Using Ollama To Build a FULLY LOCAL "ChatGPT Clone"
TLDRThe video demonstrates how to create a chatbot application using Ollama, an open-source platform for running large language models on your computer. It showcases the ease of downloading and installing Ollama, selecting from a variety of models, and running them in parallel. The tutorial walks through building a chat interface with conversation history, highlighting the speed and efficiency of model switching. The potential for integrating with various platforms and creating a centralized model for different tasks is also discussed.
Takeaways
- 🚀 Ollama is a tool that simplifies running large language models on your computer for creating applications.
- 💻 Ollama currently supports Mac OS and Linux, with a Windows version in development.
- 📥 Users can download models through Ollama and run them in parallel for efficient processing.
- 🎯 Popular open-source models like Code Llama, Llama 2, Mistol, and Zephyr are available on Ollama.
- 🕒 Ollama's performance is impressive, handling model switching swiftly and running tasks in a high-speed manner.
- 🔄 The ability to run multiple models simultaneously allows for task dispatching based on model strengths.
- 🛠️ Customizing model behavior is possible through the creation of model files and adjusting parameters like temperature.
- 📝 Ollama supports various integrations, including web and desktop UIs, terminal integrations, and libraries like Lang chain.
- 💡 The demonstration showcased building a chatbot using Python and the Mistol model, with a simple setup and API call.
- 🔗 Gradio was used to create an interactive front end for the chatbot, allowing for real-time conversation with the model.
- 📖 Conversation history can be managed to give the chatbot context from previous interactions, though limited by token limits.
Q & A
What is the main topic of the video?
-The main topic of the video is to demonstrate how to build a chatbot application, similar to GPT, from scratch using an open-source model with the help of Ollama.
What is Ollama and what does it enable users to do?
-Ollama is a tool that allows users to run large language models on their computers and build applications on top of them. It enables the running of multiple models in parallel.
Which operating systems is Ollama currently compatible with?
-Ollama is currently compatible with Mac OS and Linux, with a Windows version in development.
How can one download and start using Ollama?
-To download and start using Ollama, one needs to visit the Ollama homepage, click 'download now', and then open the downloaded application.
What are some of the open-source models available through Ollama?
-Some of the open-source models available through Ollama include Code Llama, Llama 2, Mistol, Zephyr, Falcon, and Dolphin 2.2.
How does Ollama handle running multiple models simultaneously?
-Ollama can handle running multiple models simultaneously by queuing them up and running them sequentially. The swapping between models is lightning fast.
What is a potential use case for running multiple models with Ollama?
-A potential use case is having the right model for the right task, allowing a centralized model to act as a dispatch model, assigning different tasks to the most appropriate models.
How can users customize the system messages and prompts when using Ollama?
-Users can create a model file to adjust the system messages and prompts. They can set the temperature and define the system prompt within this file.
What are some of the integrations and extensions available with Ollama?
-Ollama offers integrations like web and desktop UIs, a chatbot UI, terminal integrations, and libraries including Lang chain and Llama index. It also supports extensions and plugins like the Discord AI bot.
How does the video demonstrate building a chatbot using Python and Ollama?
-The video demonstrates creating a new Python file, importing necessary libraries, setting up a URL to the local Ollama API, and using the Mistol model to generate responses to user prompts.
What additional feature was added to the chatbot to improve user experience?
-The chatbot had conversation history added to it, allowing it to remember previous messages and provide more contextually aware responses.
Outlines
🚀 Introduction to Building Chatbots with Olama
The paragraph introduces the process of building a chatbot using an open-source model with the help of Olama, a tool that simplifies running large language models on a computer. It highlights Olama's ability to run multiple models in parallel, which was impressive to the speaker. The speaker guides the audience through downloading Olama, which is currently available for Mac OS and Linux, with a Windows version in development. The ease of use and lightweight nature of Olama is emphasized, as well as its capability to handle popular open-source models like Code Llama, Mistol, and others. The speaker also demonstrates the speed and efficiency of running models through Olama and shows how to run multiple models simultaneously, switching between them quickly.
📝 Customizing and Integrating with Olama
This paragraph delves into customizing the Olama experience by adjusting the system message prompt through a model file. The speaker shows how to create and save a model file, change the model, and set the temperature for the response. It also covers how Olama can be integrated with various platforms and tools, such as web and desktop interfaces, chatbot UIs, terminal integrations, and libraries like Lang chain and Llama index. The paragraph emphasizes the ease of use of these integrations and extensions, including a Discord AI bot, and sets the stage for building on top of Olama by creating a new project folder and file.
👾 Creating a Chat GPT Clone with Olama and Python
The speaker begins the process of building a chat GPT clone using Olama and Python. They create a new Python file and import necessary libraries, such as requests and json. The speaker then details the steps to generate a completion by sending a request to a local API, using Mistol 7B as the model. After encountering an error, the speaker corrects the code and successfully receives a response. The paragraph continues with the addition of a gradio front end to allow for browser interaction and conversational capabilities. The speaker also discusses the importance of storing conversation history to enable context-aware responses, demonstrating a method to append and manage conversation history within the code.
Mindmap
Keywords
💡Ollama
💡Language Models
💡Command Line
💡Parallel Processing
💡Integrations
💡Model File
💡Python
💡API
💡Gradio
💡Conversation History
Highlights
The tutorial demonstrates how to build a chatbot using the open-source Ollama platform.
Ollama enables running large language models on your computer for creating powerful applications.
Multiple models can be run in parallel using Ollama, showcasing impressive performance.
Ollama currently supports Mac OS and Linux, with a Windows version in development.
The platform offers a variety of popular open-source models like CodeLlama, Llama 2, Mistol, and more.
Running models with Ollama is as simple as using the command line with the 'ollama run' command.
The speed of switching between models is incredibly fast, enhancing the user experience.
Ollama's ability to handle multiple models simultaneously is ideal for task-specific model dispatching.
Custom model files can be created to adjust settings like temperature and system prompts.
Ollama supports various integrations, including web and desktop UIs, terminal integrations, and plugins.
The tutorial walks through creating a simple Python script to interface with the Ollama API.
Gradio is used to create an interactive front end for the chatbot, allowing web-based conversations.
The chatbot can maintain a conversation history to provide context in responses.
The entire process, from setup to deployment, is demonstrated in a straightforward, step-by-step manner.
The video concludes by encouraging viewers to explore further and build more sophisticated applications with Ollama.
