课程: Generative AI: Working with Large Language Models
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Gopher
- [Instructor] The DeepMind research team released Gopher in January of 2022. They released six flavors of the model ranging from 44 million parameters to 280 billion parameters. And they also put together a diverse dataset called MassiveText and then they tested the model on 152 different tasks. Now in the next few minutes, we'll look at each of these tasks in a little more detail. So let's take a look at the architecture first. And you can see it's similar to GPT-3, where you're just using the decoder portion of the transformer. And in their paper, the DeepMind team presented results for the six models with the smallest model being 44 million, all the way to 280 billion parameters. Now, the reason the model sizes increase is because you can see that we're increasing the number of layers, the number of self attention heads, and so on as we move down the table. Now let's take a look at what data Gopher was trained on.…
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内容
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GPT-34 分钟 32 秒
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GPT-3 use cases5 分钟 27 秒
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Challenges and shortcomings of GPT-34 分钟 17 秒
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GLaM3 分钟 6 秒
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Megatron-Turing NLG Model1 分钟 59 秒
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Gopher5 分钟 23 秒
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Scaling laws3 分钟 14 秒
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Chinchilla7 分钟 53 秒
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BIG-bench4 分钟 24 秒
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PaLM5 分钟 49 秒
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OPT and BLOOM2 分钟 51 秒
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GitHub models2 分钟 43 秒
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Accessing Large Language Models using an API6 分钟 25 秒
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Inference time vs. pre-training4 分钟 5 秒
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