A few weeks ago, Meta CEO Mark Zuckerberg announced via Facebook that his company is open-sourcing its large language model (LLM) Code Llama, which is an artificial intelligence (AI) engine similar to GPT-3.5 and GPT-4 in ChatGPT.
Zuck announced three interesting things about this LLM: it’s being open-sourced, it’s designed to help write and edit code, and its model has 70B parameters. The hope is that developers can feed the model more challenging problems, and the engine will be more accurate when it answers.
Also: Why open-source generative AI models are still a step behind GPT-4
The open-sourcing issue is interesting. It’s an approach that implies that you could download the whole thing, install it on your own server, and use the model to get programming help without ever taking the risk that the Overlords of Facebook will hoover up your code for training or other nefarious purposes.
Doing this work involves setting up a Linux server and doing all sorts of hoop jumps. However, it turns out that the specialists at Hugging Face have already implemented the Code Llama 70B LLM into their HuggingChat interface. So, that’s what I’m going to test next.
Getting started with Code Llama
To get started, you’ll need to create a free account on Hugging Face. If you already have one (as I do), you can use the 70B Code Llama LLM with that account.
Also: GitHub: AI helps developers write safer code, but you need to get the basics right
One thing that’s important to note is that, while you could install Code Llama on your own server and thereby not share any of your code, the story is far different on Hugging Face. That service says that anything you type in might be shared with the model authors unless you turn off that option in settings:
When you log in to HuggingChat, you’ll be presented with a blank chat screen. As you can see below, my current LLM is openchat/openchat-3.5-0106, but I’m going to change it to Code Llama — and I’ll show you how.
You change your current model in the settings, which you can get to by hitting the gear icon:
Once in settings, click (at 1) the codellama/CodeLlama-70b-Instruct-hf on the left, verify (at 2) that the Code Llama LLM has been selected, and then click Activate (at 3):
Now, when you talk to the chat interface, you’ll be using the Code Llama model, as verified at the top of the chat interface:
…
My test suite is far from comprehensive. But if Code Llama fails on two of the three tests that didn’t even slow down ChatGPT, it seems like the AI isn’t ready for prime time.
The only reason you might want to use Code Llama over ChatGPT is if you install it on your own server because then your code won’t be shared with Meta. But what good is privacy if the thing doesn’t give correct answers?
If ChatGPT hadn’t been so good, I probably would have given some points to Code Llama. But we know what’s possible with ChatGPT — and Code Llama is far from that level. In short, it looks like Facebook has to Zuck it up and make some improvements.
Also: Implementing AI into software engineering? Here’s everything you need to know
To be honest, I expected better and I’m a little disappointed. But if there’s one thing tech columnists get used to, it’s being a little disappointed by many of the products and projects we look at. I think that’s why we get so excited when something stands out and rocks our world. And Code Llama, unfortunatey, isn’t one of those.
Have you tried any of the AIs for coding help? Which ones have you used? How have they worked out? Let us know in the comments below.
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