Running LLM Locally: A Beginner’s Guide to Using Ollama
Introduction
As the use of large language models (LLMs) grows, many are looking for ways to run them locally, which ensures data privacy, reduces dependency on cloud resources, and enables offline accessibility.
- Data Privacy: Sensitive data remains on your device, crucial for industries where confidentiality is paramount.
- Customization: Local deployment allows custom modifications to models.
- Offline Capability: Once downloaded, models can run without the internet.
There are several ways to run LLMs locally, as outlined in this DataCamp tutorial. This blog will be the first in a series, focusing on setting up Ollama and using its core features.
Ollama, a platform for managing and deploying LLMs locally, is a great solution for developers wanting to experiment with or deploy custom AI models on their devices.
What is Ollama?
Ollama offers a robust environment for running, modifying, and managing various LLMs, including models like Llama, Phi, and others optimized for different tasks. It supports multiple operating systems, making it accessible for users across macOS, Linux, and Windows. This flexibility allows developers to experiment with open-source and custom models without cloud reliance.
Getting Started with Ollama
Step 1: download and Install Ollama
To begin, download the Ollama software from their official website.
Ollama provides a straightforward command-line interface(CLI), allowing users to load, configure, and run models directly from their machines.
Step 2: Pull The Model(s)
After downloading Ollama, Pull the model from https://ollama.com/library using CLI.
# ollama pull <model> e.g. llama3.2
ollama pull llama3.2Step 3: Run the model
# ollama run <model> e.g. llama3.2
ollama run llama3.2Step 4: Interact with the LLM
4.1 CLI
You can now interact with the LLM directly through the command-line interface (CLI).
Find all the CLI commands at https://github.com/ollama/ollama?tab=readme-ov-file#cli-reference
4.2 Web UI
Not a fan of chatting with the command line? Unless you’re a developer who dreams in code, Ollama has you covered with a REST API!
Now, you can seamlessly integrate LLM capabilities into your web apps without touching the CLI. Just fire up ollama serve to run Ollama without the desktop app.
I’ve also created a simple React app using Ollama’s REST API for straightforward LLM interactions. Check out the code on GitHub here: llm-chat-app.
4.3 Python
To run Ollama in Python, you can use the langchain_community library to interact with models like llama3.2. Here’s a quick setup example:
from langchain_community.llms import Ollama
# Initialize Ollama with your chosen model
llm = Ollama(model="llama3.2")
# Invoke the model with a query
response = llm.invoke("What is LLM?")
print(response)Step 5: Exploring Advanced Features
Beyond running basic commands, Ollama enables model customization, which allows users to tweak parameters and create specific use-case optimizations. This feature is invaluable for developers aiming to use models in specialized fields.
Disclaimer
The performance of this application depends heavily on your device’s computing power. Running large language models (LLMs) locally can be resource-intensive, and response times may vary based on your machine’s CPU, GPU, and memory capacity. Machines with higher specifications will yield faster, smoother interactions, while devices with limited processing power may experience slower responses.
