release Article: Link to Article
• Data Professor YouTube Channel: Llama2 Quick Video

Highlights

• Build your own Llama2 chatbot using Python and Streamlit.
• Llama2 is an open-source large language model released by Meta AI.
• Llama2 outperforms other open-source models in reasoning, coding proficiency, and knowledge tests.
• Customize your chatbot using Llama2 Pro.
• Generate your own Replicate API token for authentication.
• The app uses Streamlit for the web interface and Replicate to access the Llama2 model.
• Code walkthrough explaining the authentication mechanism, chat history, model response generation, and user prompt input.
• Revolving loop to generate model responses for user questions.
• Additional resources for further exploration and understanding.

FAQ

Q: Can I use Llama2 for commercial purposes? A: Yes, Llama2 has a permissive license that allows both research and commercial usage.

Q: How do I generate my own Replicate API token? A: You can generate your API token by signing up on the Replicate platform and following the provided instructions.

Q: Are there different sizes of Llama2 models available? A: Yes, Llama2 comes in three sizes: 7 billion parameters, 13 billion parameters, and 70 billion parameters.

Q: Is it possible to clear the chat history in the app? A: Yes, the app provides a "Clear Chat History" function that allows users to delete the chat history.

Q: Can I customize the Llama2 model parameters? A: Llama2 Pro offers customization options, allowing you to select different versions of Llama2 and customize the model parameters using a slider.