Run Llama 3.1 locally using LangChain

Code With Aarohi
23 Jul 202410:19

TLDRIn this tutorial, Ari demonstrates how to run the Llama 3.1 model locally using LangChain and Olama. The video covers downloading the model from Hugging Face, setting up an environment with Python 3.10, and installing necessary packages. It then guides through creating a Streamlit app that uses the Llama 3.1 model to answer user queries interactively. The process includes writing the app code, modifying it for Streamlit, and running the app to get responses from the model in real-time.

Takeaways

  • 😀 Llama 3.1 is the latest release by Meta, available in three sizes: 8 billion, 70 billion, and 405 billion parameter models.
  • 🔍 To test the 405 billion parameter model, log in with an Instagram or Facebook account on Meta's DOI platform.
  • 📚 The Llama 3.1 model can be downloaded from the Hugging Face page by searching for 'meta' and selecting the desired model variant.
  • 📝 Users must accept the license agreement to use the model, and the approval status can be checked on the settings page.
  • 🛠️ To use Llama 3.1 locally, create an Anaconda environment with Python 3.10 and install the required packages: LangChain, LangChain Olama, and Streamlit.
  • 🔄 LangChain is a framework that allows the use of various language models, and Olama is a package specifically for using models from the LangChain ecosystem.
  • 📥 The 'olama pull llama 3.1' command is used to download the desired Llama 3.1 model variant.
  • 💬 A basic script is provided to demonstrate how to integrate Llama 3.1 with LangChain and Streamlit for a chat application.
  • 📝 The script includes a prompt template for user interaction, where the user asks a question and the model provides an answer.
  • 🖥️ The final step is to modify the script to create a Streamlit app, allowing users to input questions and receive answers from the model in a user-friendly interface.
  • 🌐 The video tutorial also mentions the possibility of using Llama 3.1 with MongoDB by following specific installation and connection steps.

Q & A

  • What are the different sizes of the Llama 3.1 model?

    -The Llama 3.1 model comes in three different sizes: 8 billion parameters, 70 billion parameters, and 405 billion parameters.

  • How can you test the 405 billion parameter Llama 3.1 model?

    -You can test the 405 billion parameter Llama 3.1 model on Meta's platform by logging in with your Instagram or Facebook account.

  • Where can you download the Llama 3.1 model?

    -You can download the Llama 3.1 model from the Hugging Face page by searching for 'meta' and selecting the desired model.

  • What is the first step to running the Llama 3.1 model locally using LangChain?

    -The first step is to open Anaconda prompt and create an environment using the command for Python 3.10.

  • Which packages are required to run Llama 3.1 with LangChain and OLama?

    -The required packages are LangChain, LangChain-OLama, and Streamlit.

  • How do you install the required packages for running Llama 3.1?

    -You install the required packages by navigating to the folder containing the 'requirements.txt' file and running the command 'pip install -r requirements.txt'.

  • How do you download the Llama 3.1 model using OLama?

    -You download the model by running the command 'olama pull llama 3.1'.

  • What is the purpose of the 'chat prompt template' in the LangChain application?

    -The 'chat prompt template' defines how the interaction between the user and the model will occur, setting up the format for user questions and model responses.

  • How do you run the Streamlit app for Llama 3.1?

    -You run the Streamlit app by saving the code and executing the command 'streamlit run app_name' in the command prompt.

  • How can you modify the code to make it a Streamlit app?

    -You modify the code by using Streamlit functions to create a title, input box for user questions, and output area to display model responses.

Outlines

00:00

📚 Introduction to Llama 3.1 Models

In this introductory segment, the host, Ari, welcomes viewers to the channel and dives into the topic of Llama 3.1, a recent release by Meta. Ari explains that Llama 3.1 is available in three sizes: an 8 billion parameter model, a 70 billion parameter model, and a 405 billion parameter model. Viewers are guided on how to access the 405 billion parameter model through Meta's AI platform by logging in with social media accounts. Additionally, Ari provides instructions on downloading the Llama 3.1 model from Hugging Face by searching for 'meta' and selecting the desired model, followed by accepting the license agreement. The approval process and how to check the status are also discussed.

