From the course: Generative AI: Working with Large Language Models
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Accessing Large Language Models using an API
From the course: Generative AI: Working with Large Language Models
Accessing Large Language Models using an API
- [Instructor] Now in the previous video, we looked at comparing large language models using GitHub models. Now in this one, we look to see how we can access the large language models via an API using a programming language like Python. Now I prefer GitHub Codespaces for prototyping any apps. It's a cloud-based coding environment and you can access it from any device. And what I like about it is I don't waste any time with setting things up. I'm going to demonstrate the step-by-step process for setting up a GitHub repo and using Codespaces to work with GitHub models. You can also go directly to the course GitHub repo to see the configuration already in place. I've also included the coding files in the repo for you. So let's kick things off with creating a GitHub repo. So I'm going to head over to github.com, and this is my GitHub repo. And I'm going to head over to the top and select Create a New Repository. And I'm going to call this repository Joke. I'm going to leave it as public…
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Contents
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GPT-34m 32s
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GPT-3 use cases5m 27s
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Challenges and shortcomings of GPT-34m 17s
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GLaM3m 6s
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Megatron-Turing NLG Model1m 59s
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Gopher5m 23s
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Scaling laws3m 14s
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Chinchilla7m 53s
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BIG-bench4m 24s
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PaLM5m 49s
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OPT and BLOOM2m 51s
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GitHub models2m 43s
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Accessing Large Language Models using an API6m 25s
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Inference time vs. pre-training4m 5s
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