How to Run LLAMA 3 on your PC or Raspberry Pi 5

In this video, the host, Gary Sims, discusses the recent launch of LLAMA 3 by Meta (Facebook), a next-generation large language model available in two sizes: an 8 billion parameter version and a 70 billion parameter version. The video focuses on running the 8 billion parameter version locally due to hardware limitations for the larger model. LLAMA 3 demonstrates significant performance improvements over LLAMA 2, with the 8 billion parameter version outperforming the 13 billion parameter version of LLAMA 2 by 8%. The knowledge cutoff for this model is March 2023. The video provides a step-by-step guide on how to run LLAMA 3 using LM Studio on Windows and the O Lama project on a Raspberry Pi 5, showcasing the model's capabilities and the ease of local deployment. The host also tests LLAMA 3's knowledge with various questions, highlighting its depth and reasoning abilities. The video concludes with an invitation for viewers to share their thoughts on LLAMA 3 and running large language models on different devices.

Takeaways

• 🚀 Facebook has launched LLaMa 3, a next-generation large language model available in 8 billion and 70 billion parameter versions.
• 💻 The 8 billion parameter version of LLaMa 3 is suitable for running on a normal desktop or laptop, whereas the 70 billion parameter version requires more powerful hardware.
• ⏱️ LLaMa 3's 8 billion parameter version was trained using 1.3 million hours of GPU time and offers significant performance improvements over LLaMa 2.
• 📈 LLaMa 3's 8 billion parameter version is 34% better than LLaMa 2's 7 billion parameter version and 14% better than LLaMa 2's 13 billion parameter version.
• 📚 The knowledge cutoff date for LLaMa 3's 8 billion parameter version is March 2023, and for the 70 billion parameter version, it's December 2023.
• 🌐 LM Studio can be used to run LLaMa 3 on Windows, and it's also compatible with M1, M2, M3 processors and Linux.
• 📲 LM Studio provides a user interface for interacting with the language model, including a chat function similar to chat GPT.
• 🔍 Users can download and select different models within LM Studio, including LLaMa 3 and Google's 2 billion parameter model.
• 🧐 LLaMa 3 demonstrates a deep level of knowledge, even in its 8 billion parameter version, as shown by its detailed responses to queries.
• 🔗 Another way to run LLaMa 3 locally is through the OLLaMa project, which is available for Mac OS, Linux, and Windows.
• 📝 The OLLaMa project allows users to run LLaMa 3 directly from the command line after installation, as demonstrated on a Raspberry Pi 5.
• 📈 LLaMa 3's ability to handle complex queries and lateral thinking puzzles showcases its advanced language understanding capabilities.

Q & A

What are the two sizes of LLaMA 3 that Meta has launched?

Meta has launched two sizes of LLaMA 3: an 8 billion parameter version and a 70 billion parameter version.

Why is the 8 billion parameter version of LLaMA 3 used for local running instead of the 70 billion version?

The 8 billion parameter version is used for local running because a normal desktop or laptop isn't capable of running the larger 70 billion parameter version due to its size and computational requirements.

How much better is the 8 billion parameter version of LLaMA 3 compared to LLaMA 2?

The 8 billion parameter version of LLaMA 3 is 34% better than the 7 billion parameter version and 14% better than the 13 billion parameter version of LLaMA 2.

What is the knowledge cutoff date for the 8 billion parameter version of LLaMA 3?

The knowledge cutoff date for the 8 billion parameter version of LLaMA 3 is March of 2023.

What platform is used to run LLaMA 3 on Windows?

LM Studio is used to run LLaMA 3 on Windows.

How can one access different sections of the program in LM Studio?

In LM Studio, one can access different sections of the program by navigating to the left side of the interface where the sections are listed.

What is the purpose of the chat function in LM Studio?

The chat function in LM Studio provides a chat interface similar to chat GPT, allowing users to interact with the locally downloaded LLaMA 3 model.

How does one select a specific model to use in LM Studio?

To select a specific model in LM Studio, users need to go to the chat function and choose the desired model from the list of downloaded models.

