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

The video introduces AMA, a tool that allows users to run various open-source large language models locally on their systems. AMA is beneficial for individuals working with different use cases in generative AI who need to quickly test different models to find the best fit for their application. The process of using AMA is straightforward, similar to running a chatbot application. The tool recently added Windows support and is also available for Mac OS and Linux. Users can download the AMA application, install it, and start using it with a small icon indicating successful installation and background operation. AMA also has a presence on GitHub, where users can find instructions for getting started, model support, and commands for running specific models like Llama 2, Mistral, and others. The video also mentions AMA's speed, its ability to integrate with APIs, and the option for users to customize prompts for their applications.

The speaker demonstrates how to use AMA to try different language models by running commands in the command prompt. After installing AMA, users can switch between models like Llama 2 and Code Llama. The video shows an error due to a typo when trying to run Code Llama, which is then corrected. AMA allows users to download models for the first time, and subsequent uses are faster. The speaker also shows how to use the Lava model and emphasizes the ability to switch quickly between models, which is useful for solving various use cases. The video also discusses creating a custom model file, similar to creating a Dockerfile, where users can set parameters and system prompts to customize their chatbot application. The speaker creates a custom model named 'ml Guru' using Llama 2 as a base and demonstrates its functionality.

The video continues by showing how to create a custom chat GPT application using a model file. The speaker sets parameters such as temperature to make the application more creative and defines a system prompt for a teaching assistant named 'ml Guru'. The assistant is designed to answer questions based on machine learning, deep learning, and generative AI. The speaker then demonstrates how to use the custom model by running it through the command prompt and interacting with the 'ml Guru' model, which responds with information based on the system prompt. The video also touches on the possibility of hosting the custom model in different environments and using it like a chat GPT application.

The speaker explains how AMA, once installed, runs in the background and can be accessed via a local host URL. The AMA tool allows users to call any installed model, such as Llama 2 or a custom model like 'ml Guru', through the local host URL. The video demonstrates how to integrate AMA with LangChain, a library that enables calling different models. It also shows how to use AMA in a Jupyter notebook by importing the AMA module and calling the desired model with a question. The speaker further illustrates how to use AMA through an API by creating a function that sends a prompt to the model and receives a response, which can then be used in a Gradio interface. The video concludes by showing a simple application created with Gradio that uses the AMA API to generate responses.

The video concludes with the speaker expressing hope that the audience found the information on AMA beneficial for their work with generative AI. The speaker emphasizes the ease of using AMA for multiple use cases and the potential to showcase many applications. The video ends with a teaser for upcoming videos that will cover fine-tuning and end-to-end projects using AMA. The speaker also demonstrates the model's ability to remember context and generate poems on specific topics, showcasing the model's capabilities before signing off with well wishes.