Run Llama 3 on CPU using Ollama

AI Anytime
19 Apr 202407:58

TLDRThis video from the AI Anytime channel demonstrates how to use Ollama to run the Llama 3 model on a CPU machine with limited compute resources such as 16 GB or 8 GB of RAM. Llama 3 is Meta's latest open-source language model that has shown excellent performance on evaluation benchmarks. Ollama is a no-code/low-code tool that allows users to load and run language models locally and build applications with them. The video provides a step-by-step guide on downloading and installing Ollama, running the Llama 3 model, and interacting with it through prompts. It also shows how to find the local host port for integration with other tools and using it with Lang Chain. The host tests the model's capabilities by asking various questions, including a complex one about creating sulfuric acid, which the model responsibly refuses to answer. The video concludes by encouraging viewers to download Ollama for easy local testing of language models and to share their experiences and feedback in the comments.

Takeaways

  • 🚀 **Llama 3 by Meta AI**: Llama 3 is the latest open-source language model from Meta AI that has performed exceptionally well on various evaluation benchmarks.
  • 💡 **Ollama for Inference**: Ollama is a low-code tool that allows users to load and run language models locally, making it possible to perform inference on a CPU machine with limited compute resources.
  • 📥 **Downloading Ollama**: The tool is available for different operating systems including Windows, Mac OS, and Linux, and can be downloaded from the official website.
  • 🔧 **Installation Process**: Once downloaded, users need to double-click the executable file to install Ollama on their system.
  • 📝 **Running Llama 3**: To run Llama 3, users can simply use the command `ollama run llama 3` in their terminal, and if it's the first time, the model will be downloaded and quantized.
  • 🌐 **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 speed of inference can be quite fast, with a good number of tokens processed per second, depending on the machine's RAM.
  • 🔄 **Model Variants**: Meta has released two variants of Llama 3, including the 8B model which was used in the video, and users can choose between them based on their needs.
  • 🚫 **Responsible AI**: The model is programmed to avoid answering questions related to sensitive or harmful topics, such as creating sulfuric acid for non-educational purposes.
  • 🔌 **Easy Integration**: Ollama integrates easily with platforms like Langchain, allowing users to invoke models and pass messages for processing.
  • 📈 **Testing New Models**: Instead of deploying new language models on cloud providers, users can test them locally using Ollama to save on compute costs.
  • 📚 **Educational Content**: The video serves as an educational resource for those who are new to using AI models and want to understand how to run them on their local machines.

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 and how does it help with running LLMs?

    -Ollama is a no-code/low-code tool that allows users to load LLMs locally for inference and to build applications without needing high computational resources.

  • How does one install Ollama on a Windows machine?

    -To install Ollama on a Windows machine, one needs to download the executable file from the Ollama website, double-click it, and follow the installation process.

  • What are the system requirements for running Llama 3 on a CPU machine?

    -The system can have limited compute resources, such as 16 GB or 8 GB of RAM, to run Llama 3 on a CPU machine.

  • How does Ollama handle the process of running a new LLM for the first time?

    -If running a new LLM for the first time, Ollama will download the model from Hugging Face, quantize it, and then allow the user to input a prompt to generate a response.

  • What is the significance of the localhost and port number mentioned in the video?

    -The localhost and port number are significant as they indicate where the Ollama server is running. This information can be useful for integrating the model with other tools or for accessing it through a web interface.

  • 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, catering to different computational needs.

  • How does Ollama integrate with LangChain?

    -Ollama integrates with LangChain through modules like 'chatama' and 'default AMA', where users can invoke the LLM and pass messages for processing.

  • What is the video creator's stance on using high-compute resources for testing new LLMs?

    -The video creator advises against using high-compute resources or cloud providers for testing new LLMs due to cost. Instead, they recommend using Ollama for local testing to avoid unnecessary expenses.

  • What is the video creator's advice for users who have their own findings on Llama 3?

    -The video creator encourages users who have their own findings or experiences with Llama 3 to share their thoughts and feedback in the comment section of the video.

  • What is the next step or upcoming content from the video creator?

    -The video creator is planning to release a follow-up video that includes a demonstration of Rag, Ollama, and LangChain.

Outlines

00:00

🚀 Introduction to LLM Inference with LLaMa-3 using Olama

The video introduces the audience to the use of LLaMa-3, a new release by Meta AI, which has shown impressive performance on various evaluation benchmarks. The host shares their curiosity about running LLaMa-3 on a CPU machine with limited compute resources, such as 16 GB or 8 GB of RAM. They explain that Olama is a low-code tool that allows users to load and run large language models (LLMs) locally, making it ideal for those with limited resources. The host guides viewers through the process of downloading and installing the Olama tool for different operating systems and demonstrates how to run LLaMa-3 locally on a CPU machine. They also mention the ease of using Olama with LangChain, a platform for building conversational AI applications.

