Run your own AI (but private)
TLDRThis video explores the setup and benefits of running a private AI model on your personal computer, emphasizing data privacy and security. It demonstrates how to quickly install and use AI models like Llama 2, available for free on platforms like Hugging Face. The video also highlights VMware's role in enabling private AI within company data centers, showcasing their private AI foundation with NVIDIA. The host experiments with fine-tuning AI models with proprietary data and using RAG for real-time database consultation, illustrating the potential of private AI for personal and professional use.
Takeaways
- 😀 The video discusses running a private AI model on one's own computer, separate from internet-connected services like chat GPT.
- 🔒 Privacy is a key benefit, as data remains local and is not shared with external companies.
- 💻 Setting up a personal AI is presented as being straightforward and quick, with the process taking about five minutes.
- 🆓 The AI model can be freely downloaded and used, offering a cost-effective solution for personal or business use.
- 📈 The video highlights the potential of private AI in the workplace, especially in environments with strict privacy and security requirements.
- 🌐 VMware's sponsorship is mentioned as a key enabler for private AI, allowing companies to run AI models on-premises within their own data centers.
- 🚀 The video showcases the capabilities of AI models, such as answering questions and providing information without an internet connection.
- 📚 The script introduces 'hugging face.co' as a resource for downloading various AI models, including the popular Llama model.
- 💾 The process of fine-tuning AI models with proprietary data is discussed, allowing for customization to specific use cases.
- 🔧 Technical details are provided on how to install and run AI models using tools like O Lama and WSL (Windows Subsystem for Linux).
- 🎯 The video concludes with a quiz for viewers, incentivizing engagement with the content and offering a reward for those who perform well.
Q & A
What is the main difference between private AI and services like Chat GPT?
-Private AI runs entirely on the user's computer, ensuring data privacy and security as it does not share data with any external company or service.
How long does it take to set up your own AI according to the video?
-It takes about five minutes to set up your own AI on your laptop computer.
What is the advantage of running a private AI in a job setting?
-Running a private AI at work can bypass privacy and security restrictions that prevent the use of public AI services like Chat GPT.
Who is the sponsor of the video and what role do they play?
-VMware is the sponsor of the video, enabling companies to run their own AI on-premises in their data centers.
What does the AI model Llama two signify?
-Llama two is a large language model (LLM) known for its extensive training on a vast amount of data, similar to OpenAI's Chat GPT.
How many AI models are available on huggingface.co according to the video?
-There are over 505,000 AI models available on huggingface.co.
What does the acronym LLM stand for in the context of the video?
-LLM stands for Large Language Model, which is a type of AI model used for natural language processing and understanding.
How many GPUs were used to train the Llama two model as mentioned in the video?
-The Llama two model was trained using over 6,000 GPUs.
What does WSL stand for and how does it relate to running private AI?
-WSL stands for Windows Subsystem for Linux, which allows users to run Linux environments on Windows, useful for running tools and applications like private AI that may not have native Windows support.
What is fine-tuning in the context of AI models?
-Fine-tuning is the process of training an AI model further with new data to adapt it to a specific task or to improve its performance.
How does VMware's private AI solution simplify the process of running private AI?
-VMware's private AI solution provides a complete package with the necessary infrastructure, tools, and libraries pre-installed, making it easier for companies to run and fine-tune their own AI models.
Outlines
🤖 Introduction to Private AI
The speaker introduces the concept of running a private AI model on their computer, distinct from cloud-based AI like Chat GPT. They emphasize the privacy and security of keeping data local and outline two goals for the video: demonstrating the simple setup process for a personal AI and showcasing how to integrate personal documents and knowledge bases with the AI for customized queries. The speaker also discusses the benefits of private AI for professionals whose companies restrict the use of public AI tools due to privacy concerns. The video is sponsored by VMware, which enables on-premise AI solutions, and the speaker encourages viewers to explore VMware's offerings.
