课程: Generative AI: Working with Large Language Models
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OPT and BLOOM
- [Instructor] You've probably noticed that up to this point all of the language models are from big tech firms. Now although OpenAI made GPT-3 available via an API, no access was given to the actual weights of the model making it difficult for smaller research organizations and institutions to study these models. The Meta, or Facebook, AI team then released OPT, or Open Pre-trained Transformers. This was a couple of decoder-only pre-trained transformers ranging from 125 million to 66 billion parameters, which they shared with everyone. Interested research teams could also apply for access to the 175 billion parameter model. Now, this effectively gave researchers access to a large language model that was very similar to GPT-3. The Facebook team also detailed the infrastructure challenges they faced, along with providing code for experimenting with the models. This model was primarily trained on English text. The research teams…
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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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