Ollama - Local Models on your machine

The video introduces Ollama, a user-friendly tool for running large language models locally on Mac OS and Linux, with Windows support on the horizon. The tool simplifies the installation and operation of various models, including LLaMA-2, uncensored LLaMA, CodeLLaMA, Falcon, Mistral, and others. It provides a command-line interface for managing and running these models, allowing users to download, install, and interact with them easily. The presenter demonstrates how to use Ollama to download a model, create a custom prompt, and interact with it, showcasing its capabilities and potential for non-technical users. The video concludes with a teaser for future content on using Ollama with LangChain and custom models.

Takeaways

• 🦙 Ollama is a tool that allows users to easily install and run large language models locally on their computers.
• 🌐 It currently supports Mac OS and Linux, with Windows support expected to be available soon.
• 📚 Besides LLaMA-2, Ollama supports various models including uncensored LLaMA, CodeLLaMA, Falcon, and Mistral.
• 🚀 Users can run models locally without needing to be proficient in technical aspects like cloud-based model operations.
• 💻 Ollama provides a user-friendly command-line interface for interacting with the installed language models.
• 📝 The tool simplifies the process of downloading, installing, and running large language models for non-technical users.
• 🔍 Users can check the status of their models, including the number of tokens processed per second, with ease.
• 📉 Ollama allows for the creation of custom prompts and hyperparameter settings, enhancing the flexibility of model usage.
• 🚀 The tool can also be used to run LangChain locally, enabling users to test ideas and models more conveniently.
• 📦 Models can be easily added or removed from the system, and the tool manages the associated weights and files.
• 📈 Ollama provides information on the memory requirements for running different models, aiding in system resource management.
• 📌 The video suggests future content on using Ollama with other tools like LangChain and custom models from Hugging Face.

Q & A

What is the name of the user-friendly tool for running large language models locally?

The name of the tool is Ollama.

Which operating systems does Ollama currently support?

Ollama currently supports Mac OS and Linux, with Windows support coming soon.

What is one of the key features of Ollama that the speaker found fascinating?

One of the key features the speaker found fascinating is the ability to very easily install a local model.

What does the speaker plan to do in a future video regarding Ollama?

The speaker plans to make a video on running LangChain locally against all the models supported by Ollama to test out ideas.

How does one get started with Ollama?

To get started with Ollama, one needs to visit their website, download the tool for their operating system, install it on their machine, and then use the command line to run the models.

What is the process of downloading a model in Ollama?

To download a model in Ollama, you run the command to download the model. If the model is not already installed, Ollama will pull down a manifest file and then start downloading the actual model.

How large is the LLaMA-2 model that the speaker downloaded in the script?

The LLaMA-2 model that the speaker downloaded is 3.8 gigabytes in size.

What command in Ollama is used to list the available models?

The command used in Ollama to list the available models is 'Ollama list'.

How can one create a custom prompt in Ollama?

To create a custom prompt in Ollama, you make a model file with the desired system prompt and hyperparameters, then create the model using the Ollama command with the model file as a reference.

What is the advantage of running language models locally with Ollama?

Running language models locally with Ollama allows for easy access and use of the models without relying on cloud services, which can be beneficial for those who are not technical or prefer offline access.

How does the speaker demonstrate the use of a custom prompt in Ollama?

The speaker demonstrates the use of a custom prompt by creating a 'Hogwarts' model file, setting the system prompt to respond as Professor Dumbledore, and then running the model to show how it responds in character.

What is the process to remove a model from Ollama?

To remove a model from Ollama, you use the command to remove the model by specifying its name. If the model's weights are not referenced by any other models, they will be deleted as well.

Outlines

00:00

🌟 Introduction to Ollama and its Features

The speaker begins by sharing their experience at the LangChain offices where they discovered Ollama, a tool designed to run large language models locally. Initially skeptical due to their preference for cloud-based models, the speaker was intrigued by Ollama's ease of installation and potential benefits for non-technical users. Ollama supports various models including LLaMA-2, Mistral, and others, with plans to extend support to Windows. The speaker provides a step-by-step guide on how to download, install, and use Ollama, including how to access the API and command line interface. They also discuss the process of downloading and running models, such as the LLaMA-2 instruct model, and the ability to check model stats and usage.

