From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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130 lines
3.9 KiB
130 lines
3.9 KiB
WEBVTT |
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Welcome, everybody to the last day of week three. |
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Week three. |
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Day five. |
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We're here already wrapping up open source model inference with hugging face. |
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And today, today is the day that you're going pro. |
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Today is the day when we're putting together everything you've learned in the last four days of lectures |
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and really solidifying it with an excellent, uh, juicy project, a business project which is going |
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to give you some, some true experience in the field, what you can do already, if you don't mind me |
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telling you one more time, you can code with frontier models. |
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You can build AI assistants with tools, multi-modality, generating images, making sounds. |
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Uh, and you can use pipelines, tokenizers and models within the hugging face Transformers library. |
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Today, you're going to be even more confident with Tokenizers and models. |
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You're going to be able to run inference across open source models with ease, and you're going to have |
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implemented an LLM solution combining frontier and open source models together into one nice package. |
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There's also going to be a good business challenge for you to keep working on this, so let's get started. |
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The business problem that we have is a feature that is in many applications that we all know, and so |
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it's a good, real kind of product. |
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We want to build a solution that can create minutes of meetings including things like actions and owners |
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and so on. |
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Uh, it will be able to take an audio recording and then use a frontier model, use an API to convert |
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the audio to text. |
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It's actually a task that I had given you as a follow on exercise from one of the projects last week, |
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so you may have already experimented with this, but if not, we're going to do it together. |
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We're going to call a frontier model to convert audio to text. |
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We are then going to use an open source model to turn that text into meeting minutes, summarizing it, |
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plucking out actions and owners and the like. |
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And we will stream back results and show them in markdown. |
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So these these are the activities we're going to do. |
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That's how we're going to put it together. |
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And it's going to build a product that will be useful. |
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This is what we want to come up with. |
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We want to be able to have a solution that produces minutes like this with discussion points, takeaways, |
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action items, and as the input data to start with the resource that we'll be using. |
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There are audio files of publicly available council meetings from councils across the US available on |
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hugging face. |
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And that is where we'll begin. |
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I've already downloaded one of the audio files and taken a chunk out of it. |
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In the interest of time, we'll do just a piece of the Denver City Council meeting rather than the whole |
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meeting. |
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But the idea is that that's going to help us show that it works. |
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And then perhaps this is something that you'll be able to use for your own meetings, for real, when |
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we have a working product. |
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So without further ado, let's go to Google Colab and let's build our application.
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