Using Ollama To Build a FULLY LOCAL "ChatGPT Clone"

The video script provides a step-by-step guide on how to build a 'Chat GPT Clone' using the open-source model Ollama, which allows for running large language models on a local computer. The tutorial starts with downloading and installing Ollama, then demonstrates running multiple models in parallel and testing their speed and efficiency. It also covers adjusting prompts and creating a customized model profile. The script continues with building a chat interface using Python and Gradio, enabling user interaction with the model through a web browser. Finally, the guide addresses adding conversation history to the model to allow for context-aware responses. The video concludes by inviting viewers to request further tutorials and to engage with the content through likes and subscriptions.

Takeaways

• 🚀 **Ollama Introduction**: Ollama is a tool that allows you to run large language models on your computer, enabling you to build applications on top of them.
• 🌐 **Platform Support**: Ollama is currently available for Mac OS and Linux, with a Windows version in development.
• 🔄 **Model Parallelization**: Ollama can run multiple models in parallel, which was impressively demonstrated in the transcript.
• 📚 **Model Selection**: Users can choose from a variety of popular open-source models, such as Code Llama, Mistral, Zephyr, and Falcon.
• 💻 **Command Line Interface**: Ollama operates primarily through the command line, with a lightweight taskbar icon indicating its operation.
• ⚡ **Performance**: The speed of model execution is highlighted, with examples of rapid response times for tasks like joke-telling and essay writing.
• 🔄 **Model Swapping**: The ability to switch between models quickly is showcased, with a demonstration of running Mistral and LLaMa 2 simultaneously.
• 📝 **Customization**: Users can adjust the prompt and other settings of the model through a model file, allowing for tailored responses.
• 🤖 **Integrations**: Ollama offers various integrations, including web and desktop interfaces, libraries, and extensions for platforms like Discord.
• 🛠️ **Building Applications**: The transcript includes a step-by-step guide to building a chat GPT clone using Python and a Gradio front end.
• 🔗 **Conversation History**: The importance of maintaining conversation history for context in subsequent interactions is discussed and implemented in the example application.

Q & A

What is Ollama and how does it help in building applications?

Ollama is a tool that allows users to run large language models on their computers. It facilitates the development of applications by enabling the running of multiple models in parallel, which can significantly enhance the performance and capabilities of the applications being built.

Which operating systems is Ollama currently available for?

As of the time the transcript was written, Ollama is available for Mac OS and Linux. A Windows version is in development and is expected to be released soon.

How can one get started with Ollama?

To get started with Ollama, one needs to visit the Ollama homepage, click on 'download now', and follow the instructions to open the application. Once opened, a small icon will appear in the taskbar, and further operations are conducted through the command line or the Ollama interface.

What are some of the popular open-source models available on Ollama?

Some of the popular open-source models available on Ollama include Code Llama, Llama 2, Mistral, Zephyr, Falcon, and Dolphin 2.2. The platform is continuously adding more models to its roster.

How can one run a model using Ollama?

To run a model using Ollama, one needs to open a command line interface, type 'ollama run' followed by the model name they wish to run. If the model is not already downloaded, Ollama will download it for the user.

What is the significance of being able to run multiple models in parallel?

Running multiple models in parallel allows for the efficient handling of different tasks simultaneously. It enables users to have the right model for the right task, acting almost like a dispatch model that can distribute tasks to the most appropriate models.

How fast is the response time when using Ollama?

The response time when using Ollama is described as 'blazing fast' in the transcript, which is a function of both Ollama's efficiency and the power of the models being used.

What is the purpose of creating a model file in the script?

A model file is created to define the settings and characteristics for a specific model run. It allows users to adjust parameters such as the temperature and set custom system prompts for the model to follow.

How does Ollama handle model swapping?

Ollama can swap between different models very quickly, with the process taking approximately 1 and a half seconds in the example provided. This allows for seamless transitions between different models during a session.

What is the role of the 'stream' parameter in Ollama?

The 'stream' parameter in Ollama determines whether the response from the model is returned as a continuous stream of JSON objects or as a single, complete response. Setting 'stream' to false returns the entire response at once.

How does the script demonstrate the creation of a chat GPT clone?

The script demonstrates the creation of a chat GPT clone by using Python to send a request to the local Ollama instance, receiving a response, and then using Gradio to create a user interface for the chat application. It also includes the handling of conversation history to allow for context in subsequent responses.

What are some of the integrations and extensions available with Ollama?

Ollama offers a variety of integrations and extensions, including web and desktop interfaces like an HTML UI and a chatbot UI, terminal integrations, libraries such as Lang chain and Llama index, and plugins like the Discord AI bot.

Outlines

00:00

🚀 Introduction to Building Chat GPT with Olama

The speaker introduces the process of building a chat GPT from scratch using open-source models. They highlight Olama as a user-friendly tool for running large language models on a personal computer and for creating applications on top of these models. Olama's capability to run multiple models in parallel is demonstrated, along with a step-by-step guide on downloading and using Olama, including accessing available models and running them through the command line. The speed and efficiency of running models like Mistral and LLaMa2 are showcased, emphasizing the potential for using the right model for the right task.