05:00

🤖 Testing LLaMa-3 Capabilities and Integration with LangChain

The host proceeds to test LLaMa-3's capabilities by asking it various questions, including a simple arithmetic query and a more complex linguistic challenge. They also attempt to ask a question that could potentially lead to harmful information, demonstrating the model's responsible behavior in refusing to provide an answer. The video showcases the ease of using Olama to interact with LLaMa-3 and other LLMs, emphasizing the tool's utility for testing new models without the need for high compute resources. The host encourages viewers to share their experiences with LLaMa-3 and other LLMs and to subscribe to the channel for more informative content.

Mindmap

Keywords

💡Llama 3

Llama 3 refers to the latest release by Meta AI, which is a large language model (LLM) that has demonstrated strong performance across various evaluation benchmarks. In the video, the presenter is interested in running Llama 3 on a CPU machine with limited computational resources, such as 16 GB or 8 GB of RAM. This is significant because it allows users with less powerful hardware to still utilize the capabilities of advanced AI models.

💡Ollama

Ollama is a no-code/low-code tool that enables users to load and run large language models (LLMs) locally on their machines. It is showcased in the video as a means to run Llama 3 on a CPU without needing high computational power. The tool simplifies the process of inference and allows for the integration of LLMs into various applications, making it accessible for users with different levels of technical expertise.

💡Inference

Inference in the context of AI refers to the process of using a trained model to make predictions or generate responses based on new, unseen data. The video demonstrates how to perform inference using Llama 3 through Ollama on a CPU machine. This is important for individuals who want to leverage AI models without the need for extensive computational resources or cloud-based services.

💡Meta AI

Meta AI is the artificial intelligence research division of Meta Platforms, Inc. (formerly known as Facebook, Inc.). They are responsible for developing advanced AI technologies, including the Llama 3 model mentioned in the video. Meta AI's contributions are significant in the field of AI research and development, and their models are often used as benchmarks for the capabilities of current AI systems.

💡CPU

CPU stands for Central Processing Unit, which is the primary component of a computer that performs most of the processing. The video discusses running Llama 3 on a CPU, emphasizing the feasibility of using AI models on machines with limited RAM, such as 8 GB or 16 GB. This is particularly useful for users who do not have access to high-end GPUs or cloud computing resources.

💡RAM

RAM, or Random Access Memory, is the hardware in a computer that temporarily stores data for quick access by the CPU. The video script mentions limitations of 16 GB or 8 GB of RAM when running Llama 3, highlighting the challenges faced by users with lower-memory systems. The ability to run AI models on such systems is a testament to the efficiency of tools like Ollama.

💡Hugging Face

Hugging Face is a company that provides a platform for developers to use, share, and deploy machine learning models, particularly in the field of natural language processing. In the video, it is mentioned as a source from which the Llama 3 model can be downloaded and used with Ollama. Hugging Face's compatibility with Ollama facilitates the process of obtaining and running AI models locally.

💡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 power. The video mentions that Ollama automatically quantizes the Llama 3 model, allowing it to run efficiently on a CPU. This technique is crucial for enabling the execution of large models on devices with limited resources.

💡Lang Chain

Lang Chain is a tool or framework mentioned in the video that allows for the integration of language models into various applications. It is used in conjunction with Ollama to facilitate the use of Llama 3 and other models. The video demonstrates how easy it is to use Lang Chain with Ollama, showcasing the ease of incorporating advanced AI capabilities into different projects.

💡Model Variants

Model variants refer to different versions or configurations of a machine learning model that may differ in size, complexity, or performance. The video discusses the 8B model variant of Llama 3, as well as a 7B variant released by Meta. These variants offer different trade-offs between resource usage and model capability, allowing users to choose the most suitable version for their specific needs.

💡Prompt Injection

Prompt injection is a technique used in AI where specific inputs or 'prompts' are crafted to influence the model's output in a desired way. The video script briefly mentions a prompt injection video related to Llama 3, indicating that there are ways to potentially manipulate or 'jailbreak' AI models to generate responses that might not be intended by the developers. This highlights the ethical considerations and potential risks associated with AI model usage.

Highlights

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

Llama 3 is the latest open-source language model by Meta AI, performing well on evaluation benchmarks.

Ollama is a no-code/low-code tool for local loading and inference of language models.

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

Ollama used to require WSL for Windows, but now has direct support.

Running a language model with Ollama is as simple as using the 'ollama run' command followed by the model name.

If the Llama 3 model isn't present locally, Ollama will download and quantize it automatically.

Ollama provides a local host URL for easy integration with other tools and platforms.

The video showcases the speed and performance of Llama 3 on a machine with 16 GB of RAM.

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

For high-compute models like Ollama ATX 22B, a machine with 128 GB of RAM is recommended.

Ollama integrates easily with Lang Chain, allowing for invocation of language models through commands.

The video highlights the ease of using Ollama for inference of language models, even for first-time users.

An example question about '2+2' is asked to demonstrate the model's response capabilities.

A request for five words starting with 'e' and ending with 'n' is shown, but the response is inaccurate.

The video creator expresses dissatisfaction with the model's response to creating sulfuric acid due to ethical considerations.

Ollama is positioned as a cost-effective way to test language models locally without relying on cloud providers.

The video ends with a teaser for an upcoming video on Rag video with Ama C rant and Lang Chain.

Viewers are encouraged to share their experiences and feedback with Llama 3 in the comments.

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