How to use Llama 3.1 with LangChain

Data Science in your pocket
24 Jul 202403:44

TLDRThis tutorial demonstrates how to integrate Meta's Llama 3.1 model with LangChain for building AI applications. Llama 3.1, an open-source model, is shown to be powerful, outperforming other models like GPT-4 and CLOT 3.5. The presenter guides through the installation of necessary libraries, setting up a Hugging Face token, and creating a pipeline with the 8B instruct model. The script also covers wrapping the pipeline with LangChain's functionalities for a basic application, ending with a live test of the model's capabilities in generating information about a cricketer. The presenter also promotes a new book on building generative applications with LMs.

Takeaways

  • 😀 Llama 3.1 is the latest model by Meta, and it has been claimed to be very powerful, outperforming other models like JP4 and CLOT 3.5 Sonet on various tasks.
  • 📚 Llama 3.1 is open-source, making it accessible for developers and researchers to experiment with and integrate into their projects.
  • 🛠️ To use Llama 3.1 with LangChain, you need to install or upgrade the Transformers library and set up a Hugging Face token, which is free to generate.
  • 🔍 It's important to use the 'instruct' version of the model for tasks like question answering, as opposed to the base version which is designed for text completion.
  • 📝 When setting up the model in Google Colab, ensure to provide the correct model ID for Llama 3.1, such as 'meta/llama-13b-hf' for the 13B instruct model.
  • ⚙️ Create a Transformers pipeline with a specified maximum length to avoid default limitations, which is crucial for applications that require more than the default 20 tokens.
  • 🔄 Import necessary functionalities from LangChain, including the Hugging Face pipeline, prompt template, and LLM chain, to build your application.
  • 🔑 Wrap the Transformer pipeline with the Hugging Face pipeline from LangChain to create a prompt template and pass it to the LLM chain for processing.
  • 🗣️ Demonstrated in the script is a basic application using a prompt to generate information about an entity, showcasing the model's ability to provide detailed responses.
  • 📚 The speaker promotes a new book titled 'LangChain in Your Pocket: Beginners to Building Gen Applications Using LM', which has become a bestseller on Amazon.
  • 📌 The book provides guidance for beginners interested in building generative applications using large language models like Llama 3.1 and is available for purchase.

Q & A

  • What is Llama 3.1 and what is its significance?

    -Llama 3.1 is the latest model by Meta, which has been claimed to be very powerful and has already beaten other models like GPT-4 and CLOT 3.5 on various tasks. Its significance lies in its open-source nature, making it accessible for developers to build and test applications.

  • What are the prerequisites for using Llama 3.1 with LangChain?

    -To use Llama 3.1 with LangChain, you first need to install or upgrade the Transformers library, set up your Hugging Face token, and ensure you are using the 'instruct' version of the model, as these are text completion models suitable for question-answering systems.

  • Why is it important to use the 'instruct' version of the model?

    -The 'instruct' version of the model is important because it is designed for text completion tasks, which are essential for question-answering systems. The base version of the model may not be as effective for this purpose.

  • How do you set up the Hugging Face token for using Llama 3.1?

    -To set up the Hugging Face token, you can follow the instructions provided in previous videos by the same author, which guide you on how to generate and use the token for saving and loading Hugging Face models.

  • What is the model ID for the 8B instruct model of Llama 3.1?

    -The model ID for the 8B instruct model of Llama 3.1 is 'meta/llama-13b-hyper'.

  • Why is it necessary to provide the max length when creating a Transformers pipeline?

    -Providing the max length is necessary because by default, the max length is set to 20 tokens. If you do not specify a higher range, your application will be limited to outputting only 20 tokens, which may not be sufficient for your needs.

  • What is the role of the LangChain in building applications with Llama 3.1?

    -LangChain provides the necessary functionalities to wrap the Transformer pipeline, create prompt templates, and integrate with the LLM (Language Model) chain. This allows developers to build more complex applications by leveraging the power of Llama 3.1.

  • Can you provide an example of how to use Llama 3.1 with LangChain for a basic application?

    -An example would be to create a Transformers pipeline with the Llama 3.1 model ID, wrap it using the Hugging Face pipeline from LangChain, create a prompt template, and pass it to the LLM chain. Then, you can call the chain object with a prompt to generate text, such as 'tell me about entity' where 'entity' is an input variable.

  • What is the size of the Llama 3.1 8B instruct model and does it require a powerful system to run?

    -The Llama 3.1 8B instruct model is approximately 5GB in size. It should be manageable on a local system as well as on Google Colab, but it's always recommended to have a system with sufficient resources to handle large models efficiently.

  • Is there a book available that can guide beginners on building generative applications using LMs?

    -Yes, there is a book titled 'LangChain in Your Pocket: Beginners to Building Generative Applications Using LMs' by the same author, which is available on Amazon and has become a bestseller, providing a comprehensive guide for beginners.

