So how does it feel to be 30% of the way down the journey to being a proficient LLM engineer?
Take a moment to congratulate yourself on a big accomplishment and a lot of progress.
And hopefully you have that sense.
You have that feeling that you are up skilling, that you can do so much more than you could just a
matter of days ago, and it's going to keep being that way.
We're going to keep building and building on the skills and knowledge that you're acquiring.
So you're able to do more and more.
But again, what you can already do, you can already confidently code with frontiers.
You can build multimodal AI assistants using tools.
And now and now you're familiar with hugging face pipelines.
And you can use pipelines to run inference tasks across a wide variety of different common machine learning
tasks.
Next time, next time we get below into the lower level Transformers API as we start to work with Tokenizers,
we've of course already spent some time talking about tokens, and we looked at Gpts tokenizer through
the web user interface.
Now we're going to actually use code to translate text to tokens and back again.
And as part of that we're going to understand things like special tokens.
I remember I had a sidebar, uh, ramble about some time ago now, but it's going to all come together.
It's going to be worth it.
That seed I planted is going to come together.
When we look at what tokens look like for what gets passed into an LLM.
And then also when we look at these things called chat templates, all of this is going to be extremely
important foundation material, and I look forward to going through it with you next time.