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.