Getting Started with OLLAMA - the docker of ai!!!
TLDRThe video introduces a tool called 'ol', 'llama', facilitating the local machine operation of large language models (LLMs) like Mistral 7B and Llama 27B. It emphasizes the ease of use, compatibility with Docker-like commands, and the growing model hub reminiscent of Docker Hub. The video demonstrates the process of downloading, running, and interacting with different models, as well as customizing models with specific system prompts. It also highlights the potential of 'ol', 'llama', to serve as a standardized platform for LLMs, with support for web server hosting and various libraries for different programming languages.
Takeaways
- 🚀 The video introduces a tool called 'ol', 'llama', which simplifies the process of running large language models (LLMs) on a local machine.
- 💻 'Llama' is presented as a potential Docker-like hub for LLMs, allowing for easy downloading and management of various models.
- 📋 The script outlines the steps to get started with 'llama', including downloading the application and exploring the available models.
- 📱 'Llama' supports a variety of models, including 'llama 2', 'myal', 'lava', 'code llama', 'dolphin', and more, with new models continually being added.
- 🛠️ 'Llama' follows a command structure similar to Docker, making it familiar to users who have experience with containerization.
- 📈 The video demonstrates how to run an LLM (specifically 'llama 27b') and interact with it by asking questions and performing simple tasks.
- 💡 'Llama' allows users to customize models by creating their own model files, which can be listed alongside other models in the 'llama' ecosystem.
- 🌐 'Llama' hosts a web server, supporting standardization for LLM hosting and enabling interaction through web-based interfaces.
- 🔄 The script showcases the ability to push custom models to a registry, suggesting a community-driven approach to sharing and distributing LLMs.
- 🔧 'Llama' supports the same file format as 'llama CPP', which means it can run any model compatible with this format, including user-generated models.
- 🔗 The video highlights the potential for future development, suggesting that 'llama' could become a central platform for building, sharing, and running LLMs.
Q & A
What is the main topic of the video?
-The main topic of the video is about using a tool called 'ol', 'llama', to run large language models (LLMs) on a local machine.
What are some of the large language models mentioned in the video?
-Some of the large language models mentioned in the video include Mistral 7B, Llama 27B, Llama 2, Code Llama, Dolphin, and Lava.
How does the 'ol', 'llama' tool function in relation to Docker?
-The 'ol', 'llama' tool functions similarly to Docker by using a consistent command structure, allowing users to download and run models on their local machines, and also by using terms like 'manifest' and 'layers' that are familiar to Docker users.
What is the significance of 'ol', 'llama' being referred to as the Docker or Docker Hub of large language models?
-The significance is that 'ol', 'llama' is becoming a centralized hub where users can easily find, download, and run a variety of large language models, much like Docker Hub is a repository for Docker containers.
What are the system requirements for running the Llama 27B model?
-The system requirements for running the Llama 27B model include a hefty amount of disk space (about 3.8 GB) and a significant amount of RAM (around 16 GB).
How does one get started with 'ol', 'llama'?
-To get started with 'ol', 'llama', one needs to download the tool, install it by adding it to their applications, and then use the 'ol', 'llama run' command followed by the model name to run a specific model.
What is the purpose of quantization in the context of large language models?
-Quantization is a technique used to reduce the size of the model within memory, allowing it to run on local consumer hardware more efficiently.
How can users customize models using 'ol', 'llama'?
-Users can customize models by creating their own model files with specific parameters and system prompts. They can use the 'ol', 'llama create' command to build a new customized model.
What web server does 'ol', 'llama' use in the background?
-'ol', 'llama' uses Fast API as its web server in the background.
What are some of the additional features provided by 'ol', 'llama'?
-Additional features provided by 'ol', 'llama' include the ability to host large language models on a web server, push custom models to a registry, and use standardized file formats for model development.
How can developers interact with the 'ol', 'llama' web server?
-Developers can interact with the 'ol', 'llama' web server using various libraries such as JavaScript and Python libraries, which allow them to communicate with the server and use different model types.
