Get Started with Mistral 7B Locally in 6 Minutes

Developers Digest
2 Oct 202306:42

TLDRIn this informative video, the creator introduces the Mistal AI model, highlighting its superior performance compared to other models like L 270b and Lama 23b. The video offers a step-by-step guide on how to set up and interact with the Mistal model locally using a new project called 'ol.a', as well as how to leverage Hugging Face's hosted inference API. Additionally, the creator demonstrates how to integrate the model into Lang chain for Python or Node.js developers and points to Perplexity Labs for a fast implementation experience. The video concludes with a GitHub repo for those interested in implementing the model in their projects.

Takeaways

  • 🚀 The video introduces the Mistal AI, a new model that outperforms other models like L 270b and Llama 23b.
  • 💻 The presenter will demonstrate how to set up and use Mistal AI both locally and on Hugging Face.
  • 📋 The video will also show how to incorporate Mistal AI into Lang chain, a tool for Python or Node.js developers.
  • 🔍 A resource for trying out the Mistal 7B model without downloading anything will be provided.
  • 📚 The presenter will not delve into the specific metrics but will provide links for further exploration.
  • 📂 The setup process for Mistal AI locally involves a simple command in the terminal after installing a specific package.
  • 🔗 Hugging Face's hosted inference API allows for text generation with Mistal AI.
  • 🔄 Lang chain enables easy model switching for different AI models in a local application.
  • 📈 The presenter will provide a GitHub repo with code examples for implementing Mistal AI in Node.js projects.
  • 💡 The video script mentions that Mistal AI can run efficiently on systems that are a few years old, not just on the latest hardware.
  • 📹 The video ends with a call to action for viewers to like, comment, share, and subscribe, as well as consider Patreon support.

Q & A

  • What is the main focus of the video?

    -The video focuses on demonstrating how to get started with the Mistal AI model, both locally and using Hugging Face, and how to incorporate it into Lang chain.

  • What is unique about the Mistal 7B model?

    -The Mistal 7B model is unique because it outperforms other models like L 270b and even the 13B variant of Llama 2, as indicated by its better performance metrics.

  • To set up the model locally, the video suggests using a new project called 'ol.a', which simplifies the process by allowing users to download, install, and choose which models to download.

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  • Is the 'ol.a' project available for Windows?

    -As of the video, 'ol.a' is not available for Windows, but it can be downloaded for both Mac and Linux.

  • How does one interact with the Mistal model using the terminal?

    -After setting up 'ol.a', users can interact with the Mistal model in the terminal by running a simple command, which allows for a chat-like interaction with the model.

  • What is the advantage of using an inference server with the 'ol.a' project?

    -The advantage of using an inference server is that it allows users to make requests to the model running in the background, enabling the querying of different models on the fly without needing to install additional dependencies.

  • How can one use Hugging Face's hosted inference API?

    -Users can play around with text generation using Hugging Face's hosted inference API directly from their browser, without the need to set up anything locally.

  • What is Lang chain and why is it recommended for developers?

    -Lang chain is a tool recommended for Python or Node.js developers as it simplifies the process of setting up and using AI models within their projects, leveraging the ecosystem and other tools available.

  • How can one try out the Mistal 7B model without downloading anything?

    -Perplexity Labs offers a platform where users can try out the Mistal 7B model, as well as other models like Llama, without the need to download anything, benefiting from fast implementations.

  • What is the minimum system requirement for running the Mistal model?

    -The video demonstrates that the Mistal model can be run on systems a couple of years old, such as an Intel-based Mac with 16GB of RAM, though newer computers will likely perform better.

  • How can one implement the Mistal model in Node.js projects without dependencies?

    -The video shows a method using a simple fetch request in Node.js to log out streaming responses from the Mistal model, which can be done without any additional dependencies.

Outlines

00:00

🚀 Introduction to MistaI AI and Setup

The video begins with an introduction to MistaI AI, a new model that has gained attention for its performance, surpassing even the 13B variant of LLaMA 2. The host plans to demonstrate how to set up MistaI locally and explore its capabilities on Hugging Face. They will also guide viewers on how to incorporate MistaI into Lang chain and provide a resource for trying out the model without any downloads.

05:02

🛠️ Local Setup and Hugging Face Integration

The host explains the process of setting up MistaI locally using a new project called 'ol.a', which simplifies the installation and model selection. They mention the availability of the model for Mac and Linux and provide instructions on how to download, install, and run the model. The video also covers how to interact with MistaI via terminal and use it as an inference server. Additionally, the host discusses the ability to query different models on the fly and shows how to use Hugging Face's hosted inference API for text generation.

