Run Mistral, Llama2 and Others Privately At Home with Ollama AI - EASY!

Jim's Garage
19 Dec 202312:45

TLDRIn this video from Jim's Garage, the host introduces viewers to a straightforward method for self-hosting AI instances privately using Ollama AI. The video outlines two options: a command line interface for Linux and a user-friendly web interface resembling chat GPT. The host demonstrates setting up a virtual machine with ample resources to run multiple models, emphasizing that while an Nvidia GPU is recommended for performance, the CPU is sufficient for most users. The tutorial guides through installing Ollama, selecting and running various large language models, and interacting with them via command line. Additionally, the video covers deploying a Docker container for a more visual experience, allowing users to choose and run models through a web interface, ensuring all data remains local and private. The host concludes by encouraging viewers to experiment with different models and share their experiences.

Takeaways

  • 🤖 **Self-hosting AI**: The video introduces a method to self-host AI instances privately, keeping data local and addressing privacy concerns.
  • 🚀 **Ollama AI**: Ollama is the engine used to run large language models privately, offering a command line interface and a web-based user-friendly interface.
  • 📡 **Local Infrastructure**: You can run AI models on your own infrastructure, which means your requests, queries, and responses stay local without being sent to external servers.
  • 💻 **Linux and Docker**: The video covers how to install and run Ollama on Linux using a convenience script and also discusses a Docker setup.
  • 🧠 **Large Language Models**: Users can choose from various large language models, such as Mistral and Mixol, to run privately on their systems.
  • 💾 **Hardware Requirements**: The video mentions that a minimum of 8GB of RAM is recommended for running these models, with more CPU cores and an Nvidia GPU for better performance.
  • 🔍 **GitHub Repository**: Detailed instructions and scripts for installation are available on Ollama's GitHub, which viewers can access for setup guidance.
  • 📦 **Docker Containers**: Docker is used to simplify the deployment of the AI models, allowing for an all-in-one solution or separate hosting of components.
  • 🔗 **API Connectivity**: The installed models can be served through an API, enabling remote connections and interactions with the AI models.
  • 📈 **Performance Monitoring**: The video demonstrates how to monitor system performance while the AI models are running, giving insight into resource usage.
  • 🌐 **Web Interface**: Ollama provides a web interface that resembles popular chat platforms, allowing for an intuitive interaction with the AI models.
  • 🔒 **Data Privacy**: A key benefit highlighted is the privacy aspect of running AI models locally, ensuring that user data is not sent outside of their network.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is about self-hosting AI instances like Mistral, Llama2, and others privately at home using Ollama AI, which allows users to run large language models on their own infrastructure without sending data to external servers.

  • Why is running AI instances privately beneficial?

    -Running AI instances privately is beneficial because it keeps all requests, queries, responses, and data local, addressing privacy concerns and preventing the need to send personal data to external servers for data farming.

  • What are the two options presented in the video for running AI instances?

    -The two options presented are a simple command line interface that runs in Linux and a more user-friendly web-based interface that resembles chat GBT.

  • What is Ollama AI and what does it do?

    -Ollama AI is an engine that runs all of the large language models. It allows users to choose and run different large language models privately on their infrastructure.

  • What are the system requirements for running these AI instances?

    -The system requirements include a minimum of 8 GB of RAM, though more is better. A CPU is required, and having an Nvidia GPU can significantly improve performance. However, support for AMD and Intel may be implemented in the future.

  • How can users install and run AI instances using the command line?

    -Users can install and run AI instances using a convenience script provided by Ollama AI. They can copy and paste the script into their terminal, and after the installation process, they can use the 'ollama run' command to pull and install new large language models.

  • What is the process for deploying the AI instances using Docker?

    -The process involves building the Docker image locally, which includes cloning the repository, copying files into a folder, and editing the docker-compose file if necessary. Then, running 'docker compose up -d' to deploy the containers, which may take time due to image downloading and the build process.