Outlines

00:00

🤖 Introduction to Using Meta's Lama 3.1 with Lang Chain

The speaker introduces the video by stating the purpose: to demonstrate how to use Meta's latest model, Lama 3.1, in conjunction with Lang Chain to build generalized AI applications. Lama 3.1 is highlighted as a powerful, open-source model that has outperformed other models like GPT-4 and CLOT 3.5 on various tasks. The speaker shares their positive experience with the model and encourages viewers to try it out, starting with a basic language application. The setup process involves installing or upgrading the Transformers library, obtaining a Hugging Face token, and selecting the appropriate model ID for text completion tasks, specifically recommending the 8B instruct model due to its suitability for question-answering systems.

Mindmap

Keywords

💡Llama 3.1

Llama 3.1 is the latest AI model developed by Meta. It is noted for its powerful capabilities, surpassing other models like JP4 and CLOT 3.5 Sonet in various tasks. The model is open-sourced, which makes it accessible for developers and researchers to experiment with and integrate into their applications. In the video, the speaker demonstrates how to use Llama 3.1 with LangChain, showcasing its potential in building advanced language applications.

💡LangChain

LangChain is a platform designed to facilitate the integration of AI models into applications. It provides tools and frameworks that help in building language-based applications. In the context of the video, LangChain is used to work with Llama 3.1, allowing the creation of a pipeline that can be utilized for text generation and other language tasks.

💡Transformers

Transformers is a popular library in the field of AI, particularly in natural language processing. It provides pre-trained models and tools for tasks like language translation, text generation, and more. The script mentions installing or upgrading the Transformers library to work with Llama 3.1, emphasizing the importance of having the right version to leverage the capabilities of the new model.

💡Hugging Face

Hugging Face is a company that offers a platform for developers to build, share, and discover the latest in machine learning models. They provide a token system for accessing their models, which is mentioned in the script as a requirement for setting up the environment to use Llama 3.1. The token is essential for accessing and loading models from Hugging Face's platform.

💡Model ID

A Model ID is a unique identifier for a specific AI model. In the script, the speaker uses the Model ID 'metah llama for/ metah llama Hy 3.1 hy8 8B hyper construct' to specify the Llama 3.1 model. This ID is crucial for correctly loading and utilizing the model within the application.

💡Instruct Model

The term 'Instruct Model' refers to AI models that have been trained to follow instructions provided in the input text. These models are designed to understand and execute tasks based on the instructions given. The script emphasizes the importance of using the 'instruct' version of the model, as opposed to the base version, for tasks like question answering.

💡Text Generation

Text generation is a process where AI models create new text based on a given input or prompt. In the video, the speaker demonstrates how Llama 3.1 can be used for text generation by creating a pipeline with a specified maximum length. This is important for controlling the output length and ensuring the model generates relevant responses.

💡Prompt Template

A prompt template is a predefined set of instructions or questions that guide the AI model in generating text or performing a task. In the script, the speaker uses a prompt template to instruct the Llama 3.1 model to provide information about a specific entity, demonstrating how the model can be directed to generate relevant content.

💡LLM Chain

LLM Chain, in the context of the video, refers to a chain or sequence of operations involving the language model (LLM). The speaker wraps the Hugging Face pipeline with a LangChain function to create an LLM chain, which is then used to execute tasks like text generation based on the provided prompt.

💡Entity

In the script, 'entity' is used as an example input variable in the prompt template. The speaker asks the Llama 3.1 model to provide information about 'VAT Kohli,' an Indian cricketer, using the entity as a variable in the prompt. This demonstrates how the model can be used to generate information about specific subjects or entities.

💡LangChain Book

The LangChain book mentioned in the script is a resource for beginners looking to build generative applications using language models. The speaker promotes the book, which is available on Amazon and has become a bestseller, indicating its popularity and usefulness in the field of AI and language applications.

Highlights

Llama 3.1 is the latest model by Meta and is claimed to be very powerful.

Llama 3.1 has beaten JP4 and CLOT 3.5 Sonet on various tasks.

Llama 3.1 is open-sourced.

The demonstration will cover how to use Llama 3.1 with LangChain to build generative AI applications.

To start, install Transformer langin langin Community or upgrade the existing Transformers version.

Set up a Hugging Face token, which is free to generate.

Provide the model ID, using the instruct version of models for better performance in text completion.

Create a Transformers pipeline with the model ID, float type, and max length.

The 8B instruct model is recommended for its balance between size and capability.

Download the model, which is about 5GB for the 8B instruct model.

Import necessary functionalities from LangChain such as hugging face pipeline, prompt template, and llm chain.

Wrap the Transformer pipeline using the Hugging Face pipeline from LangChain.

Create a prompt template and pass it to the llm chain.

The llm chain object is used to execute the text generation.

An example prompt is provided to demonstrate the text generation process.

The output shows the generated text based on the input prompt.

The speaker recommends testing Llama 3.1 due to its power and open-source nature.

A new book titled 'LangChain in Your Pocket: Beginners to Building Gen Applications Using LM' is mentioned.

The book is available on Amazon and is a bestseller.

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