LLMs Guides to Deploy and Running LLama, Alpaca, BERT on Raspberry Pi & LattePanda SBCs
Get ready to unleash the power of LLMs! This guide collection is for exploring the capabilities of AI LLMs like LLaMA, Alpaca, and BERT on Raspberry Pi 4B, LattePanda Alpha, LattePanda 3 Delta, and LattePanda Sigma single-board computers. Learn how to deploy and run LLMs locally. Discover the potential of ChatGPT and AI and learn how to use them to make creative and useful projects.
How to Deploy and run LLM on Single Board Computer?
Guide 1. Comprehensive Guide of Running LLM (LLaMA, Alpaca, and BERT) on Single Board Computer
Introduction: This guide provides an in-depth exploration of large language models (LLM) such as LLaMA, Alpaca, and BERT, and their execution on CPUs. The guide elucidates the configuration and testing of these models on single board computers like Raspberry Pi and LattePanda. It also discusses the comparative advantages of CPUs and GPUs in AI model deployment. The guide includes practical tests with different LLMs on various single-board computers, offering readers a hands-on understanding of running these models efficiently.
Guide 2. Deploy and run LLM on Raspberry Pi 4B (LLaMA, Alpaca, LLaMA2, ChatGLM)
Introduction: This guide explores the deployment of large language models (LLM) such as LLaMA, Alpaca, LLaMA2, and ChatGLM on Raspberry Pi 4B. It provides insights into the challenges and solutions of running these models on devices with limited computational resources. The guide also offers practical advice on model selection, focusing on models that can run on a CPU and have smaller memory footprints. It concludes with a hands-on tutorial on building an AI Chatbot Server on Raspberry Pi 4B.
Guide 3. Running LLaMA 7B on a 8 GB RAM LattePanda Alpha
Introduction: The tutorial guides users through the process of executing the LLaMA 7B model on a LattePanda Alpha, a powerful single-board computer. The tutorial provides step-by-step instructions, from setting up the environment to running the model, and includes detailed explanations of each step. It also addresses the challenges of running large language models on devices with limited computational resources and offers solutions to overcome these challenges.
Guide 4. Testing the LLaMa Language Model on Lattepanda Sigma: Unleashing AI Capabilities on a SBC
Introduction: The guide provides an overview of large language models, their development, and their application on single-board computers. It also offers a detailed walkthrough of running different versions of the LLaMa model, including LLaMa 7B and LLaMa 13B, highlighting their performance and resource demands. The guide concludes with a practical demonstration of running these models using a C/C++ version, making it accessible for users without powerful GPUs.
How to use LLM to Make Fun Projects?
Project 1. AI-driven IoT Shopping Assistant w/ ChatGPT
Introduction: The guide provides a detailed walkthrough of developing a full-fledged e-commerce web application and installing an Apache HTTP Server on LattePanda 3 Delta. It also demonstrates how to employ the OpenAI API to generate ChatGPT-powered recommendations and Brevo's Email API to send HTML emails directly from localhost.
Project 2. Grammatizator, fiction writing assisted by ChatGPT
Introduction: The project combines the power of AI with the art of fiction writing. Using a Raspberry Pi and a custom Python-coded text editor, the Grammatizator allows users to insert bursts of text generated by ChatGPT into their writing. The AI model can be adjusted to mimic different writing styles, from dry and concise to poetic and surreal. This project offers a unique and innovative approach to creative writing, making it an exciting venture for both tech enthusiasts and fiction writers.
Project 3. Unihiker chattering teeth optimistic news reader with ChatGPT
Introduction: The project involves programming a tiny Debian Linux board with an integrated touchscreen to fetch the latest news headlines via RSS feeds. These headlines are then rewritten by the OpenAI ChatGPT API in a happy, optimistic tone. The revised headlines are converted into an audio file using Google Translate's text-to-speech API and played back through a Bluetooth speaker. Simultaneously, a set of plastic chattering teeth, controlled by an SG90 servo motor, mimics the action of reading the headlines, adding a comedic effect. The project requires various hardware components, software configurations, and a bit of creativity.
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