From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
97 lines
2.6 KiB
97 lines
2.6 KiB
WEBVTT |
|
|
|
00:01.400 --> 00:02.960 |
|
What more is there to say, really? |
|
|
|
00:02.990 --> 00:04.820 |
|
Tomorrow is the day for results. |
|
|
|
00:04.850 --> 00:07.610 |
|
A day that very excited indeed about. |
|
|
|
00:07.850 --> 00:08.570 |
|
Uh. |
|
|
|
00:08.570 --> 00:15.920 |
|
But I will mention everything that you can do at this point generating text and code with frontier models |
|
|
|
00:15.920 --> 00:22.550 |
|
and their APIs, open source models through hugging faces, various libraries, tools, assistance, |
|
|
|
00:22.580 --> 00:23.420 |
|
rag. |
|
|
|
00:23.540 --> 00:25.460 |
|
It's all part of your skill set. |
|
|
|
00:25.550 --> 00:32.420 |
|
You can follow the five step strategy I went through to solve problems including curating data, building |
|
|
|
00:32.420 --> 00:39.320 |
|
a baseline model, and fine tuning frontier model with example training sets in those jsonl the JSON |
|
|
|
00:39.320 --> 00:48.530 |
|
lines files that we uploaded, and now and now you can run Q Laura fine tuning for open source models, |
|
|
|
00:48.560 --> 00:52.250 |
|
including defining and selecting your hyperparameters. |
|
|
|
00:52.250 --> 00:56.270 |
|
And then you can run training and you can monitor training. |
|
|
|
00:56.270 --> 01:05.900 |
|
And a great joy it is to uh, so tomorrow, you will be able to run inference on a fine tuned model, |
|
|
|
01:05.900 --> 01:10.340 |
|
which isn't as simple as it sounds, because you can't just load it in. |
|
|
|
01:10.370 --> 01:15.710 |
|
You have to know how to load in the base model and apply these chullora weights on top of it. |
|
|
|
01:15.890 --> 01:16.730 |
|
But it's not that difficult. |
|
|
|
01:16.730 --> 01:17.810 |
|
But but it is. |
|
|
|
01:17.840 --> 01:19.400 |
|
There are a couple of steps to it. |
|
|
|
01:19.850 --> 01:23.480 |
|
Uh, and then, of course, we'll take a moment to look at the results. |
|
|
|
01:23.630 --> 01:31.220 |
|
Um, but most importantly, at that point, you will be able to carry out the end to end process from |
|
|
|
01:31.220 --> 01:37.760 |
|
first idea and thought and definition of the commercial problem through to having a trained model, |
|
|
|
01:37.850 --> 01:45.050 |
|
uh, to build your own proprietary verticalized LLM to solve a business problem. |
|
|
|
01:45.650 --> 01:50.450 |
|
And I was going to try and put a third bullet in there, because I've normally had three bullets against |
|
|
|
01:50.450 --> 01:52.100 |
|
the what you'll be able to do tomorrow. |
|
|
|
01:52.100 --> 01:57.320 |
|
But it seems to me like that second bullet is enough of a mic drop that it's like you don't need a third |
|
|
|
01:57.320 --> 01:57.740 |
|
bullet. |
|
|
|
01:57.740 --> 01:58.820 |
|
That's all you need. |
|
|
|
01:58.820 --> 02:00.830 |
|
That second bullet, it says it all. |
|
|
|
02:00.830 --> 02:02.150 |
|
That's what you're going to be able to do. |
|
|
|
02:02.150 --> 02:03.560 |
|
And it's huge. |
|
|
|
02:03.590 --> 02:04.520 |
|
I'll see you then.
|
|
|