What is the significance of the 8 billion parameter version of LLaMA 3 being only 8% worse than the 70 billion parameter version?

This signifies that the 8 billion parameter version offers a remarkable level of performance and knowledge, approaching the capabilities of the much larger 70 billion parameter version, making it a highly efficient model for most applications.

How does one install and run LLaMA 3 on a Raspberry Pi 5 using the OLLaMA project?

To install and run LLaMA 3 on a Raspberry Pi 5 using the OLLaMA project, one needs to visit OLLaMA's website, download the install script, paste and run it on the Raspberry Pi, and then use the command line to run LLaMA 3 with the 'ollama run llama 3' command.

What is a common challenge that LLaMA models face when answering lateral thinking puzzles?

A common challenge is avoiding the trap of simple multiplication or linear thinking. For example, LLaMA models should recognize that the drying time of towels does not depend on the number of towels but rather on the characteristics of the towels and the drying process.

What are the different devices and operating systems on which one can run LLaMA 3?

LLaMA 3 can be run on various devices including a Raspberry Pi, laptop, desktop, and on different operating systems such as Windows, Mac OS, and Linux.

Outlines

00:00

🚀 Introduction to Llama 3 and Local Execution

The video introduces Llama 3, a next-generation large language model by Meta (Facebook), which comes in two sizes: an 8 billion parameter version and a 70 billion parameter version. The video focuses on the 8 billion parameter version, which is feasible to run locally due to hardware limitations. Llama 3 is compared to Llama 2, showing significant performance improvements. The knowledge cutoff for the 8 billion parameter model is March 2023, and for the 70 billion parameter version, it's December 2023. The video demonstrates how to run Llama 3 locally using LM Studio on Windows and also hints at running it on a Raspberry Pi 5. LM Studio is shown with options to download for various platforms, and the process of selecting and using the Llama 3 model within the application is detailed. The capabilities of Llama 3 are tested with a historical question and a logic puzzle, showcasing its knowledge and reasoning.

05:00

📚 Running Llama 3 on Raspberry Pi using OLLama

The second part of the video script explains an alternative method to run Llama 3 locally using the OLLama project. It provides instructions for downloading and installing the project on a Raspberry Pi 5, which is chosen due to its Linux OS compatibility. The process involves running an install script to download and set up Llama 3 on the Raspberry Pi. Once installed, the viewer is shown how to interact with Llama 3 through a command-line interface, including asking a classic lateral thinking puzzle about towel drying times. The video emphasizes the flexibility of running Llama 3 on various devices, from a Raspberry Pi to a desktop or laptop, and invites viewers to share their thoughts on the model and the experience of running large language models locally.

Keywords

LLAMA 3

LLAMA 3 refers to the third generation of a large language model developed by Meta (formerly Facebook). It is available in two sizes: an 8 billion parameter version and a 70 billion parameter version. The model is designed to process and understand human language at a high level, enabling tasks such as text generation, summarization, and question-answering. In the video, the focus is on running the 8 billion parameter version locally due to the computational constraints of running the larger 70 billion parameter model.

LM Studio

LM Studio is a platform mentioned in the video that allows users to run and interact with large language models like LLAMA 3 on their local machines. It provides a user interface for downloading models, accessing different sections of the program, and engaging in a chat interface to communicate with the model. The video demonstrates how to use LM Studio to download and run LLAMA 3 on a Windows system.

Raspberry Pi 5

The Raspberry Pi 5 is a small, affordable, and powerful computer used in the video to demonstrate running the LLAMA 3 model locally. Despite its compact size, it is capable of running sophisticated applications like LLAMA 3, showcasing the versatility and accessibility of large language models. The video provides a guide on installing and running LLAMA 3 on a Raspberry Pi 5 using the OLLama project.

Parameter

In the context of the video, a parameter refers to a specific aspect or element within a machine learning model, particularly a neural network. The number of parameters is indicative of the model's complexity and capacity to learn from data. An 8 billion parameter version of LLAMA 3 implies that the model has been trained on a vast amount of data and can perform intricate language tasks.

