I'm so happy to welcome you to week four, day four on our journey to LLM Engineering and Mastery.
Here we are looking again at Code Generation.
We had tons of fun last time generating code using frontier models, using what ended up being a very
sharp UI to allow us to try out different kinds of puzzles and see the startling performance improvement
that Claude was able to generate for us winning the day in our competition.
What was it more than 60,000 times faster when it reinterpreted the problem and applied Shannon's algorithm?
So today we're moving to open source.
You're going to be able to use open source models for coding.
We're going to use something called hugging face endpoints, which is another feature of hugging Face
that lets you deploy models and run them in the cloud privately for you for inference purposes.
So it's another great feature of the fabulous resource that is hugging face.
And we're going to build a solution that uses open source LMS along with frontier LMS to generate code.
Let's see.
First, quick reminder of the challenge.
I'm sure you remember it.
We're building a product that is able to convert C plus, Plus or Python code into high performance
C plus plus code.
And we used GPT four and Claude last time, uh, the Pi program ran 100 times faster.
Um, GPT four optimized the code at least, uh, when it when it ran, it was able to do it with a speed
up.
Uh, Claude rewrote the algorithm for more than 60,000 times.
Performance improvement.
Fantastic.
Now the time has come to see how open source measures up against the beasts that are the frontier models.
Let's have a try.
See you in a second.