From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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283 lines
7.6 KiB
283 lines
7.6 KiB
WEBVTT |
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That concludes a mammoth project. |
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Three weeks in the making. |
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In the course of those three weeks, starting with the data curation, the working with frontier models, |
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and then ending with the complete user interface in Gradio with even that very unnecessary but great |
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plot 3D chart on the bottom right, together with the trace from the agents and the results of the memory. |
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Um, and all of it came together. |
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Remember, the user interface was not really the point. |
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The user interface was a bit extra, so we can monitor it. |
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The main point is that that just runs and it just keeps running, and as it runs, it will be continually |
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notifying me every few minutes with a new deal that it hasn't surfaced before. |
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And that is the conclusion of a very satisfying, great project that we have built end to end. |
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And so with that, let's take a moment to do a retro of the last eight weeks. |
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One more time. |
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I've gone on about this so many times, and so you'll be happy, or maybe a bit sad that this is the |
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final retrospective you started eight weeks ago. |
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Over on the left, we wanted to get to being an LM engineer, someone who had mastered LM engineering, |
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highly proficient, advanced, and this is how we got there. |
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In the first week we played with lots of models. |
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We found out how many times the letter A appeared in sentences. |
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We took a quick look at one preview amongst other things. |
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In week two we first saw gradient and we played with some multi-modality, which was fun. |
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We also saw an early version of agent ization. |
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In week three we got stuck into hugging face. |
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We had pipelines, we had tokenizers, we had models. |
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In week four we got deeper into hugging face. |
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We selected Llms, we generated code and we had that remarkable project, with the 60 000 time performance |
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improvement in week five. |
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We built our Rag solution for insert film, which used chroma, and also briefly we used face as well |
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and created our expert. |
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And maybe you did the big project associated with that. |
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Week six we fine tuned a frontier model, although most of which six was spent curating data. |
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But that is such an important activity. |
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Week seven. |
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We did fine tune an open source model that then beat the frontier, and in week eight, we packaged |
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it together to a genetic AI solution complete with seven agents and a user interface. |
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And it was fabulous. |
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So that was the journey. |
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I need to take a moment to thank you. |
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Thank you so much for staying through to the end. |
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You can't, I can't I can't explain how much I appreciate it. |
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It's really, uh, so, so wonderful to have had people come all the way through the course, gone through |
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the eight week journey, and take advantage of everything that we've been doing and get to this point. |
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And I've, I've heard from several of you along the way, and it's been really, really rewarding for |
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me to experience this. |
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Uh, super grateful. |
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I hope you've enjoyed it. |
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I obviously I've enjoyed it a lot. |
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Uh, far too much. |
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I hope you've enjoyed it as well. |
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I really hope you can stay in touch. |
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By all means, please do LinkedIn with me if you're open to that. |
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If you're okay with that, I'm very much welcome. |
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LinkedIn connections. |
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And we can have a community, um, and message me if you've got to this point, I definitely want to |
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hear it. |
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Uh, and of course, you've got this big challenge. |
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Now, can you take what you've learned and use it to build your own project? |
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Maybe that idea I had about using the finance data would be an interesting one. |
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See if you can build something that could make some money. |
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If you do, then I expect a lunch out of it. |
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At the very least, perhaps. |
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Uh, but that would be a fun challenge. |
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Whatever you do with it, I want to hear about it. |
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If you built a great platform that is using some of this learning, then please share it. |
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I'd love other students to see that too, and it's great to have that kind of output as a tiny little |
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extra. |
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I don't know if you remember all the way, way, way, way back in week one, I did mention there was |
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going to be a little extra juicy nugget at the very end, and this is what it is. |
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I wanted to tell you that I did a personal project where I fine tuned an LLM. |
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It was in fact a llama two LLM from the beginning of this year, uh, on all of my text message history. |
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It turns out that I have 240,000 text messages that have built up over time on my iPhone, uh, since |
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I had the first iPhone some time ago. |
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And so I had a lot of text message history, and I was able to use that to train llama two to make a |
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simulation of me. |
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Uh, and there's a write up on my website, on my blog, Edward Dot com. |
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You can take a look and there's instructions for how you can do it too. |
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And of course, I was using llama two and now llama 3.1 is so much better along with some of the others |
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like like Kwan and so on. |
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So you could definitely have a stab at this, and you will probably have even better results than I |
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had. |
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And the results I had were spooky. |
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They really were very good indeed. |
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And so with that, I have to bring up the final slide. |
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Congratulations. |
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I hope that you're proud of what you've accomplished. |
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I hope you do feel that sense that you have now reached an advanced point in your learning. |
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You have got to the summit of the mountain. |
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I'm so, so very happy. |
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And I hope, I really, really hope that you're able to take this and use it in your day job, in your |
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career to move forwards. |
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And I very much want to hear all about it. |
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So do stay in touch. |
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Thank you once again. |
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And a huge congratulations.
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