05:00

📚 Customizing and Running Multiple Models

The speaker demonstrates how to customize the system prompt and adjust the temperature for model responses. They also show how to create a model file for a specific character, like Mario from Super Mario Brothers, and how to run this model using Olama. The video then transitions into building a chat GPT clone using open-source models. The process includes creating a new Python file, importing necessary libraries, setting up a URL for local API calls, and crafting a request to generate responses from the model. The speaker also addresses the challenge of handling streamed responses and parsing JSON data to extract the desired information.

10:01

💬 Creating a Conversational Interface with Gradio

The speaker proceeds to build a front end for the chat GPT clone using Gradio, allowing users to interact with the model through a browser interface. They modify the code to include a 'generate response' method, which consolidates the process of generating responses from the model. The video also covers how to enable back-and-forth conversations by storing and appending conversation history to the prompts given to the model. This ensures that the model has context from previous messages, which is crucial for a coherent dialogue. The speaker concludes by encouraging viewers to suggest further enhancements and to provide feedback in the comments.

Keywords

Ollama

Ollama is a software application that enables users to run large language models on their personal computers. It is highlighted in the video for its ease of use and capability to power multiple models in parallel, which is crucial for building applications on top of language models. It is used as the foundation for creating a 'ChatGPT Clone' in the video.

Large Language Models

Large Language Models (LLMs) are artificial intelligence models that are trained on vast amounts of text data to generate human-like language. They are the core technology behind applications like chatbots and content generators. In the video, various LLMs are mentioned, such as Mistral and Llama 2, which are used to demonstrate the capabilities of Ollama.

Command Line

The command line is a text-based interface used to interact with a computer's operating system. In the context of the video, the command line is used to execute commands for running and managing language models through Ollama, showcasing its versatility and power for developers.

Parallel Processing

Parallel processing refers to the ability of a system to execute multiple tasks or processes simultaneously. The video emphasizes Ollama's ability to run multiple models in parallel, which significantly enhances the performance and utility of the application being developed.

API

An API, or Application Programming Interface, is a set of protocols and tools that allows different software applications to communicate with each other. In the video, the local computer's API is used to generate responses from the language models, which is a key part of building the 'ChatGPT Clone'.

Gradio

Gradio is an open-source Python library used for quickly creating web interfaces for machine learning models. In the video, Gradio is used to build a user-friendly front end for the chat application, allowing users to interact with the language model through a web browser.

Conversation History

Conversation history refers to the record of past exchanges within a dialogue. The video discusses the importance of maintaining conversation history to enable context-aware responses from the language model, which is essential for creating a more natural and interactive user experience.

Token Limit

The token limit is the maximum number of tokens (words or characters) that a language model can process at one time. It is mentioned in the context of the limitations when storing conversation history, as the model can only handle a certain amount of information before it needs to be truncated.

Stream

In the context of the video, 'stream' refers to the process of receiving data in a continuous flow, as opposed to all at once. The video shows how to adjust the streaming of JSON objects from the API response to better handle the data for the application.

JSON

JSON (JavaScript Object Notation) is a lightweight data-interchange format that is easy for humans to read and write and easy for machines to parse and generate. In the video, JSON is used as the format for the API response, which contains the information generated by the language model.

Model File

A model file in this context is a configuration file that defines the settings and parameters for a specific language model. The video demonstrates how to create and use a model file to customize the behavior of the language model, such as adjusting the temperature or setting a custom system prompt.

Highlights

Ollama is a tool that allows running large language models on your computer locally.

It supports running multiple models in parallel, which is impressive for performance.

Ollama is currently available for Mac OS and Linux, with a Windows version in development.

The application is lightweight and operates primarily through the command line.

Popular open-source models like Code Llama, Mistral, Zephyr, and Falcon are available through Ollama.

Demonstration of running the 'nral' model and its quick response time.

Simultaneous running of Mistral and LLaMa 2 models showcasing the software's capabilities.

The ability to switch between models in about 1.5 seconds is a significant feature.

Use case of having the right model for the right task, acting as a dispatch model.

Integration of Ollama with AutoGen for running multiple models on the same computer.

Adjusting the system prompt and temperature settings through a model file.

Creating a model file to customize the model's behavior, such as making it respond as Mario from Super Mario Brothers.

Ollama offers numerous integrations including web and desktop UIs, libraries, and plugins.

Building a chat GPT clone using Python and Ollama to generate responses.

Using the Mistral model to assist in writing code for the chat GPT clone.

Incorporating a Gradio front end for a browser-based interface.

Adding conversation history to the model to allow for context in responses.

Successfully creating a chat GPT clone that remembers previous messages in a conversation.

The entire process was done from scratch, showcasing the power of Ollama and open-source models.