I Ran Advanced LLMs on the Raspberry Pi 5!

Data Slayer
7 Jan 202414:42

TLDRThe video explores running advanced language models on a Raspberry Pi 5, a cost-effective device. It tests various open-source models like Orca and Fi, and even attempts to run a 13 billion parameter model. The host demonstrates the practicality of these models for small computers, their uncensored nature, and the privacy of local interactions. The video also discusses the potential of these models for offline use and their ability to function as a local repository of knowledge.

Takeaways

  • 🤖 The Raspberry Pi 5, a cost-effective device, is used to explore the capabilities of advanced language models (LLMs).
  • 🚀 GPT-4 is anticipated to have over 1.7 trillion parameters, requiring substantial computational resources to run.
  • 🌐 The host is interested in testing open-source, small LLMs like Orca and Fi on modest hardware for practical applications.
  • 📈 The performance of various LLMs is evaluated, including private GPT, which is trained on local documents, and Mistl 7B, noted for its speed and capability.
  • 💾 Fast storage is recommended for the Raspberry Pi 5 to handle the large size of the models being tested.
  • 🔌 The Raspberry Pi 5 used in the demonstration has no internet connection, ensuring all operations are performed locally and privately.
  • 📸 The 'lava' model is tested for image analysis, demonstrating impressive accuracy in describing the content of an image.
  • 🌶️ 'Llama 2' is used to generate a spicy mayo recipe, showcasing the model's versatility in generating content.
  • 💬 Smaller models like Fi 2 and Orca Mini are tested for tasks such as historical trivia and coding commands, proving their practicality.
  • 🌐 The 'Mistl 7B' model is highlighted for its comprehensive responses, including a rhyming poem about semiconductors, indicating its advanced capabilities.
  • 🌐 The potential of LLMs to serve as a local, private knowledge base is discussed, which could be invaluable in the event of a catastrophic internet failure.

Q & A

  • What is the estimated parameter count for GPT-4?

    -GPT-4 is believed to feature more than 1.7 trillion parameters.

  • What are the hardware requirements to run GPT-4?

    -To run GPT-4, you would need hundreds of gigabytes of VRAM and likely over a 100 CPUs.

  • What is the price of the Raspberry Pi 5 mentioned in the script?

    -The Raspberry Pi 5 sells for just $80.

  • What is the purpose of using an external SSD with the Raspberry Pi 5?

    -An external SSD is used to store and train on local documents for private GPT.

  • Why is the Raspberry Pi 5 considered 'offline' in the script?

    -The Raspberry Pi 5 is considered 'offline' because it has no internet connection and is 100% private and off-grid.

  • What is the purpose of the LM Studio tool mentioned?

    -LM Studio is a tool that allows users to download, test, and swap major LLMs by running them from the command line.

  • What is the significance of the model called 'Lava' in the script?

    -The Lava model is significant because it claims to be able to analyze images.

  • What is the recipe generated by Llama 2 for a spicy mayo?

    -The recipe includes 1 cup mayonnaise, 2 tbsp yellow mustard, 2 tbsp hot sauce, two pinches of cayenne pepper, 1/2 teaspoon chili powder, and 1/2 teaspoon garlic powder.

  • What is the Venezuelan president's name mentioned in the script for the year 1980?

    -The Venezuelan president in 1980 was Carlos Andrés Pérez.

  • What is the Linux command provided by Fi 2 to delete a folder recursively?

    -The Linux command to delete a folder recursively is 'rmdir path to folder'.

  • What is the explanation given by Code Llama about the async/await concept?

    -Async/await is a programming construct that allows developers to write synchronous code that is easier to read and maintain, used to handle the results of async operations.

Outlines

00:00

🤖 Exploring AI Models on Raspberry Pi 5

The speaker discusses the capabilities of AI models like GPT-4 and the feasibility of running them on modest hardware like the Raspberry Pi 5. They mention the impressive specs of GPT-4, requiring significant computational resources. The focus then shifts to testing open-source, small language models like Orca and Fi on the Raspberry Pi 5, aiming to assess their practicality and performance. The speaker details the setup process, including the use of an external SSD for model storage and the utilization of tools like 'olama' for model testing. The video script also includes a live demonstration of the AI analyzing an image and generating a spicy mayo recipe, showcasing the model's versatility and accuracy.

05:02

🔍 Diving into Smaller AI Models and Their Capabilities

This section explores the performance of smaller AI models on the Raspberry Pi, starting with a historical trivia question answered by the Venezuelan president in 1980. The speaker then tests the model's coding knowledge by requesting a recursive folder deletion command in Linux. They also inquire about the reason behind the sky's blue color, receiving a scientifically accurate explanation involving Rayleigh scattering. The conversation moves to the translation capabilities of the AI, requesting a translation into Spanish. The speaker concludes this segment by comparing the performance of smaller models to larger, more capable ones, highlighting the speed and accuracy of llama 2 in handling basic facts and coding-related questions.

10:04

🚀 Testing Large-Scale AI Models and Their Practicality

The speaker attempts to run a 13 billion parameter model on the Raspberry Pi but finds it infeasible due to memory constraints. They discuss the potential of using an edge TPU like the Coral AI for acceleration but conclude it's inadequate for running even the smallest LLMs due to limited RAM. The speaker then explores the possibility of training models on external drives using Private GPT and demonstrates how it can answer questions based on trained documents. They test the Mistl 7B model, asking it to answer historical questions and generate a rhyming poem about semiconductors, which it does impressively. The speaker reflects on the potential of these models to contain a significant portion of the world's knowledge, suggesting their value in scenarios where internet access is unavailable.

