Ollama-Run large language models Locally-Run Llama 2, Code Llama, and other models

This video introduces 'Ollama', a tool that allows users to run various open-source large language models locally on their systems. It discusses the benefits of using Ollama for quickly testing different language models for various use cases in generative AI. The video demonstrates the installation process for Ollama on Windows, Mac OS, and Linux, and shows how to run models like Llama 2, Mistral, and Lava. It also covers creating a custom model file for a personalized chat GPT application and using Ollama with APIs and in Jupyter notebooks. The presenter emphasizes the speed and convenience of Ollama for developers looking to integrate large language models into their applications.

Takeaways

• 🤖 Ollama is a tool that allows you to run various open-source large language models locally on your system.
• 🚀 It's beneficial for individuals or developers who want to quickly test different large language models for their generative AI use cases.
• 💻 Ollama supports Windows, Mac OS, and Linux, and the installation process is straightforward, involving a simple download and execution of an .exe file.
• 🔗 Once installed, Ollama runs in the background and can be accessed through a system icon.
• 📚 Ollama also has a presence on GitHub, where you can find instructions for getting started and support for Docker.
• 🚀 You can run models like Llama 2, Mistral, Dolphin, and others using commands such as `AMA run Llama`.
• ⚡ The tool is designed to be fast, providing quick responses after model download and setup.
• 🔧 Users can customize their experience by creating a model file to set parameters like temperature and system prompts for their specific needs.
• 📝 Ollama can be integrated into applications, such as Jupyter notebooks, and used via REST APIs for end-to-end application development.
• 🔄 The tool supports creating custom models and allows for easy switching between different models for various use cases.
• 🌐 Ollama can be accessed through a local URL (http://localhost:11434), enabling the use of any downloaded model in a chatbot-like application.

Q & A

What is the primary purpose of using AMA?

AMA allows users to run different large, open-source language models locally within their system, which can be beneficial for quickly trying various models to find the best fit for specific use cases in generative AI.

How does AMA support different operating systems?

AMA has support for multiple operating systems including Mac OS, Linux, and Windows. Users can download the appropriate version for their OS and install it to start using AMA.

What is the process to download and install AMA on Windows?

To download and install AMA on Windows, users need to click on the download button, select the option for Windows, download the .exe file, and then double-click to install the application.

How can AMA be used to run different language models?

Once AMA is installed, users can run different language models by using the command 'AMA run <model_name>'. For instance, to run Llama 2, the command would be 'AMA run Llama 2'.

What are some of the language models supported by AMA?

AMA supports a variety of language models including Llama 2, Mistral, Dolphin, Neural Chat Starlink, Code Llama, Uncensored Llama 213, Llama 270 billion, Oram Mini, and Lava Gamma.

How does AMA facilitate the creation of custom applications?

AMA allows users to create custom applications by using the models in the form of APIs. It also enables the customization of prompts for specific applications using the supported language models.

What is the significance of using the 'AMA create' command?

The 'AMA create' command is used to create a custom model based on a model file that the user defines. This allows for the creation of personalized language models with specific parameters and system prompts.

How can AMA be integrated into Jupyter Notebooks?

AMA can be accessed in Jupyter Notebooks by using the provided URL (http://localhost:11434) and the LangChain library to call any installed model and generate responses.

What is the benefit of using AMA for local development?

Using AMA for local development allows for faster experimentation with different models without the need for constant internet access or cloud-based resources. It streamlines the process of testing and integrating models into applications.

How does AMA support the development of end-to-end applications?

AMA can be used to create end-to-end applications with platforms like Gradio. It allows for the quick setup of interactive interfaces that utilize the power of large language models for various tasks.

What are the steps involved in creating a custom model with AMA?

To create a custom model with AMA, one must first define a model file specifying the source model, parameters like temperature, and a system prompt. Then, use the 'AMA create' command followed by the app name and the model file name to create the custom model. Finally, run the custom model using the 'AMA run' command with the app name.

Outlines

00:00

🚀 Introduction to AMA and its Benefits

The video introduces AMA, a tool that enables users to run various open-source large language models locally on their systems. AMA is beneficial for those working with generative AI, as it allows for quick testing of different models to find the best fit for specific use cases. The process is straightforward, similar to running a chat GPT application, and supports Windows, Mac OS, and Linux. The video demonstrates the installation process and how to run models using AMA, including accessing models through GitHub and using commands like 'AMA run Lama'.

05:03

🔍 Exploring Different Models with AMA

The speaker discusses the ability to try different models using AMA, highlighting the support for a wide range of models such as Lama 2, Mistral, and Lava. The video shows the process of running a model like Code Lama and the initial download process, which is only time-consuming for the first use. The speaker also demonstrates how to switch between models quickly, emphasizing the flexibility and speed of AMA in handling various open-source models for different use cases.

10:04

🛠️ Creating a Custom Model with AMA

The video explains how to create a custom model using AMA by creating a model file that specifies parameters like temperature for creativity and a system prompt for customizing the model's behavior. The speaker creates a custom chat GPT named 'ml Guru' designed to act as a teaching assistant. The process involves using a command like 'AMA create ml Guru -F model file' to generate a custom model that can be interacted with, showcasing the power of personalization with AMA.