05:04

📜 Custom Prompts and Model Management with Ollama

In this segment, the speaker delves into the advanced features of Ollama, such as creating custom prompts and managing different models. They demonstrate how to use the tool to generate a coherent text by customizing prompts and switching between models. The speaker also shows how to download and run uncensored models and provides a practical example of creating a custom 'Hogwarts' prompt, where the AI assumes the persona of Professor Dumbledore. Furthermore, they explain how to list, remove, and manage installed models, emphasizing the efficiency of running models locally with Ollama. The speaker concludes by expressing their intent to create more content exploring Ollama's capabilities and encourages viewers to ask questions and engage with the content.

Keywords

💡Ollama

Ollama is a user-friendly tool designed to run large language models on a local machine, currently supporting Mac OS and Linux with Windows support in the pipeline. It simplifies the process of installing and using various language models without requiring extensive technical knowledge. In the video, the speaker discusses their experience with Ollama and its ease of use for non-technical users.

💡Local Models

Local models refer to artificial intelligence or language models that are run directly on a user's computer rather than relying on cloud-based services. This approach can offer benefits such as reduced latency and privacy, as data doesn't need to be sent over the internet. The video emphasizes the advantages of using local models through Ollama for those who may not be comfortable with cloud-based solutions.

💡LLaMA-2

LLaMA-2 is one of the language models supported by Ollama. It is an open-source model that the speaker mentions as being easily accessible through the Ollama platform. The video demonstrates how to download and use the LLaMA-2 model locally, showcasing its capabilities in generating coherent text.

💡Fine-tuning

Fine-tuning is a process in machine learning where a pre-trained model is further trained on a specific task to improve its performance. In the context of the video, the speaker mentions fine-tuning open-source models like LLaMA-2 and Mistral, which can be done locally using Ollama for more personalized model behavior.

💡Command Line

The command line is a text-based interface used to interact with a computer's operating system. In the video, the speaker explains that Ollama operates through the command line, which requires users to input text commands to execute actions such as installing, running, and managing language models.

💡API

An API, or Application Programming Interface, is a set of protocols and tools that allows different software applications to communicate with each other. The video mentions that once Ollama is installed, it creates an API that serves the model, enabling users to interact with the language model as they work.

💡Manifest File

A manifest file is a file that contains metadata about other files in a software package. In the video, the speaker describes how, when a model is not installed, Ollama will first download a manifest file, which then initiates the download of the actual model files, such as the 3.8 gigabyte LLaMA-2 model.

💡Custom Prompt

A custom prompt is a user-defined input that tailors the language model's response to a specific context or character. The video demonstrates creating a custom prompt named 'Hogwarts', where the model assumes the persona of Professor Dumbledore to provide information related to the Harry Potter universe. This feature allows users to generate context-specific responses from the language model.

💡Model Weights

Model weights are the parameters of a machine learning model that have been learned from training data. The video discusses the concept of model weights in the context of deleting models from Ollama. If a model weight is used by multiple models, deleting one model will not remove the weight until all referencing models are also deleted.

💡Censored vs Uncensored Models

Censored models are language models that have restrictions on the type of content they can generate, often to avoid producing inappropriate or harmful text. Uncensored models, on the other hand, do not have these restrictions. The video contrasts the use of a censored LLaMA-2 instruct model with an uncensored chat model, highlighting the trade-offs between safety and freedom of content.

💡Hyperparameters

Hyperparameters are settings of a machine learning model that are not learned from the data but are set prior to the training process. In the video, the speaker sets hyperparameters such as the temperature when creating a custom prompt, which can affect the randomness and creativity of the model's responses.

Highlights

Ollama is a user-friendly tool for running large language models locally on your computer.

Currently supports Mac OS and Linux, with Windows support coming soon.

Offers easy installation of local models, benefiting non-technical users.

Supports various models including LLaMA-2, uncensored LLaMA, CodeLLaMA, Falcon, and Mistral.

Allows for running LangChain locally against these models for testing ideas.

The process begins by downloading Ollama from their website and installing it on your machine.

An API is created to serve the model after installation.

The tool operates through the command line, using Terminal on Mac or a similar application on Linux.

Downloading models, such as the 3.8GB LLaMA-2 model, can take some time.

Ollama provides commands to manage and interact with the installed models.

Custom prompts can be created for specific uses, like a Hogwarts-themed prompt.

The system prompt can be tailored with hyperparameters like temperature.

Models can be listed, run, and removed directly through the command line interface.

Ollama allows for the creation of manifests for custom models with specific settings.

The tool is particularly useful for those who prefer to work with models locally rather than in the cloud.

Ollama is expected to release more features and support for additional models in the future.

The presenter plans to create more videos exploring Ollama's capabilities and integration with other tools.