I really hope you've enjoyed this week.
We've got tons done.
We've experimented with all sorts of new techniques and models, and hopefully you've learned a ton
through it all.
Uh, at this point, not only can you code frontier models, including AI assistants, not only can
you choose the right model for your project backed by metrics from leaderboards arenas, but also you
can use frontier and open source models to generate code and as an extra little tool to to add to your
tool belt.
You are also able to deploy models as inference endpoints using Hugging Face's inference endpoint functionality.
So congratulations on all of these skills.
Uh, and uh, perhaps, like me, you're slightly disappointed that open source didn't quite measure
up, but it did a fine job.
We had great fun with it.
And for many tasks of optimizing Python to C plus plus, you would find that Codex would do great.
Um, but when it comes down to it, uh, putting a 7 billion parameter model up against a much more
than 1.76 trillion parameter model, that was GPT four and GPT four and Claude 3.5 sonnet are considered
to be much bigger.
Uh, so it wasn't a particularly fair match.
And in the circumstances, I think Codex did very well indeed.
Next time you're going to be comparing open source and closed source models, performance, talking
about different commercial use cases for code generation, and being able to build solutions that use
this kind of code generation technique for all sorts of tasks.
Uh, looking forward to it.
I'll see you then.