15:06

🔗 Accessing AMA Models via API

The speaker demonstrates how AMA can be accessed through an API by using a specific URL to call any installed model. The video shows integration with Lang Chain and the ability to call custom models like 'ml Guru' directly from a Jupyter notebook or using request.py in a Gradio interface. This approach allows for the creation of end-to-end applications that can leverage AMA's models, making it easier to develop and deploy AI-driven solutions.

20:06

📚 Conclusion and Future Applications

The video concludes with a reminder of AMA's capabilities, emphasizing its utility in handling multiple use cases and the ease of showing results. The speaker encourages viewers to start using AMA and hints at future videos that will cover fine-tuning and end-to-end projects. The video ends with a poem generated by the custom 'ml Guru' model, showcasing its ability to remember context and generate creative content.

Keywords

💡Ollama

Ollama is a tool that enables users to run various large language models locally on their systems. It supports open-source models and is beneficial for individuals who wish to experiment with different models for their use cases in generative AI. In the video, Ollama is showcased as a means to quickly try out and select the most suitable language model for a given application.

💡Large Language Models

Large language models refer to complex AI systems designed to process and generate human-like language. These models are often open-source and can be used for a variety of tasks, including text generation, translation, and more. In the context of the video, the host discusses using Ollama to run different large language models to find the best fit for specific use cases.

💡Generative AI

Generative AI is a branch of artificial intelligence that involves creating new content, such as text, images, or music, that is similar to content created by humans. The video emphasizes the use of Ollama for experimenting with different large language models within the realm of generative AI, allowing for the quick testing of models for various applications.

💡Open Source

Open source refers to software whose source code is available to the public, allowing anyone to view, use, modify, and distribute the software. The video script mentions that Ollama supports open-source large language models, which means users can access and use these models without restrictions, facilitating innovation and experimentation.

💡Windows Support

The term 'Windows Support' in the video refers to the compatibility of Ollama with the Windows operating system. Initially available for Mac OS and Linux, the addition of Windows support broadens the tool's accessibility, allowing more users to download, install, and run large language models locally on their Windows-based systems.

💡Docker

Docker is a platform that enables developers to develop, ship, and run applications in containers. The video mentions Docker in the context of running large language models, suggesting that users can utilize Docker to manage and deploy these models, making it easier to run different models in isolated environments.

💡Llama 2

Llama 2 is one of the large language models supported by Ollama. It is used as an example in the video to demonstrate how users can select and run specific models using the Ollama tool. The host uses 'AMA run Llama 2' to activate and interact with the Llama 2 model to generate content.

💡Custom Prompt

A custom prompt is a user-defined input that guides the language model to generate specific types of responses. In the video, the host discusses the ability to customize prompts for applications using the supported language models, allowing for tailored interactions based on individual needs or use cases.

💡APIs

API stands for Application Programming Interface, which is a set of rules and protocols that allows software applications to communicate and interact with each other. The video explains how Ollama can be used to create APIs for language models, enabling developers to integrate these models into their applications for various purposes.

💡Gradio

Gradio is an open-source Python library used for quickly creating interactive web demos. In the context of the video, Gradio is mentioned as a tool that can be used to create end-to-end applications with Ollama, allowing users to develop interfaces for interacting with the language models in a user-friendly way.

💡End-to-End Application

An end-to-end application refers to a software solution that encompasses all stages of a process, providing a complete workflow from input to output. The video script discusses creating an end-to-end application using Ollama and Gradio, which would allow users to utilize language models in a seamless and integrated manner.

Highlights

Ollama (AMA) allows you to run various open-source large language models locally on your system.

AMA is beneficial for quickly trying different open-source large language models for various generative AI use cases.

The process of using AMA is simple, similar to running a chat GPT application.

AMA has introduced Windows support, in addition to existing support for Mac OS and Linux.

Downloading and installing AMA is straightforward, with an executable file for Windows users.

Once installed, AMA runs in the background with an icon indicating successful installation.

AMA supports a variety of models including Llama 2, Mistral, Dolphin, Neural Chat Starlink, and Code Llama.

AMA provides fast responses once a model is downloaded, making it efficient for testing different inputs.

AMA can be used to create end-to-end applications with platforms like Gradio.

AMA also supports customization of prompts for specific applications using LLM models.

AMA can be utilized in the form of REST APIs, offering integration with web and desktop applications.

The AMA command prompt allows for quick model activation and interaction, such as requesting a poem on generative AI.

AMA enables switching between different models to find the best fit for specific use cases.

Users can create their own model files for custom applications, similar to creating a Dockerfile.

AMA facilitates the creation of a custom chat GPT application with unique parameters and system prompts.

AMA can be accessed through a local URL, allowing for integration with Jupyter notebooks and other applications.

AMA supports calling different models through its API, demonstrated with a request to the custom 'ml Guru' model.

AMA can be used to develop applications that remember context and provide detailed responses to queries.

The video demonstrates the practical applications of AMA in creating custom, interactive AI applications.