I Ran Advanced LLMs on the Raspberry Pi 5!
TLDRIn this video, the host explores the capabilities of running advanced language models on a Raspberry Pi 5, a cost-effective device. He investigates open-source models like Orca and Fi, and tests their performance on small-scale hardware. The host also experiments with Coral AI Edge TPUs for acceleration and trains a model on local documents using Private GPT. The video concludes with a demonstration of Mistl 7B, a 7 billion parameter model, showcasing its impressive accuracy and speed, hinting at the potential of edge computing for AI.
Takeaways
- 😲 GPT-4 is believed to have over 1.7 trillion parameters, requiring significant computational resources to run.
- 🤖 The Raspberry Pi 5, priced at $80, is explored for its potential to run advanced language models with open-source and free alternatives.
- 🚀 The host aims to test various major LLMs, including private GPT, on the Raspberry Pi 5 to assess their practicality and performance on small computers.
- 📈 The video demonstrates the use of the 'olama' tool for downloading, testing, and swapping major LLMs via command line.
- 🔍 The 'lava' model is showcased for its image analysis capabilities, accurately describing a selfie image.
- 🌶️ 'Llama 2' is tested for its general-purpose functionality, including generating a spicy mayo recipe.
- 💻 The video includes a coding segment where 'Fi 2' provides a Linux command for recursive folder deletion.
- 🌐 'Ora' is tested for its language translation capabilities, translating a sentence from English to Spanish.
- 🧠 'Llama 2' demonstrates its ability to answer basic factual questions and provide code examples, like a time decay function in JavaScript.
- 🌐 The video ponders the potential of using LLMs in a post-internet scenario, serving as a local repository of knowledge.
- 🏆 'Mistral 7B' is highlighted as a standout model for its performance in various tasks, including writing a rhyming poem about semiconductors.
Q & A
What is the estimated parameter count of GPT-4?
-GPT-4 is believed to feature more than 1.7 trillion parameters.
What is the cost of the Raspberry Pi 5 mentioned in the script?
-The Raspberry Pi 5, which is used in the script, sells for just $80.
What are some open-source, small language models that can run on modest hardware like the Raspberry Pi?
-Some open-source, small language models that can run on modest hardware include Orca and Fi.
What is the purpose of using a Coral AI Edge TPU in conjunction with the Raspberry Pi?
-The Coral AI Edge TPU is considered for accelerating the performance of language models on the Raspberry Pi, although it's later mentioned that it may not be adequate due to its limited RAM and SRAM.
What is the memory requirement for running a 7 billion parameter model?
-For running a 7 billion parameter model, it is suggested to have around 7 gigabytes of RAM.
How does the script describe the capabilities of the Llama 2 model?
-The Llama 2 model is described as a great general-purpose model that can perform tasks like image analysis, recipe generation, and coding assistance.
What is the significance of training Private GPT on local documents?
-Training Private GPT on local documents allows the model to answer questions in a chatbot type way, specifically tailored to the content of the documents provided.
What is the claim of the Mistral 7B model in the script?
-The Mistral 7B model is claimed to be the most capable 7 billion parameter model.
What is the potential use of local LLMs in the event of a catastrophic internet failure?
-In the event of a catastrophic internet failure, local LLMs could serve as a source of historical knowledge, language information, and practical how-to guides, acting as a local, private AI.
How does the script suggest using the Raspberry Pi 5 for local model inference?
-The script suggests using the Raspberry Pi 5 with 8 GB of RAM, running a 64-bit OS, and utilizing fast storage like a 256 GB micro SD or external SSDs for local model inference.
Outlines
🤖 Exploring AI on Raspberry Pi 5
The speaker begins by introducing a project to test the capabilities of large AI models like GPT-4 and smaller, open-source alternatives on a Raspberry Pi 5. They discuss the massive computational requirements of running huge models and pivot to exploring more modest, practical options. The speaker's goal is to evaluate various AI language models (LLMs), including Private GPT, on the Raspberry Pi 5, focusing on their performance and practicality for small devices. The setup includes an 8 GB Raspberry Pi 5 running a 64-bit OS with fast storage options like a 256 GB micro SD card for better performance. The speaker also mentions the use of Coral AI Edge TPU for potential acceleration but notes its limitations due to memory constraints.
🔍 Testing LLMs for Practicality and Performance
The speaker proceeds to test different LLMs on the Raspberry Pi, starting with a model called Lava for image analysis. They upload a selfie and observe the model's performance and resource utilization. The model accurately describes the image content, impressing the speaker with its accuracy. They then test Llama 2 for general-purpose tasks, including recipe generation and historical trivia. The speaker also tests smaller models like Fi and Ora, noting their faster response times and practicality for smaller tasks. The discussion includes the potential of these models for IoT applications and their uncensored nature, offering privacy and reduced costs.
🚀 Running Large Models and Training Custom LLMs
The speaker attempts to run a 13 billion parameter model but finds it too resource-intensive for the Raspberry Pi. They consider using an Edge TPU for acceleration but find its memory insufficient. The speaker then explores training a model on external files using Private GPT, demonstrating how it can be tailored to specific documents. They successfully train the model on a biography of Susan B. Anthony and query it for information, receiving accurate responses with citations. The speaker also tests the Mistl 7B model, which performs well on various tasks, including a rhyming poem about semiconductors. The video concludes with a discussion on the potential of LLMs to contain significant portions of the world's knowledge, suggesting their value in scenarios where internet access is lost.
Mindmap
Keywords
💡LLMs
💡Raspberry Pi 5
💡Coral AI Edge TPU
💡Private GPT
💡Orca
💡Fi
💡Mistl 7B
💡On-device AI
💡RAM
💡Edge Computing
Highlights
GPT-4 is believed to have over 1.7 trillion parameters, requiring significant computational resources to run.
The Raspberry Pi 5, priced at $80, is explored for its capability to run advanced language models.
Open-source, free small language models like Orca and Fi are tested for practicality on small computers.
Coral AI Edge TPUs are considered for accelerating model performance.
The use of LM Studio is discussed, but it does not support ARM architecture.
Olama is introduced as a tool for downloading, testing, and swapping major LLMs.
The Raspberry Pi 5 is set up with 8 GB of RAM and a 64-bit OS for testing.
Fast storage solutions like 256 GB micro SD or external SSDs are recommended for model performance.
The Raspberry Pi is used in an offline, private setup for local model testing.
Lava, a model for image analysis, is tested and shown to provide accurate image descriptions.
Llama 2 is demonstrated to generate a spicy mayo recipe and answer historical trivia.
Fi 2 is tested for its ability to answer coding-related questions and general knowledge.
Orca Mini is used to translate a sentence into Spanish, showcasing multilingual capabilities.
Llama 2 is tested for its performance in answering basic facts and coding questions.
Code Llama is praised for its ability to explain programming concepts and provide code examples.
The attempt to run the 13 billion parameter Llama model on the Raspberry Pi is discussed.
The potential of running LLMs on a cluster of Raspberry Pis is considered.
Private GPT is demonstrated for training on local documents and answering questions based on that training.
Mistil 7B is tested and shown to produce a rhyming poem about semiconductors.
The discussion on the potential of LLMs to act as a local, private AI in case of a catastrophic event is highlighted.