Mindmap

Keywords

💡LLMs (Large Language Models)

Large Language Models (LLMs) are advanced artificial intelligence systems designed to understand and generate human-like text based on vast amounts of data. In the video, the host explores the feasibility of running these models on a Raspberry Pi 5, a relatively low-cost and less powerful device compared to the high-end hardware typically required for such tasks. The discussion revolves around the practicality and performance of LLMs like GPT-4, which is believed to have over 1.7 trillion parameters, and the implications of deploying them on small-scale devices.

💡Raspberry Pi 5

The Raspberry Pi 5 is a single-board computer that is part of the Raspberry Pi series, known for its affordability and versatility. In the context of the video, it is used to test the capabilities of running advanced AI models locally, which is a significant achievement considering the computational demands of such tasks. The host's experiment with the Raspberry Pi 5 highlights the potential for edge computing and the democratization of AI technology.

💡VRAM (Video Random-Access Memory)

VRAM, or Video Random-Access Memory, is a type of memory used to store image data for rendering or processing in computer graphics. The video mentions the need for 'hundreds of gigabytes of VRAM' to run certain LLMs, illustrating the high memory requirements for these models. This is a critical consideration when attempting to deploy such models on devices with limited resources like the Raspberry Pi 5.

💡Coral AI Edge TPU

The Coral AI Edge TPU is a hardware accelerator designed to speed up machine learning tasks, particularly for on-device or edge computing applications. The video explores the possibility of using this technology to enhance the performance of LLMs on the Raspberry Pi 5. However, it is noted that the TPU's memory limitations make it unsuitable for running even the smallest LLMs, indicating the challenges in optimizing AI models for low-resource environments.

💡Private GPT

Private GPT refers to a version of the GPT (Generative Pre-trained Transformer) model that can be trained on local documents, allowing for the creation of customized AI models. In the video, the host demonstrates training Private GPT on a biography of Susan B. Anthony, enabling the model to answer questions specific to the content of the document. This showcases the potential for personalized AI applications and the ability to maintain data privacy.

💡Mistil 7B

Mistil 7B is mentioned as a 7 billion parameter model that the host tests for its capabilities on the Raspberry Pi 5. The model is praised for its performance and accuracy in answering questions, including historical facts and even creative tasks like writing a rhyming poem. The video highlights Mistil 7B as a standout model, suggesting that it offers a good balance between size and capability for edge devices.

💡Edge Computing

Edge computing refers to the practice of processing data near the source of the data, rather than in a centralized location. The video's exploration of running LLMs on the Raspberry Pi 5 is an example of edge computing, as it involves performing AI tasks on a local device rather than relying on cloud-based services. This approach can reduce latency, improve privacy, and enable functionality in offline environments.

💡Orca

Orca is one of the open-source, small language models mentioned in the video. It is tested for its practicality on the Raspberry Pi 5 and its ability to perform tasks like image analysis and translation. The mention of Orca illustrates the host's interest in finding efficient and effective AI models that can run on less powerful hardware, making advanced AI more accessible.

💡Fi

Fi is another small language model discussed in the video. It is tested for its ability to answer historical trivia and provide coding assistance, demonstrating its versatility and utility in a small package. The inclusion of Fi in the video's experiments further emphasizes the quest for practical, lightweight AI solutions that can be deployed on devices like the Raspberry Pi 5.

💡Llama

Llama is a series of language models tested in the video, including Llama 2 and Llama 13B. The host evaluates these models for their performance on the Raspberry Pi 5, noting their ability to handle various tasks such as recipe generation and coding assistance. The term 'Llama' in this context represents the host's broader investigation into the state-of-the-art in small, efficient language models suitable for edge computing.

Highlights

GPT-4 is believed to have over 1.7 trillion parameters, requiring extensive computational resources to run.

The Raspberry Pi 5, priced at $80, is used to explore the capabilities of advanced language models on modest hardware.

Open-source, small language models like Orca and Fi are tested for practicality on small computers.

Coral AI Edge TPUs are considered for accelerating model performance, but their memory limitations are noted.

The project's objective is to test every major LLM, including private GPT, trained on local documents.

The Raspberry Pi 5 with 8 GB RAM and a 64-bit OS is used for testing, with an emphasis on fast storage.

LM Studio is not compatible with ARM architecture, prompting the use of a new tool called Olam.

The Raspberry Pi is set up offline for complete privacy during model testing.

Lava, an image analysis model, accurately describes a selfie image.

Llama 2 generates a spicy mayo recipe, showcasing its capability for general tasks.

Fi 2 answers historical trivia and provides Linux commands, demonstrating its versatility.

Ora mini translates a sentence into Spanish, highlighting the model's language capabilities.

Llama 2 provides accurate and concise answers to basic facts and coding questions.

Code Llama explains the concept of async/await in JavaScript, showing its suitability for developer assistance.

The 13 billion parameter Llama model is too large to run on the Raspberry Pi 5.

Private GPT is trained on local documents, allowing for personalized responses.

Mistil 7B impresses with its ability to answer questions and create content across various domains.

The video discusses the potential of LLMs to serve as local, private AI in the event of a catastrophic internet failure.

The video concludes by highlighting the rapid advancements in LLMs and their potential future use on the edge.

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