From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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262 lines
7.8 KiB
262 lines
7.8 KiB
WEBVTT |
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So I hope you've just enjoyed yourself experimenting with different LMS locally on your box using the |
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power of Olama. |
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You've probably had similar experience to me, which is. |
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I certainly found that Quinn 2.5 is perhaps the most powerful model when it comes to grasp of different |
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languages. |
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Some of the other models are better, I think, at explaining. |
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So I'd be very interested to hear your observations. |
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Please do post them with the course or message me direct. |
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I'd love to hear what you've discovered and this kind of experimenting with different models and and |
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finding the one that works best for your your problem. |
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That is a critical skill for an LM engineer. |
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So this was valuable time spent. |
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All right. |
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Let's talk about the next eight weeks. |
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So I am looking to take you from where you are today, over on the left to being a master of LM engineering |
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in eight weeks time. |
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And this is how we'll do it. |
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We'll start this week by looking at models at the frontier of what's possible today, which people call |
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frontier models. |
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Things like GPT 4001 preview and Claude 3.5, and a number of other pioneering models that are closed |
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source and are able to achieve amazing things. |
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And we'll do that through the web user interface like ChatGPT, and also then through the APIs. |
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And we're going to build a commercial project, something immediately that will be useful, and there'll |
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be an interesting commercial exercise for you as well. |
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Then next week we will slap a user interface on top of it using a platform which I love, which is called |
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Gradio. |
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And we will have good fun with it and you'll see that I love it. |
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I go on about it a bit, but it's so easy to use and it's so easy for people like me who are terrible |
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at front end to build a nice, sharp user interface very quickly indeed. |
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We'll do it to solve a classic JNI use case, which is the building an AI assistant, a chatbot, and |
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we'll but we'll do so in a way that has audio and pictures. |
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So it's multimodal and it will be able to use tools, which means that it's able to call out to code |
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running on your computer, which sounds kind of spooky, but it's going to make sense when we do it. |
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So that is all. |
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Week two. |
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In week three, we turn to open source, and we use the ubiquitous Hugging Face platform, which is |
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used by data scientists and LM engineers across the board and will use it to build both. |
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We'll use the simple API in hugging face called the pipelines API, and then we'll use the more advanced |
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API, and we'll explore things like Tokenizers and models in Hugging Face. |
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In week four, we're going to talk about something which is a particularly thorny issue in the world |
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of AI, which is there are so many models to choose from. |
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How do you go about selecting what is the right model for the task you have at hand? |
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So we'll we'll work on things like benchmarks and leaderboards and figure out how do you go about that |
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decision path. |
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And then we're going to take on a particularly different kind of commercial problem about generating |
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code. |
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We're going to build an application which is able to rewrite Python code as C plus plus high performance |
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C plus plus code. |
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And we're going to then try it out with a bunch of closed source and open source models. |
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And one of them will be the winner. |
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The one that's the winner is going to take our test Python code. |
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It's going to rewrite it and the new code is going to run 60,000 times faster, which is shocking. |
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And you will see that yourself. |
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And then there'll be some exercises for you to build other kinds of code generation tools. |
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In week five, we will turn to one of the the topics that is super hot at the moment, which is rag |
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retrieval, augmented generation, using, uh, data stores of information to add expertise to your |
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LLM will be building our own Rag pipeline for answering questions that pertain to an organization. |
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And then there'll be a difficult commercial challenge for you and exercise in which you apply this to |
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your own information. |
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And I'm really excited to see what people make of this, and to see some of your projects of rebuilding |
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a Rag pipeline for yourself. |
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In week six, we begin our three week flagship project for this course. |
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Uh, week six, we will set up the business problem. |
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We'll do a lot of work on data, and we're then going to create some traditional machine learning models, |
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which is very important to do to build a baseline. |
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And then we'll try models at the frontier, and we'll fine tune models at the frontier as well, to |
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do as well as we possibly can with this business problem. |
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In week seven, we'll apply it to open source. |
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We're going to take open source models and they're initially going to perform terribly, and we're going |
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to make it our mission to improve those open source models by fine tuning until at least we can compete |
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with GPT four. |
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The model at the frontier. |
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And I'm not going to tell you what happens, but I will tell you that I believe that the results will |
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astonish you. |
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I will tell you that. |
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So it is very much worth hanging on and seeing what happens in week seven. |
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But then it all comes together in the finale in week eight, which is a fitting conclusion to the eight |
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weeks we are going to build a fully autonomous Agentic AI solution, which will have seven agents collaborating |
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to solve a real commercial problem. |
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And the end. |
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Not only will it be doing something where it scans the internet for various things, but will end up |
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sending you push notifications with some of its discoveries. |
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So it's going to be really fabulous. |
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It's going to have a terrific result at the end of it. |
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It will be a good way to to be a culmination of everything that you've learned each week, building |
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on top of it, of the of the prior week, and resulting in true commercial projects that you'll be able |
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to put into action in your day job right away.
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