Run Mistral, Llama2 and Others Privately At Home with Ollama AI - EASY!
TLDRIn this video, the host guides viewers on how to self-host AI instances privately using Ollama AI, allowing users to run large language models on their own infrastructure. The process ensures data privacy as it remains local. Two options are presented: a simple command line interface for Linux and a user-friendly web interface resembling chat GPT. The video demonstrates setting up a virtual machine with sufficient resources, installing Ollama AI, and running various language models, including Mistral and Dolphin. It also covers Docker deployment for a more accessible interface. The host emphasizes the importance of choosing a model that matches the user's hardware capabilities and highlights the potential performance improvements with an Nvidia GPU. The summary concludes with the host's enthusiasm about the future of AI and inviting viewers to share their experiences.
Takeaways
- ๐ **Self-host AI Instances**: The video introduces a method to run large language models privately on your own infrastructure, keeping data local and addressing privacy concerns.
- ๐ป **Two Options Presented**: The host demonstrates both a command line interface for Linux and a user-friendly web interface similar to chat GPT.
- ๐ **Ollama AI Engine**: Ollama is the engine that powers large language models, and the video shows how to use it for self-hosting.
- ๐ฅ **Easy Installation**: The process includes downloading and installing Ollama via a convenience script, with options for Linux and upcoming support for Windows and Docker.
- ๐ง **Powerful Virtual Machine**: A virtual machine with significant resources (32GB RAM, 20 cores, 50GB HD) is recommended for running multiple models, although the minimum requirement is 8GB RAM.
- ๐ฎ **GPU Acceleration**: Nvidia GPU support is available for performance improvements, with potential future support for AMD and Intel.
- ๐ **Command Line Interaction**: The video shows how to interact with language models through the Linux command line using a convenience script.
- ๐ **Model Selection**: Ollama's website allows users to choose from various supported models, including the recently popular Mistral 7B model.
- ๐ก **API and Remote Access**: The installed system can serve models through an API, enabling remote connections which is useful for Docker installations.
- ๐ **Dolphin Model Example**: The host chooses the Dolphin model for its small size and uncensored responses for demonstration purposes.
- ๐ **Monitoring Performance**: The video highlights the ability to monitor system performance while the AI model is running, giving insight into resource usage.
- ๐ **Local Deployment**: All interactions and data processing happen locally, ensuring that user data stays within the user's network.
Q & A
What is the main topic of the video?
-The main topic of the video is about self-hosting AI instances privately, using a tool called Ollama AI, to run large language models without sending data to external servers.
What are the two options presented in the video for interacting with AI instances?
-The two options presented are a simple command line interface that runs in Linux, and a more user-friendly web interface that resembles chat GPT.
What is the name of the engine used to run all the large language models in the video?
-The engine used to run all the large language models is called Ollama.
What are the minimum hardware recommendations for running Ollama AI?
-The minimum hardware recommendations include 8 GB of RAM and more CPU cores for better performance. However, for running larger models, more resources like 32 GB of memory and multiple cores are advised.
How does the video demonstrate installing Ollama AI on a virtual machine?
-The video demonstrates installing Ollama AI on a virtual machine by using a convenience script in the Linux terminal and following the instructions on Ollama's website.
What is the significance of having an Nvidia GPU for running AI models?
-Having an Nvidia GPU can lead to significant performance improvements when running AI models, although the video notes that support for Nvidia GPUs is currently the only option, with potential support for AMD and Intel in the future.
How does the video show adding a new large language model to Ollama AI?
-The video shows adding a new large language model by using the 'AI LL run' command followed by the model name, such as 'AI LL run mistol' to download and install the Mistol model.
What is the purpose of Docker in the video?
-Docker is used in the video to set up a more user-friendly web interface for interacting with the AI models, by building and running a Docker container that includes both the Ollama AI agent and the web UI.
How is the web UI accessed in the video?
-The web UI is accessed by navigating to the virtual machine's IP address on port 3000 in a web browser, after the Docker container has been set up and is running.
What is the benefit of running AI models locally using Ollama AI?
-The benefit of running AI models locally is that it keeps all data and queries private, without having to send them to external servers, thus safeguarding user privacy.
What is the potential for future development of the large language models featured in the video?
-The video suggests that while some models may not yet match the capabilities of chat GPT 4, it is exciting to see how they evolve, and there is potential for them to mature and possibly overtake existing models in the future.
Outlines
๐ค Self-Hosting AI Instances for Privacy
The video introduces the concept of self-hosting AI instances to maintain privacy. It discusses the ability to run large language models privately on one's own infrastructure, keeping data local and avoiding concerns related to data farming. The host outlines two options: a command-line interface for Linux and a web-based GUI resembling chat GPT. The video also covers using 'Ola', the engine for large language models, and provides instructions for downloading and installing it. The host demonstrates setting up a virtual machine with substantial resources to run multiple models and emphasizes the potential for performance gains with an Nvidia GPU.
๐ Running AI Models via Command Line and Docker
The host demonstrates how to interact with large language models through a Linux command line using a convenience script. After installing Ola, the host shows how to pull and install new language models from the Ola website. The host chooses a smaller model due to internet speed and hardware limitations. The video then transitions to a Docker setup, explaining the process of building a Docker image locally and running both the Ola agent and the web UI in the same container. The host guides viewers through cloning the repository, setting up the Docker compose file, and deploying the service. The video concludes with accessing the web UI, selecting a model, and interacting with it through the browser.
๐ Local AI Model Deployment and Performance Monitoring
The video concludes with the host showing how to download and set a default model using the web UI. It demonstrates the process of asking the AI a question and generating a Kubernetes manifest file for Python. The host emphasizes the importance of understanding that AI models can make mistakes. Additionally, the video shows the system's resource usage while the AI model is running, highlighting the model's demand on CPU and memory. The host wraps up by encouraging viewers to try out self-hosting AI models for privacy and to share their experiences.
Mindmap
Keywords
๐กAI overlords
๐กSelf-hosting AI instances
๐กOllama AI
๐กLarge language models (LLMs)
๐กPrivacy concerns
๐กCommand line interface (CLI)
๐กDocker
๐กNvidia GPU
๐กVirtual machine
๐กWeb UI
๐กLocal infrastructure
Highlights
Jim's Garage video introduces a simple way to self-host AI instances privately on your own infrastructure.
Privacy is maintained as data stays local without being sent to external servers.
Two options are presented: a command line interface for Linux and a user-friendly web interface resembling chat GPT.
Ollama AI is used as the engine to run large language models.
The video provides easy-to-follow instructions for downloading and installing on Linux.
A Windows version and Docker support are mentioned as upcoming features.
A virtual machine with 32GB of memory, 20 cores, and 50GB of storage is recommended for optimal performance.
Nvidia GPU support is available for significant performance improvements.
The command line interface allows for model selection and execution of commands.
Models like Mistral and Mixol can be installed and run via the command line.
The Dolphin 2.1 model is demonstrated for its small size and uncensored responses.
Docker is used to set up a more user-friendly interface that can run locally.
The Docker setup allows for local deployment of the AI model, agent, and GUI.
The web UI provides a familiar interface for interacting with the hosted AI model.
Models can be selected and set as default for use within the web interface.
Performance monitoring is possible through the hypervisor while the AI model runs.
An example of generating a Kubernetes manifest file is given.
All interactions and data remain local, enhancing privacy and security.
The video concludes with a discussion on the potential future development of these AI models.