Run Llama 3 on CPU using Ollama

In this informative video, the presenter guides viewers on how to use the Ollama tool to run the Llama 3 model on a CPU, which is particularly useful for those with limited computational resources like 16 GB or 8 GB of RAM. The video explains that Ollama is a no-code/low-code tool that simplifies the process of loading and inferring large language models (LLMs) locally, allowing users to build and test applications without high computational requirements. The presenter demonstrates the installation process for different operating systems and shows how to run the Llama 3 model using a simple command. The video also highlights the tool's compatibility with LangChain, making it easy to integrate into various applications. The presenter tests the model's capabilities by asking several questions and discusses the model's limitations, such as its inability to answer certain sensitive questions. The video concludes with an encouragement to download and experiment with Ollama for local model testing and a teaser for an upcoming video on working with LangChain.

Takeaways

• 🚀 **Llama 3 Overview**: Llama 3 is a new open-source language model released by Meta AI that has shown strong performance on evaluation benchmarks.
• 💡 **Ollama Tool**: Ollama is a no-code/low-code tool that allows users to load and run language models locally, which is useful for those with limited compute resources like 16 GB or 8 GB of RAM.
• 📥 **Downloading Ollama**: Users can download Ollama for different operating systems (Windows, Mac OS, Linux) from the official website.
• 🔧 **Installation Process**: After downloading, users need to double-click the executable file to install Ollama on their system.
• 📝 **Running Models**: To run a model like Llama 3, users simply use the command 'ollama run Llama 3' in the terminal, which will download and quantize the model if not already present.
• 🌐 **Local Hosting**: Ollama runs on a local port (e.g., localhost:11434) which can be used for integration with other tools and applications.
• ✅ **Performance**: The video demonstrates that Llama 3 can generate responses quickly, even on a machine with 16 GB of RAM.
• 🔄 **Model Variants**: Meta has released different variants of Llama 3, including the 8B model used in the video, and users can choose which one to run.
• 🚫 **Content Restrictions**: The model is designed to be responsible and avoid generating harmful content, as demonstrated when it refuses to answer a question about creating sulfuric acid.
• 📈 **Integration with LangChain**: Ollama integrates easily with LangChain, allowing users to invoke models and pass messages for processing.
• 📚 **Testing Models Locally**: Instead of deploying on cloud providers, users can test language models locally with Ollama to avoid unnecessary costs.
• 📣 **Community Feedback**: The video encourages viewers to share their experiences and feedback with Llama 3 or other language models in the comments section.

Q & A

What is the main topic of the video?

The main topic of the video is demonstrating how to use Ollama to run the Llama 3 model on a CPU machine.

What is Llama 3?

Llama 3 is the newest release by Meta AI, an open-source large language model (LLM) that has performed well on various evaluation benchmarks.

What is Ollama?

Ollama is a no-code/low-code tool that allows users to load and run large language models (LLMs) locally for inference, as well as to build chat applications.

How can you install Ollama on a Windows machine?

You can install Ollama on a Windows machine by downloading the executable file from the Ollama website and then double-clicking to install it.

What is the minimum RAM requirement to run Llama 3 on a local machine?

The video suggests that you can run Llama 3 on a local machine with limited compute, such as 16 GB or even 8 GB of RAM.

How do you run Llama 3 using Ollama?

To run Llama 3 using Ollama, you open your terminal and type 'ollama run llama-3'. If it's the first time, Ollama will download and quantize the model. Subsequent runs will use the local model.

What does Ollama do with the Llama 3 model when you run it for the first time?

When you run Llama 3 for the first time using Ollama, it downloads the model from Hugging Face, quantizes it, and prepares it for local inference.

What is the significance of the localhost port shown in the video?

The localhost port (e.g., 11434) is significant because it's the port where Ollama is running the Llama 3 model, which can be useful for integrating the model with other tools or applications.

What are the different variants of Llama 3 mentioned in the video?

The video mentions two different variants of Llama 3: the 8B model and the 7B model, released by Meta.

Why might running certain large models like Mixtrax 22B require more computational resources?

Models like Mixtrax 22B are very large and require more computational resources, such as a machine with 128 GB of RAM, to run efficiently.

How does Ollama integrate with LangChain?

Ollama integrates with LangChain by allowing users to invoke the model through LangChain modules like 'chatama' or 'default AMA', passing the model name and then the message for the LLM to process.

What is the creator's advice for those who want to test new LLMs without incurring high computational costs?

The creator advises using Ollama to test new LLMs locally on their own machines, which can save money as opposed to deploying on cloud providers like Runpod, Lambda, or Salesmaker.

Outlines

00:00

🚀 Introduction to LLM Inference with LLaMa-3 using oLlama

The video introduces viewers to LLaMa-3, a new release by Meta AI that has shown strong performance on evaluation benchmarks. The host expresses curiosity about running LLaMa-3 on a CPU machine with limited compute resources, such as 16 GB or 8 GB of RAM. oLlama is presented as a no-code/low-code tool that simplifies the process of loading and inferring large language models (LLMs) locally. The host demonstrates how to download and install the LLaMa-3 model using oLlama, and how to run it on a local CPU machine. The video also touches on the different operating system options available for downloading the model and shows the process of running the model through the terminal with a simple command. Additionally, the host explains how to use the local host URL provided by oLlama for integration with other tools and services.

