I'm currently dipping my toes into Large Language Models (LLMs, or "AI") and what you can do with them. It's a fascinating topic, so expect some more posts on this in the coming days and weeks.

For starters, I wanted to document how I got my first LLM running on my local machine (a 2022 MacBook Pro). Ollama makes this process super easy. You just install it (brew install ollama in my case) and then run the model:

ollama run llama3

This will download the model and open a prompt, so you can start chatting right away!

You can think of Ollama as the Docker CLI but for LLMs. There's a directory of LLMs, and if a model has multiple different sizes, you can use it like you would pull a different docker tag:

ollama pull llama3:8b
ollama pull llama3:70b

The best thing about ollama is that it also exposes a web server for you to integrate the LLM into your application. As an example, here's how you would curl your local LLM:

curl http://localhost:11434/api/chat -d '{
    "model": "llama3",      
    "messages": [{ "role": "user", "content": "Are you a robot?" }],
    "stream": false
{"model":"llama3","created_at":"2024-06-17T11:19:23.510588Z","message":{"role":"assistant","content":"I am not a human, but I'm also not a traditional robot. I'm an artificial intelligence language model designed to simulate conversation and answer questions to the best of my ability. My \"brain\" is a complex algorithm that processes natural language inputs and generates responses based on patterns and associations learned from large datasets.\n\nWhile I don't have a physical body or consciousness like humans do, I'm designed to interact with humans in a way that feels natural and conversational. I can understand and respond to questions, make suggestions, and even tell jokes (though my humor may be a bit... algorithmic).\n\nSo, while I'm not a human or a traditional robot, I exist at the intersection of technology and language, designed to assist and communicate with humans in a helpful way!"},"done_reason":"stop","done":true,"total_duration":12565842250,"load_duration":7059262291,"prompt_eval_count":15,"prompt_eval_duration":331275000,"eval_count":156,"eval_duration":5172858000}

If your local machine is not beefy enough and you want to try out a large LLM on a rented server (AWS has g5.2xlarge, which gave me good results for mixtral 8x7b), you also have to set OLLAMA_HOST= in your environment variables to be able to reach the remote server. This exposes the LLM to the public internet, so be careful when chosing your deployment strategy.

And there you go! You just deployed your very own LLM. Pretty cool, huh?

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