Run your own AI (but private)
TLDRThe video introduces the concept of private AI, demonstrating how to set up a local AI model on one's computer for data privacy and security. It showcases the ease of using tools like Hugging Face and O Lama to run various AI models, including an uncensored version. The video also discusses the potential of private AI in the workplace, emphasizing VMware's role in enabling on-premise AI solutions. It further explores fine-tuning AI models with proprietary data and highlights the capabilities of VMware's private AI foundation with NVIDIA and Intel's support for custom LLM development and deployment.
Takeaways
- 🌟 Private AI allows users to run AI models locally on their computers, ensuring data privacy and security.
- 🚀 Setting up a private AI model is quick and easy, taking about five minutes and offering free access to powerful AI capabilities.
- 📚 Hugging Face's platform hosts a vast collection of AI models, with over 505,000 available for use, many of which are free and pre-trained.
- 💡 Large Language Models (LLMs) like Llama and Chat GPT can be downloaded and used offline without internet connection.
- 🔍 Users can fine-tune AI models with their own data to create customized, private AI that understands specific information or contexts.
- 🛠️ VMware's Private AI, in partnership with Nvidia, provides a comprehensive solution for companies to run their own private AI, including necessary tools and infrastructure.
- 🔧 Fine-tuning an AI model doesn't require massive resources; it involves changing a small percentage of the model's parameters based on new data.
- 📈 VMware's solution includes pre-installed deep learning VMs with tools like PyTorch, TensorFlow, and others, simplifying the process for data scientists.
- 🔗 RAG (Retrieval-Augmented Generation) allows AI models to consult databases or knowledge bases for accurate information before generating responses.
- 🎯 VMware also partners with Intel and IBM, offering a range of options for AI development and deployment, emphasizing choice for the user.
- 🎁 The video offers a quiz for viewers with the chance to win free coffee from Network Chuck Coffee for the first five people who score 100%.
Q & A
What is the main advantage of running a private AI like the one discussed in the video?
-The main advantage of running a private AI is that it allows users to keep their data private and contained locally on their own computer, without sharing it with external companies or entities.
How long does it take to set up a private AI on your computer according to the video?
-It takes about five minutes to set up a private AI on your computer, as mentioned in the video.
What is the significance of the number 505,000 in the context of the video?
-The number 505,000 refers to the number of AI models available on Hugging Face's platform, which are open and free for users to use and pre-trained.
What does LLM stand for in the context of AI?
-LLM stands for Large Language Model, which is a type of AI model pre-trained on large datasets to understand and generate human-like text.
How much did it cost to train the Llama two model as mentioned in the video?
-It is estimated that it cost around $20 million to train the Llama two model.
What is the role of a GPU in running AI models?
-A GPU (Graphics Processing Unit) is used to accelerate the processing of AI models, making the running of these models faster and more efficient.
What is the purpose of the tool called RAG mentioned in the video?
-RAG, or Retrieval-Augmented Generation, is a tool that allows an LLM to consult a database or knowledge base before answering questions to ensure the accuracy of the information provided.
How does VMware's solution differ from the private GPT side project discussed in the video?
-VMware's solution provides a complete, easy-to-use package for companies to run their own private local AI, including pre-installed tools and infrastructure, whereas the private GPT side project requires manual installation of numerous tools and is more complex to set up.
What is the significance of the term 'data freshness' in the context of AI training?
-Data freshness refers to the recency and up-to-date nature of the data used for training AI models, ensuring that the AI can provide the most current and relevant information.
What is the main challenge for individuals or companies wanting to fine-tune AI models on their own data?
-The main challenge is the requirement for specialized hardware like GPUs and a variety of tools and libraries, which can be complex and resource-intensive to set up and manage.
How does the video demonstrate the practical use of a private AI?
-The video demonstrates the practical use of a private AI by showing how it can be connected to personal documents and journals, and then used to ask questions and receive relevant information from those documents.
Outlines
🌟 Introduction to Private AI
The speaker introduces the concept of private AI, contrasting it with public AI models like Chat GPT. They emphasize the benefits of running AI on a personal computer, ensuring data privacy and security. The video aims to show viewers how to set up their own AI quickly and easily, and to discuss how private AI can be beneficial in professional settings, especially where company policies restrict the use of public AI models due to privacy concerns. The speaker also mentions VMware's role in enabling private AI solutions.