Outlines
🚀 Introduction to Running Large Language Models Locally
The paragraph introduces the viewers to the concept of running large language models (LLMs) like Mistal 7B and Llama 27B on a local machine. It discusses previous videos that demonstrated methods using tools such as ll, cpb, nodejs, and python. The focus is on presenting a new, simple method for running LLMs using a tool called 'ol llama', which is emerging as a hub or dock for LLMs. The speaker highlights the ease of use and the availability of various models through 'ol llama', including 'llama 2', 'myal', and 'lava', an image model. The paragraph emphasizes the continuous addition of new models and the command structure similar to Docker, which simplifies the process of running these models on a local machine.
📂 Exploring the 'ol llama' Interface and Model Options
This section delves into the user interface of 'ol llama', showcasing the welcome screen and the process of getting started with LLMs. It explains how to view the list of available models by clicking on the 'models' tab and emphasizes the variety of models that can be run, such as 'llama 2', 'code llama', and 'dolphin'. The speaker also discusses the 'docker'-like command structure and the ease of downloading and running models. The paragraph further explains the process of downloading 'ol llama' for Mac and Linux, and the upcoming availability for Windows. It also touches on the system requirements for running these models, such as disk space and RAM.
🛠️ Customizing and Interacting with 'ol llama' Models
The paragraph demonstrates how to customize and interact with 'ol llama' models. It explains the process of downloading and running models, such as 'llama 27b', and the automatic launching of the model. The speaker shows how to use the 'ol llama' command prompt to ask questions and perform basic tasks like mathematical calculations. It also discusses the 'show info' command, which provides details about the model, including its family, size, and quantization level. The paragraph highlights the compatibility of 'ol llama' with 'llama CPP' and the standardized file format that allows for easy integration of models.
🔄 Creating and Managing Custom 'ol llama' Models
This part of the script focuses on creating and managing custom 'ol llama' models. The speaker guides through the process of creating a new model file based on an existing one, such as 'llama 2', and modifying it with a custom system prompt. The example given involves creating a 'pirate model' that responds in pirate speak. The paragraph also covers the commands for listing, creating, and deleting models, as well as the potential for pushing custom models to a registry for wider use. It concludes with the exploration of 'ol llama' serving as a web server, allowing for interaction with models through APIs and the potential for hosting large language models online.
🌐 Hosting 'ol llama' Models and Future Integration
The final paragraph discusses the capabilities of 'ol llama' for hosting large language models on a web server. It explains how 'ol llama' can be used to serve models through a web interface, similar to Docker, and the convenience of this standardized approach. The speaker also talks about the availability of libraries in different programming languages to interact with the 'ol llama' server, which can be deployed on various cloud platforms. The paragraph concludes with the speaker's enthusiasm for 'ol llama' and its potential, as well as a预告 for future videos that will build upon 'ol llama'.
Mindmap
Keywords
💡Large Language Models (LLMs)
💡OLLama
💡Docker
💡Quantization
💡Model File
💡System Prompt
💡Web Server
💡Docker Image
💡Fast API
💡Node.js
💡Bun
Highlights
Introduction of a new tool called 'ol' for running large language models (LLMs) locally.
ol is becoming a dockor or dock hub for large language models, making it easy to access and run various models.
ollama currently supports running models like llama 2, code llama, and even image model lava, which is open source.
ollama's interface is similar to Docker, with a consistent command structure for running models.
ollama is available for Mac and Linux, with Windows support coming soon.
ollama allows users to download and run models with a simple download button click.
Running an LLM requires a significant amount of disk space and RAM, for example, about 3.8 GB and 16 GB respectively for llama 27b.
ollama's output is fast and can answer questions like 'Who is Ada Lovelace?' efficiently.
Basic math questions can also be answered by the LLM, such as 'What is 2+2?'.
ollama provides detailed information about the models, including family, size, and quantization level.
ollama is built on top of llama CPP, allowing compatibility with models that are compatible with llama CPP.
Users can create their own model files for customization, such as adding a system prompt.
ollama supports the same file format as llama CPP, which is the GG UF file format.
ollama can host large language models on a web server using a standardized web server for different model types.
There are libraries available in different programming languages like JavaScript and Python to interact with the ollama server.
ollama can be deployed using Docker, making it easy to run on various cloud platforms or local machines.
The demonstration of creating a custom 'pirate model' that responds in Pirate speak showcases the customization capabilities of ollama.
ollama's ability to run models locally and host them on a web server makes it a versatile tool for both individual users and developers.