🔗 Lang Chain Demonstration and No-Dependency Usage

The host proceeds to demonstrate how to use Lang chain, a tool recommended for Python or Node.js developers, to set up and interact with MistaI. They explain the ease of specifying the model within Lang chain and show a live demo of the process. The video also addresses the performance of the model on older systems and provides a simple method to use MistaI without dependencies through a fetch request in Node.js. The host concludes by offering a GitHub repo for those interested in implementing the model in their projects.

Mindmap

Keywords

💡MistaI AI

MistaI AI refers to an advanced artificial intelligence model developed by a company that has recently gained significant attention and funding. In the video, the presenter highlights the model's superior performance compared to other models like L 270b and Lama 23b, indicating its potential for various applications. The video aims to demonstrate how to set up and interact with the MistaI AI model, both locally and through hosted services.

💡Hugging Face

Hugging Face is a platform that provides tools and resources for developers working with AI models, particularly in the field of natural language processing. In the context of the video, the presenter shows how to use Hugging Face's hosted inference API to interact with AI models like MistaI without the need for local setup. This platform allows users to experiment with AI models in a user-friendly environment.

💡Lang Chain

Lang Chain is a tool or library that simplifies the process of integrating AI models into Python or Node.js applications. It provides a straightforward way for developers to set up and use AI models within their projects. The video encourages the use of Lang Chain for its ease of use and compatibility with other tools in the ecosystem.

💡Inference Server

An inference server is a system that runs AI models and provides predictions or outputs based on input data. In the video, the presenter mentions that the AI model can be set up as an inference server, allowing for requests to be made to the model from different applications. This setup enables the model to be queried on the fly, making it a flexible solution for AI integration.

💡Perplexity Labs

Perplexity Labs is a resource mentioned in the video that offers fast implementations of AI models, including MistaI and Llama models. It provides a platform for users to try out these models without the need for local installation or setup, making it accessible for quick experimentation and testing.

💡Model Performance

Model performance refers to the effectiveness and efficiency of an AI model in completing its tasks, such as text generation or understanding. In the video, the presenter compares the performance of MistaI AI with other models, emphasizing its superior capabilities. This is important for users looking to implement AI models in their projects, as it helps them choose the most suitable model for their needs.

💡Local Setup

Local setup refers to the process of installing and configuring AI models on a user's own computer or server. The video provides instructions on how to set up AI models like MistaI locally, which allows for more control and customization but requires technical knowledge and resources.

💡Text Completion

Text completion is a feature of AI models where the model generates text to complete a given input or prompt. In the video, the presenter demonstrates how to use the text completion model of MistaI AI, which can be interacted with like a chatbot, providing users with a conversational interface for AI interactions.

💡Streaming Response

A streaming response is a continuous flow of data or output from an AI model as it processes input. This is useful for applications that require real-time interaction with the AI. In the video, the presenter shows how to get a streaming response from the AI model, which can be leveraged for more dynamic and interactive applications.

💡GitHub Repo

A GitHub repository (repo) is a collection of files and folders that are used to store and manage a project's code. In the video, the presenter mentions spinning up a GitHub repo to share code snippets for implementing AI models like MistaI in Node.js projects. This provides a resource for developers to access and build upon the shared code.

Highlights

The video demonstrates how to quickly get started with Mistal AI, a new model by Mr AI that outperforms other models like L 270b and Lama 23b.

Mistal AI's 7B model was recently released and has shown better performance than the 13B variant of Llama.

The video provides a guide on setting up Mistal AI locally using a new project called ol.a.

ol.a simplifies the process of setting up a model locally by allowing users to download, install, and choose models through a terminal command.

The video shows how to interact with Mistal AI through the terminal, similar to a chat GPT.

An inference server is set up out of the box, allowing users to make requests to different models installed on their system.

The video also covers how to use Hugging Face's hosted inference API for text generation with Mistal AI.

Lang chain is recommended for Python or Node.js developers to easily set up and use Mistal AI within their projects.

Perplexity Labs is highlighted as a resource for trying out Mistal 7B and other models without any setup, offering fast implementations.

The video creator provides a GitHub repo with code snippets for implementing Mistal AI in Node.js projects.

Lang chain allows for streaming responses, which can be leveraged for real-time interactions with the AI model.

The video creator's setup on an Intel-based Mac with 16GB RAM shows that Mistal AI can be used on systems that are a few years old.

A simple fetch request in Node.js is demonstrated to show how to use Mistal AI without any dependencies.

The video encourages viewers to like, comment, share, and subscribe, as well as consider becoming a Patreon subscriber.

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