Run Your Own LLM: Ollama and Llama 2 Quick Overview

What’s an LLM and Why you should run it locally?

Let’s discuss setting up and running a local large language model (LLM) using Ollama and Llama 2.

1. What’s an LLM?
   • LLM stands for large language model. These models are powerful at extracting linguistic meaning from text.
   • Ollama is a tool that allows you to run open-source LLMs locally on your machine.
   • Ollama provides access to a variety of open-source models, including bilingual models, compact-sized models, and code generation models.
2. Why Run LLMs Locally?
   • Running LLMs locally has several advantages:
     • Cost: You avoid paying for someone else’s server.
     • Privacy: You can query your private data without security concerns.
   • Ollama makes it possible to run LLMs on your own machine.
3. Installation and Usage:
   • Ollama can be installed on Mac, Windows (as a preview), or via Docker.
   • The article demonstrates running the Llama 2 model locally.
   • The terminal console allows you to interact with the model.
4. Quality and Speed:
   • While local LLMs controlled by Ollama are self-contained, their quality and speed may not match cloud-based options.
   • Building a mock framework for testing can be quicker but tedious.
5. Remember:
   • LLMs are not intelligent; they excel at extracting linguistic meaning.
   • The article provides a fun example of querying the Llama 2 model.

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How to Set up and Run a Local LLM with Ollama and Llama 2

1. Installation:
   • Begin by installing Ollama on your local machine. You can choose from different installation methods, including Mac, Windows (as a preview), or Docker.
   • Follow the installation instructions provided by the Ollama documentation.
2. Selecting a Model:
   • Ollama offers access to various open-source LLMs. Choose the model that best suits your needs:
     • Llama 2: The article demonstrates running this model locally.
     • Bilingual Models: If you need multilingual capabilities.
     • Compact Models: For resource-constrained environments.
     • Code Generation Models: Ideal for developers working with code-related tasks.
3. Running the Model:
   • Once installed, you can interact with the LLM via the terminal console.
   • Use Ollama to query the model, generate text, or extract linguistic meaning from your input.
4. Quality and Speed Considerations:
   • While local LLMs controlled by Ollama provide privacy and cost advantages, their quality and speed may not match cloud-based alternatives.
   • Consider building a mock framework for testing and experimentation.

Where to get started

Documentation | Ollama | Anaconda