WEBVTT

00:01.370 --> 00:08.900
And once again, it's that moment when you take a pause and congratulate yourself on another day of

00:08.900 --> 00:17.270
skills learned and fantastic achievements of being able to be an expert in the hugging face Transformers

00:17.270 --> 00:18.110
library.

00:18.110 --> 00:22.640
In addition to using pipelines and tokenizers, you can now use models.

00:22.640 --> 00:29.120
You can look at models, you can load different models, and you can run models to do hopefully more

00:29.120 --> 00:36.260
than just tell jokes, but also other kinds of text generation tasks like the ones we've done in previous

00:36.260 --> 00:37.250
weeks.

00:37.340 --> 00:44.420
Uh, you, uh, also can, of course, code confidently with frontier model APIs and build AI assistants,

00:44.420 --> 00:48.320
including multimodal AI assistants, and use tools.

00:48.320 --> 00:55.820
So all of this together, uh, totals a significant amount of learning that you've done already, with

00:55.820 --> 00:58.250
a lot more exciting stuff ahead.

00:58.520 --> 01:03.890
The next session, we're going to do one more project with Tokenizers and models, just to give you

01:03.890 --> 01:05.720
a little bit more experience.

01:05.810 --> 01:12.500
Uh, and we're also going to yeah, just keep keep running inference on open source models and implement

01:12.500 --> 01:19.520
an LLM solution that's going to combine a frontier model call with an open source model call.

01:19.520 --> 01:22.610
And it will be a useful business application.

01:22.610 --> 01:28.400
And it's going to really wrap up this week of learning about hugging face and open source.

01:28.400 --> 01:30.140
So looking forward to it.

01:30.140 --> 01:31.220
I will see you then.