GPU Time

GPU Time stands for Graphics Processing Unit Time and refers to the duration that a GPU (Graphics Processing Unit) is used for processing tasks, such as training a machine learning model. In the video, it is mentioned that the 8 billion parameter version of LLAMA 3 was trained using 1.3 million hours of GPU time, highlighting the intensive computational resources required to train advanced language models.

Knowledge Cut-off Date

The knowledge cut-off date is the point in time up to which the information and training data of a language model is considered current. For the LLAMA 3 8 billion parameter version, this date is March 2023, and for the 70 billion parameter version, it is December 2023. This date is significant as it determines the freshness and relevance of the information the model can provide.

OLLama Project

The OLLama Project is an initiative that enables users to run various large language models, including LLAMA 3, on different operating systems and hardware, such as a Raspberry Pi 5. The project provides an open-source solution for installing and interacting with these models locally. The video demonstrates how to use the OLLama Project to download, install, and run LLAMA 3 on a Linux-based system.

Local Running

Local running refers to the execution of software or models on a user's own computer or device rather than on a remote server or cloud platform. In the context of the video, local running of LLAMA 3 allows users to interact with the model without relying on internet connectivity or external services. This approach can offer benefits such as reduced latency and privacy.

Chat Interface

A chat interface is a user interface designed for communication between a user and a computer program, in this case, a language model like LLAMA 3. The video showcases a chat interface within LM Studio and the command line on a Raspberry Pi 5, where users can ask questions and receive responses generated by the model in real-time.

Lateral Thinking Puzzle

A lateral thinking puzzle is a type of brain teaser that requires creative and indirect reasoning to solve, rather than a straightforward logical approach. In the video, a classic lateral thinking puzzle about drying towels is presented to LLAMA 3 to demonstrate its ability to understand and respond to non-linear problems. The model correctly identifies that the drying time does not scale linearly with the number of towels.

Large Language Model

A large language model is an artificial intelligence system designed to understand and generate human language at scale. These models are trained on vast amounts of text data and can perform a variety of language-related tasks. The video focuses on LLAMA 3, a next-generation large language model that offers improved performance over its predecessors.

Highlights

Facebook has launched LLaMa 3, a next-generation large language model available in two sizes: an 8 billion parameter version and a 70 billion parameter version.

The 8 billion parameter version of LLaMa 3 is more performant than LLaMa 2, being 34% better than the 7 billion parameter version and 14% better than the 13 billion parameter version of LLaMa 2.

The 8 billion parameter version of LLaMa 3 has a knowledge cutoff date of March 2023, while the 70 billion version's is December 2023.

LM Studio can be used to run LLaMa 3 locally on Windows, and it is also compatible with M1, M2, M3 processors and Linux.

LLaMa 38 billion parameter version has been added to LM Studio, enabling users to download and use it locally.

LM Studio provides a chat interface similar to chat GPT for interacting with the locally hosted LLaMa 3 model.

Smaller models may lack information; for example, a 2 billion parameter model might not answer a question about Henry VII.

LLaMa 3's 8 billion parameter version can provide detailed information, such as the year Henry VII was married to Katherine of Aragon.

LLaMa 3 can answer logical questions and perform tasks like identifying the color of an object in a list.

When comparing movies to Star Wars Episode IV: A New Hope, LLaMa 3 identifies The Princess Bride as the most similar due to its classic adventure and fantastical world.

The OLLaMa project allows users to run LLaMa 3 locally using a command-line interface, which is also compatible with Raspberry Pi 5.

To install LLaMa 3 on a Raspberry Pi 5, users can run a provided script from the OLLaMa website.

LLaMa 3 can be run on various platforms, including a Raspberry Pi, laptop, or desktop, offering flexibility for users to experiment with the model.

LLaMa 3 is capable of understanding and responding to lateral thinking puzzles, such as the towel drying scenario.

The video demonstrates LLaMa 3 running on a Raspberry Pi 5, showcasing its ability to function on lower-powered devices.

Gary Sims, the presenter, invites viewers to share their thoughts on LLaMa 3 and running large language models on different devices.

The video concludes with an invitation to subscribe to the channel for more content on machine learning and AI.