Llama 2 Chat Application 🦙💬

In this project, we will build a Llama 2 chatbot in Python using Streamlit for the frontend, while leveraging the Llama 2 model hosted on Replicate for the backend.

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Table of Contents 📑

1. Introduction
2. What is Llama 2?
3. Key Features of Llama 2
4. App Overview
5. Getting Started
   • 1. Get a Replicate API token
   • 2. Set up the coding environment
   • 3. Build the app
   • 4. Set the API token
   • 5. Deploy the app
6. Try It Out
7. Wrapping Up
8. References

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Introduction

This guide walks you through building a chatbot using Llama 2, an open-source large language model by Meta. Our app will have a Streamlit interface, and we’ll call the Replicate API to communicate with the Llama 2 backend for generating responses to user inputs.

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What is Llama 2?

Llama 2 is Meta’s open-source language model, released on July 18, 2023. Meta has made the model free for research and commercial use while promoting transparent and responsible AI through their Responsible Use Guide.

Key Features of Llama 2:

• Performance: Outperforms other open-source LLMs in reasoning, coding, and knowledge benchmarks.
• Training Data: Trained on 2 trillion tokens, nearly double the data of version 1.
• Human Annotations: Includes over 1 million human annotations for fine-tuning chat completions.
• Model Sizes: Available in three sizes: 7B, 13B, and 70B parameters.
• Longer Context: Supports up to 4096 tokens for extended context understanding.
• Commercial License: Offers a more permissive license, allowing commercial use.

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App Overview 🛠️

The Llama 2 Chat Application allows users to interact with the Llama 2 model via a simple frontend. The key components are:

1. User Input: Users provide a Replicate API token and a message prompt.
2. Backend: The app makes API calls to Replicate, which processes the prompt and returns an AI-generated response.
3. Response Display: The chatbot displays the response in the app interface.

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Getting Started 🚀

Follow these steps to build your Llama 2 chat app:

1. Get a Replicate API Token

• Visit Replicate to create an account and obtain a free API token.

2. Set Up the Coding Environment

• Ensure you have Python and Streamlit installed.
• Install the necessary libraries by creating a requirements.txt file and running:

  pip install -r requirements.txt

3. Build the App

• Write the Python code for your Streamlit app. The app will take inputs from the user (API token and prompt), send them to Replicate, and display the response.

4. Set the API Token

• Add a section to your code to request the Replicate API token if not already provided.

5. Deploy the App

• Use Streamlit Cloud or another hosting platform to deploy your app online.

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Try It Out 🧪

Ready to test the app? Follow these simple steps:

1. Visit the live app at: Llama 2 Chat Assistance
2. Sign up for a free API key on Replicate.
3. Enter your Replicate API token when prompted by the app.
4. Type your prompt into the chatbox and enjoy the experience!

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Wrapping Up 🎉

Congratulations on building and trying out your Llama 2 Chat Application! Whether you're a developer or just curious, I hope you enjoyed this project. In this app, we used the 7B version of Llama 2, and the model parameters (such as temperature and top_p) were set to arbitrary values.

Feel free to experiment with these settings to see how they affect the AI's responses. You can even upgrade to the Pro version, where you can specify the model and fine-tune parameters for deeper customization.

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References 📚

• Streamlit
• Llama 2 Chat App Tutorial
• Replicate