  • How can users interact with the AI instances through the web UI?

    -Users can interact with the AI instances through the web UI by navigating to the Docker IP with the specified port (default is 3000). They can select a model, set it as default, and then ask questions or request actions from the AI.

  • What is the advantage of using a web-based interface for AI instances?

    -The web-based interface provides a more user-friendly and familiar experience, similar to chat GBT, allowing users to interact with AI instances through a graphical interface rather than a command line.

  • How does the video demonstrate the performance and resource usage of the AI instance?

    -The video demonstrates performance by showing the AI generating a Kubernetes manifest file for Python and monitoring system performance in real-time within the hypervisor, noting the CPU and memory usage.

  • What are the privacy implications of running AI instances on external servers like chat GPT?

    -Running AI instances on external servers can lead to privacy concerns as personal data and queries are sent to these servers, potentially being used for data farming or other purposes without the user's consent.

  • What is the future outlook on the development of AI models like those discussed in the video?

    -The future outlook is that these models will continue to evolve and mature, potentially reaching or surpassing the capabilities of current leading models like chat GPT. There is excitement around how these models will develop and the impact they will have on privacy and functionality.

Outlines

00:00

😀 Introduction to Self-Hosting AI Instances

The video begins with the host, Jim, welcoming viewers to his garage and addressing the arrival of AI. He introduces the concept of self-hosting AI instances to maintain privacy and control over data. Jim outlines two options: a command-line interface for Linux and a web browser-based interface resembling chat GPT. He mentions using 'Ola', the engine for large language models, and provides a step-by-step guide to downloading and installing it on Linux. The video also covers setting up a virtual machine with significant hardware resources to host multiple models, emphasizing the potential performance benefits of having an Nvidia GPU. Jim demonstrates installing and running a language model through the command line and highlights the importance of choosing a model that matches the system's hardware capabilities.

05:02

🤖 Running AI Models through Docker

The second paragraph focuses on deploying AI models using Docker for a more user-friendly experience. Jim guides viewers on how to set up Docker, build the Docker image, and run it on a local machine. He explains the process of pulling and installing new language models from the Ola website, selecting the 'dolphin' model for its smaller size and uncensored nature. The video demonstrates running the model through the command line and then transitioning to a Docker setup for a more visually appealing interface. Jim details the steps to clone the repository, configure the Docker Compose file, and build the Docker image. He also discusses the potential for hosting the web UI on a separate server and the option to enable GPU acceleration if an Nvidia GPU is available. Finally, he shows how to access the web UI, select a model, and interact with it through a web browser.

10:04

📈 Local AI Model Deployment and Performance

In the final paragraph, Jim demonstrates the local deployment and performance monitoring of an AI model. He guides viewers on how to download and set a model as default within the web UI, asking the model a question to showcase its capabilities. The video highlights the importance of being aware of AI's potential for errors and emphasizes the local nature of the setup, which ensures data privacy. Jim then requests the model to generate a Kubernetes manifest file, which it successfully does, while he monitors the system's resource usage. He points out the significant resource demands of running an AI model, even a smaller one, and extrapolates the requirements for larger models. The video concludes with a reflection on the maturity of the technology compared to platforms like chat GPT and an invitation for viewers to share their experiences and thoughts in the comments section.

Mindmap

Keywords

💡AI overlords

The term 'AI overlords' is used in the video to refer to the dominant role that artificial intelligence systems play in our lives. It implies that AI has a significant influence over various aspects of society, potentially raising concerns about privacy and control. In the context of the video, the creator is addressing the fact that AI technologies are here to stay and suggests ways to use them privately to maintain control over personal data.

💡self-hosting AI instances

Self-hosting AI instances refers to the process of running AI models on one's own infrastructure, rather than relying on cloud-based services. This approach allows for greater control over data privacy and can be tailored to specific needs. The video provides a guide on how to set up and run large language models privately, ensuring that user data remains local and is not sent to external servers.