🚀 Setting Up Private AI on Your Computer
The speaker guides the audience through the process of installing a private local AI model on their computer. They discuss the availability of AI models on platforms like hugging face.co and the vast number of models that can be used for free. The speaker then demonstrates how to install Windows Subsystem for Linux (WSL) and use a tool called O Lama to run different LLMs, including Llama two, an uncensored version of a popular model. The process is shown for both Linux and Windows users, highlighting the benefits of using a GPU for faster AI processing.
🔍 Fine-Tuning AI for Specific Use Cases
The speaker explains the concept of fine-tuning an AI model with specific data to tailor it to individual or company needs. They discuss the resource-intensive process of pre-training an AI model and contrast it with the relatively modest requirements for fine-tuning. The speaker uses VMware as an example of a company that provides tools and infrastructure for fine-tuning AI models, making the process more accessible for businesses and individuals. They also touch on the potential applications of fine-tuned AI, such as internal knowledge bases and customer-facing chatbots.
🛠️ Implementing Private AI with VMware and NVIDIA
The speaker delves into how VMware and NVIDIA collaborate to provide a comprehensive solution for private AI. They discuss the ease of setting up and fine-tuning AI models using VMware's infrastructure and NVIDIA's AI tools. The speaker highlights the benefits of using tools like RAG (Retrieval-Augmented Generation) to connect AI models with databases of proprietary information, allowing for accurate and personalized responses. The video also mentions partnerships with Intel and IBM, emphasizing the flexibility and choice that VMware offers for running private AI.
🎁 Bonus Content: Running Your Own Private GPT
The speaker shares a personal project of running a private GPT model using RAG, which allows the AI to consult a database of personal notes and journal entries for accurate responses. They provide a step-by-step guide on how to set up a private GPT, including installing necessary prerequisites and leveraging GPUs for processing power. The speaker demonstrates the functionality by asking the AI about personal experiences, showcasing the potential of private AI for personalized applications.
Mindmap
Keywords
💡Private AI
💡Chat GPT
💡Hugging Face
💡LLM (Large Language Model)
💡Fine-tuning
💡VMware
💡Data Privacy
💡WSL (Windows Subsystem for Linux)
💡GPU (Graphics Processing Unit)
💡RAG (Retrieval-Augmented Generation)
Highlights
The introduction of a private AI model that runs locally on a user's computer, ensuring data privacy and security.
The ease and speed of setting up a private AI, which can be done in about five minutes and is free to use.
The ability to connect personal knowledge bases, notes, documents, and journal entries to a private GPT for personalized queries.
The discussion on how private AI can be beneficial in job environments, especially where the use of public AI models is restricted due to privacy and security concerns.
The role of VMware in enabling on-premises AI deployment, allowing companies to run their own AI within their data centers.
The mention of Hugging Face, a platform with a community dedicated to sharing AI models, which hosts over 505,000 AI models.
The explanation of AI models, specifically large language models (LLMs) like Llama and Chat GPT, and their pre-training processes.
The demonstration of downloading and using a pre-trained AI model like Llama 2, which was trained using over 2 trillion tokens of data and required 1.7 million GPU hours.
The tool O Lama, which simplifies the process of running various LLMs on a local machine.
The compatibility of the private AI setup with different operating systems, including macOS, Linux, and Windows through WSL.
The performance difference when running AI models on GPUs versus CPUs, with GPUs significantly speeding up the process.
The concept of fine-tuning AI models with proprietary data to make them more accurate and relevant for specific use cases.
The example of VMware's private AI with NVIDIA, which includes necessary tools and resources for fine-tuning AI models in a single package.
The use of RAG (Retrieval-Augmented Generation) to connect an LLM to a database of information for providing accurate answers without retraining the model.
The potential of private AI in various applications, from personal use to business solutions, offering a more private and customizable approach to AI technology.
The quiz at the end of the video for viewers to test their understanding of the content, with a chance to win free coffee from Network Chuck Coffee.