05:03

💻 Setting Up Llama 3.1 with Lang Chain and Streamlit

This paragraph focuses on the technical setup required to use the Llama 3.1 model on local machines. Ari demonstrates how to create a Python environment using Anaconda and install necessary packages such as Lang Chain and Streamlit. The process involves creating an environment with Python 3.10, activating it, and installing the required packages from a GitHub repository using a requirements.txt file. Ari also shows how to download the Llama 3.1 model using the 'oLlama pull llama 3.1' command. The segment concludes with a basic script for creating an app using Lang Chain Streamlit, where the user's question is passed to the Llama 3.1 model and the model's response is printed. Modifications to the code are suggested to transform it into a Streamlit app, allowing users to input questions and receive answers through the app interface.

10:05

🌐 Using Llama 3.1 in a Streamlit Application

In the final paragraph, Ari walks through the process of modifying the basic script to create a fully functional Streamlit application. The code is adjusted to include a title for the app, a prompt template for user interaction, and a mechanism to capture user input. Ari demonstrates how to run the app, which now prompts users to enter their questions and displays the answers generated by the Llama 3.1 model. The video script ends with a live demonstration of the app in action, answering questions and translating text into Hindi. Ari also mentions alternative ways to download the Llama 3.1 model from Hugging Face or Meta's official site, and encourages viewers to like, share, and subscribe to the channel.

Mindmap

Keywords

💡Llama 3.1

Llama 3.1 is the latest release by Meta, available in three sizes: 8 billion, 70 billion, and 405 billion parameter models. It is a significant update in the Llama series of language models, enhancing the capabilities of natural language processing tasks. In the video, the speaker discusses how to download and use these models locally.

💡Meta

Meta, formerly known as Facebook, is the company behind the Llama 3.1 model. They provide the infrastructure and access to these advanced language models. The video mentions that the largest 405 billion parameter model can be tested on Meta's platform after logging in with an Instagram or Facebook account.

💡Hugging Face

Hugging Face is a popular platform for sharing and utilizing machine learning models. The video guides viewers on how to search for and download the Llama 3.1 models from Hugging Face after accepting the license agreement. It is an essential step for setting up the model locally.

💡LangChain

LangChain is a framework used for building applications with language models. In the video, LangChain is used to integrate and run the Llama 3.1 model locally. It helps streamline the process of connecting different components like the model and user interface.

💡Anaconda

Anaconda is a distribution of Python and R programming languages for scientific computing. The video shows how to create and manage a virtual environment using Anaconda to set up the required dependencies for running the Llama 3.1 model with LangChain.

💡Streamlit

Streamlit is an open-source app framework used for creating web applications in Python. The video demonstrates how to build a user interface for the Llama 3.1 model using Streamlit, allowing users to interact with the model through a web app.

💡requirements.txt

The requirements.txt file lists all the dependencies needed for a Python project. In the video, it is used to install necessary packages like LangChain and Streamlit. The speaker explains how to use pip to install these requirements from the file.

💡olama

oLlama is a package that facilitates the use of various large language models, including Llama 3.1. The video highlights the importance of the LangChain oLlama module for integrating the Llama 3.1 model in a local environment.

💡virtual environment

A virtual environment is an isolated environment for Python projects, allowing specific dependencies to be installed without affecting the global Python installation. The video demonstrates creating and activating a virtual environment to run the Llama 3.1 model setup.

💡prompt template

A prompt template defines the structure and flow of interaction in a language model application. The video shows how to create and use a prompt template in LangChain to manage user queries and model responses effectively.

Highlights

Introduction to Llama 3.1, the latest release by Meta.

Llama 3.1 comes in three different sizes: 8 billion, 70 billion, and 405 billion parameter models.

Instructions on how to test the 405 billion parameter model using Meta's DOIA platform.

Guide on where to download the Llama 3.1 model from Hugging Face.

Explanation of the license agreement acceptance process for using the Llama 3.1 model.

Demonstration of using Llama 3.1 locally with LangChain and Olama.

Step-by-step guide on creating an environment in Anaconda for Llama 3.1.

List of requirements needed for running Llama 3.1 locally, including LangChain and Olama packages.

How to install the necessary packages using pip and a requirements.txt file.

Command to download the Llama 3.1 model using Olama.

Basic script for using Llama 3.1 with LangChain and a chat prompt template.

Creating a chain to process user questions and retrieve answers from the Llama 3.1 model.

Running the initial code to test the Llama 3.1 model's response to a question.

Modification of the code to integrate with a Streamlit app for user interaction.

Instructions on running the Streamlit app and user input handling.

Example of the Llama 3.1 model translating text into Hindi using the Streamlit app.

Demonstration of using LangChain with MongoDB, including installation and connection steps.

Conclusion summarizing the process of using Llama 3.1 with LangChain and additional resources.