💡Ollama AI

Ollama AI, as mentioned in the video, is the engine that powers various large language models. It serves as the backbone for the AI systems that the video creator demonstrates how to run privately. By using Ollama AI, users can access and interact with different AI models without having to rely on third-party platforms, which may have privacy concerns.

💡command line interface

A command line interface (CLI) is a way of interacting with computer systems where users issue commands in a text-based format. In the context of the video, the CLI is one of the methods presented for interacting with the self-hosted AI models. It is a more technical approach that allows users to execute commands directly in the terminal to manage and communicate with the AI.

💡user-friendly experience

A user-friendly experience refers to the design and functionality of a system or application that is easy and intuitive to use, often with a graphical interface. In the video, the creator contrasts the command line interface with a more visually appealing and easy-to-navigate web-based interface, which mimics the look and feel of chatbots like Chat GPT, making it more accessible to a wider audience.

💡Docker

Docker is a platform that enables developers to create, deploy, and run applications in containers. Containers are lightweight, portable, and self-sufficient, allowing the same application to run on any system regardless of its operating system. In the video, Docker is used to simplify the deployment of the AI models and their associated services, making it easier to set up and manage the local AI environment.

💡virtual machine

A virtual machine (VM) is a software emulation of a physical computer that can execute programs like a real machine. VMs allow for the creation of multiple virtual systems on a single physical host, each with its own operating system and resources. In the video, the creator sets up a VM with significant hardware resources to run multiple AI models, demonstrating the flexibility and scalability of virtualization technology.

💡Nvidia GPU

Nvidia GPUs (Graphics Processing Units) are specialized hardware designed for handling complex图形 and parallel computations. They have become essential in AI and machine learning tasks due to their ability to accelerate processing and improve performance. In the context of the video, the creator mentions that using an Nvidia GPU can significantly enhance the performance of running AI models, although the demonstration is CPU-only due to the lack of GPU hardware.

💡large language models

Large language models (LLMs) are AI systems designed to process and generate human-like text based on the input they receive. These models are trained on vast amounts of data to understand and produce text in a way that can be useful for various applications, such as chatbots, text summarization, and content creation. The video focuses on self-hosting different LLMs and provides instructions on how to install and run them locally.

💡data privacy

Data privacy refers to the protection of personal and sensitive information from unauthorized access, use, or disclosure. It is a critical concern in the age of digital technology, especially when dealing with AI systems that may collect and process user data. The video addresses this issue by presenting a solution for running AI models locally, ensuring that user data stays private and is not sent to external servers.

Highlights

The video introduces a method to self-host AI instances privately, avoiding data privacy concerns.

Ollama AI is the engine that powers large language models and can be used to run them privately.

Two options are presented: a command line interface and a user-friendly web interface similar to Chat GPT.

The video focuses on deploying AI models on Linux, with Windows and Docker support coming soon.

A virtual machine with 32GB of memory, 20 cores, and 50GB of storage is recommended for running multiple models.

Nvidia GPUs can significantly improve performance, but the current setup only works with Nvidia hardware.

The installation process is simplified with a convenience script available on the Ollama website.

After installation, users can pull and install new large language models using the 'Al LL run' command.

The video demonstrates running the Dolphin 2.1 model, which is small and uncensored.

A Docker setup is available for a more user-friendly experience with a graphical interface.

The Docker image can be built locally and configured to use GPU acceleration if available.

The web UI allows users to select and run different models, with the ability to set a default model.

The video shows creating a Kubernetes manifest file using the AI, demonstrating its coding capabilities.

System performance can be monitored while the AI is running, showing the resource demands of the models.

The AI setup is designed to stay within the user's network, ensuring data privacy and local processing.

The video encourages users to experiment with the AI and compare its performance with services like Chat GPT.

The video concludes by highlighting the potential for AI models to evolve and improve over time.