05:00

🤖 Testing LLaMa-3's Capabilities and Limitations

The host proceeds to test LLaMa-3's capabilities by asking it various questions, including a simple arithmetic question and a more complex linguistic challenge to generate five words starting with 'e' and ending with 'n'. The results are mixed, with the arithmetic question answered correctly, but the linguistic challenge not met satisfactorily. The host also attempts a question on creating sulfuric acid, which LLaMa-3 responsibly refuses to answer, demonstrating the model's adherence to ethical guidelines. The video concludes with the host's recommendation to use oLlama for testing new LLMs locally before deploying them on cloud providers, to avoid unnecessary expenses. The host invites viewers to share their experiences with LLaMa-3 and other LLMs and encourages them to subscribe to the channel for more informative content.

Keywords

💡Ollama

Ollama is a low-code or no-code tool that allows users to load and run large language models (LLMs) locally on their machines. It is particularly useful for those with limited computational resources, such as a CPU machine with 16 GB or 8 GB of RAM. In the video, Ollama is used to run the Llama 3 model, demonstrating how it can be used to perform inference tasks without the need for high computational power.

💡Llama 3

Llama 3 is the latest open-source language model released by Meta AI. It has shown strong performance on various evaluation benchmarks. The video focuses on how to use Ollama to run Llama 3 on a CPU machine, highlighting its capabilities and ease of use for inference tasks.

💡Inference

Inference in the context of machine learning and AI refers to the process of using a trained model to make predictions or decisions based on new, unseen data. The video demonstrates the inference capabilities of Llama 3 when run through Ollama on a CPU machine.

💡CPU Machine

A CPU machine refers to a computer that relies on a central processing unit (CPU) for its primary operations. These machines may have limited computational power compared to those with graphics processing units (GPUs). The video script discusses how Ollama enables users with CPU machines to run powerful language models like Llama 3.

💡RAM

RAM stands for Random Access Memory, which is the type of memory used by a computer to store data temporarily while it is being processed. The video mentions machines with 16 GB or 8 GB of RAM, indicating the types of systems that can run Llama 3 using Ollama.

💡Hugging Face

Hugging Face is a company that provides a platform for developers to use, share, and train machine learning models. In the video, it is mentioned as the source from which the Llama 3 model is downloaded when using Ollama for the first time.

💡Quantization

Quantization in the context of AI models refers to the process of reducing the precision of the model's parameters to use less memory and computational resources. The video script mentions that Ollama automatically quantizes the Llama 3 model to make it more suitable for running on a CPU machine.

💡Lang Chain

Lang Chain is a tool or module that can be used in conjunction with Ollama to facilitate the use of language models. The video discusses how Lang Chain can be integrated with Ollama to invoke the Llama 3 model and perform tasks such as generating responses to prompts.

💡Model Variants

Model variants refer to different versions or configurations of a machine learning model that may differ in size, complexity, or capabilities. The video mentions that Meta has released two different variants of the Llama 3 model: the 8B model and the 7B model.

💡Prompt Injection

Prompt injection is a technique used in AI where specific inputs or 'prompts' are given to a model to guide its output. The video script briefly mentions a prompt injection video, indicating a method to influence the behavior of AI models like Llama 3.

💡Streaming Response

A streaming response in the context of AI refers to the output that is generated in real-time as the model processes the input. The video demonstrates the fast streaming response of Llama 3 when running on Ollama, showing how it can quickly generate text based on a given prompt.

Highlights

The video demonstrates how to use Ollama to run the Llama 3 model on a CPU.

Llama 3 is a new release by Meta AI and has performed well on evaluation benchmarks.

Ollama is a no-code/low-code tool for locally loading and inferring large language models (LLMs).

The video shows how to download and install Ollama for different operating systems, including Windows, Mac, and Linux.

Ollama can be used to run LLMs locally, even on machines with limited compute resources like 16 GB or 8 GB of RAM.

The video provides a step-by-step guide on how to run Llama 3 on a local CPU machine using Ollama.

Ollama can download and automatically quantize the Llama 3 model for efficient CPU inference.

The video shows the process of running Llama 3 by simply using the command 'ollama run Llama 3' in the terminal.

After running the model, users can input prompts to generate responses, with Ollama handling the backend processes.

The video demonstrates the speed of inference, with a good amount of tokens processed per second on a 16 GB RAM machine.

Llama 3's 8B model is used in the demonstration, but Meta has also released a 7B variant.

For higher compute models like Mixtrax 22B or GPT-3, a machine with 128 GB of RAM is recommended for optimal performance with Ollama.

The video explains how to integrate Ollama with LangChain for easy invocation of models and messaging.

Ollama provides an easy way to test LLMs locally without the need for high compute resources or cloud providers.

The host shares insights on the importance of responsible AI usage, noting that Ollama refuses to answer certain questions, like how to create sulfuric acid.

The video concludes with a teaser for an upcoming video on using Ollama with LangChain and a call to action for viewer feedback.

The host encourages viewers to download Ollama and start interacting with LLMs, highlighting the ease of use and practical applications.

The video emphasizes the cost-effectiveness of using Ollama for testing LLMs locally instead of relying on cloud services.