From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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79 lines
2.2 KiB
79 lines
2.2 KiB
WEBVTT |
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I'm so happy to welcome you to week four, day four on our journey to LLM Engineering and Mastery. |
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Here we are looking again at Code Generation. |
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We had tons of fun last time generating code using frontier models, using what ended up being a very |
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sharp UI to allow us to try out different kinds of puzzles and see the startling performance improvement |
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that Claude was able to generate for us winning the day in our competition. |
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What was it more than 60,000 times faster when it reinterpreted the problem and applied Shannon's algorithm? |
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So today we're moving to open source. |
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You're going to be able to use open source models for coding. |
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We're going to use something called hugging face endpoints, which is another feature of hugging Face |
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that lets you deploy models and run them in the cloud privately for you for inference purposes. |
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So it's another great feature of the fabulous resource that is hugging face. |
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And we're going to build a solution that uses open source LMS along with frontier LMS to generate code. |
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Let's see. |
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First, quick reminder of the challenge. |
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I'm sure you remember it. |
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We're building a product that is able to convert C plus, Plus or Python code into high performance |
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C plus plus code. |
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And we used GPT four and Claude last time, uh, the Pi program ran 100 times faster. |
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Um, GPT four optimized the code at least, uh, when it when it ran, it was able to do it with a speed |
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up. |
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Uh, Claude rewrote the algorithm for more than 60,000 times. |
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Performance improvement. |
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Fantastic. |
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Now the time has come to see how open source measures up against the beasts that are the frontier models. |
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Let's have a try. |
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See you in a second.
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