🔧 Setting Up Private AI on Your Computer
The speaker guides viewers through the process of setting up a private AI model on their computer. They begin by explaining what an AI model is and direct viewers to Hugging Face, a platform hosting numerous AI models available for use. The speaker highlights the Llama two model, developed by Meta (Facebook), and discusses its extensive training process involving over 2 trillion tokens of data and a super cluster of 6,000 GPUs. The speaker then demonstrates how to install and run the Llama two model using a tool called O Lama, which simplifies the process of running various large language models (LLMs) on different operating systems, including Windows through the Windows Subsystem for Linux (WSL).
💡 Enhancing Private AI with Personal Data
The speaker explores the concept of fine-tuning AI models with proprietary data to make them more useful for specific tasks or industries. They discuss how companies like VMware are leveraging private AI to keep sensitive data secure while still benefiting from AI capabilities. The speaker explains that fine-tuning an AI model requires significant computational resources, which VMware facilitates through its private AI solutions. They also touch on the idea of using AI models to enhance customer service by training them on company-specific knowledge bases and documentation.
🧠 Fine-Tuning AI with VMware's Private AI
The speaker delves into the technical aspects of fine-tuning AI models using VMware's private AI infrastructure. They describe the process of preparing data for training, the use of virtual machines equipped with Nvidia GPUs, and the tools provided by VMware to simplify the fine-tuning process. The speaker also introduces the concept of RAG (Retrieval-Augmented Generation), which allows AI models to consult databases or knowledge bases for accurate responses without the need for fine-tuning. They highlight VMware's partnerships with Nvidia, Intel, and IBM to provide a comprehensive suite of tools for both system administrators and data scientists to deploy and manage private AI solutions.
🌟 Personalizing AI with Your Own Knowledge Base
The speaker concludes with a demonstration of how to run a personal AI model connected to their own knowledge base, using a project called Private GPT. They detail the steps for setting up Private GPT on a Windows machine using WSL and Nvidia GPU, and show how to upload documents for the AI to learn from. The speaker then interacts with the AI, asking questions about their personal journal entries and demonstrating the AI's ability to retrieve and respond with information from the uploaded documents. This personalization of AI showcases the potential for customized, private AI solutions.
Mindmap
Keywords
💡Private AI
💡LLM (Large Language Model)
💡Hugging Face
💡Fine-tuning
💡VMware
💡WSL (Windows Subsystem for Linux)
💡RAG (Retrieval-Augmented Generation)
💡Data Freshness
💡vSphere
💡Prompt Tuning
Highlights
Introduction to running a private AI model on your computer, separate from internet-connected services.
Demonstration of setting up a private AI in under five minutes.
Explanation of how private AI can be integrated with personal or company data for customized assistance.
Discussion on the benefits of private AI for job-related tasks and overcoming privacy and security restrictions.
VMware's role in enabling on-premise AI solutions within companies' own data centers.
Overview of the process to install and run a private AI model using the tool 'O Lama'.
Tutorial on utilizing the Windows Subsystem for Linux (WSL) to run AI models on Windows machines.
Showcasing the power of AI models by downloading and running the Llama two model without internet connection.
Comparison of AI model performance on CPU vs. GPU, emphasizing the benefits of GPU usage.
Introduction to the concept of fine-tuning AI models to include proprietary or personal data.
VMware's solution for private AI, simplifying the process of fine-tuning AI models within companies.
Explanation of the resources and tools required for fine-tuning an AI model, such as GPUs and various SDKs.
Case study of VMware using AI to keep internal knowledge up-to-date with proprietary information.
Technical walkthrough of setting up a data scientist's environment for AI model fine-tuning within VMware's ecosystem.
Introduction to RAG (Retrieval-Augmented Generation) for enhancing AI responses with real-time database consultation.
Practical example of connecting personal journals to a private AI model to answer questions about personal experiences.
Emphasis on the flexibility and choice provided by VMware's partnerships with Nvidia, Intel, IBM, and others for private AI solutions.
Invitation to participate in a quiz for a chance to win free coffee from Network Chuck Coffee.