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| "text": "Now foundation models are pre trained on large amounts of unlabeled and self supervised data.", | |
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| "text": "Meaning the model learns from patterns in the data in a way that produces generalizable and adaptable output.", | |
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| "text": "And large language models are instances of foundation models applied specifically to text and text like things I'm talking about things like code.", | |
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| "text": "Now, large language models are trained on large data sets of texts such as books, articles, and conversations.", | |
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| "text": "And look, when we say large, these models can be 10s of gigabytes in size and trained on enormous amounts of text data.", | |
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| "text": "We're talking potentially petabytes of data here.", | |
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| "text": "So to put that into perspective, a text file that is let's say 1 GB in size, that can store about 178,000,000 words, a lot of words just in 1GB.", | |
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| "text": "And how many gigabytes are in a petabyte?", | |
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| "text": "Well, it's about 1,000,000.", | |
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| "text": "Yeah, that's truly a lot of tech.", | |
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| "text": "LLMS are also among the biggest models when it comes to parameter count.", | |
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| "text": "A parameter is a value the model can change independently as it learns.", | |
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| "text": "And the more parameters the model has, the more complex it can be.", | |
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| "text": "GTP 3 for example is pre trained on a corpus of actually 45 terabytes of data and it uses 175 billion ML parameters.", | |
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| "text": "All right, so how do they work?", | |
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| "id": 23, | |
| "text": "Well, we can think of it like this.", | |
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| "text": "LLM equals three things, data architecture, and lastly, we can think of it as training.", | |
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| "text": "Those three things are really the components of an LLM.", | |
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| "text": "Now we've already discussed the enormous amounts of text data that goes into these things.", | |
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| "text": "As for the architecture, this is a neural network and for GPT that is a transformer, and the transformer architecture enables the model to handle sequences of data like sentences or lines of code.", | |
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| "text": "And Transformers are designed to understand the context of each word in a sentence by considering it in relation to every other word.", | |
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| "text": "This allows the model to build a comprehensive understanding of the sentence structure and the meaning of the words within it.", | |
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| "text": "And then this architecture is trained on all of this large amount of data.", | |
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| "text": "Now, during training, the model learns to predict the next word in a sentence.", | |
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| "text": "So the sky is.", | |
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| "text": "It starts off with a with a random guess, the sky is bug.", | |
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| "text": "But with each iteration, the model adjusts its internal premises to reduce the difference between its predictions and the actual outcomes.", | |
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| "text": "And the model keeps doing this, gradually improving its word predictions until it can reliably generate coherent sentences.", | |
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| "id": 36, | |
| "text": "Forget about bug, it can figure out it's blue.", | |
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| "text": "Now the model can be fine-tuned on a smaller, more specific data set.", | |
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| "text": "Here the model refines its understanding to be able to perform this specific task more accurately.", | |
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| "text": "Fine tuning is what allows a general language model to become an expert at a specific task.", | |
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| "text": "OK, so how does this all fit into #3 business applications?", | |
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| "text": "Well, for customer service applications, businesses can use LLMS to create intelligent chat bots that can handle a variety of customer queries, freeing up human agents for more complex issues.", | |
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| "id": 42, | |
| "text": "Another good field?", | |
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| "text": "Content creation that can benefit from LLMS, which can help generate articles, emails, social media posts, and even YouTube video scripts.", | |
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| "text": "There's an idea now LLMS can even contribute to software development, and they can do that by helping to generate and review code.", | |
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| "text": "And look, that's just scratching the surface.", | |
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| "text": "As large language models continue to evolve, we're bound to discover more innovative applications.", | |
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| "text": "And that's why I'm so enamoured with large language models.", | |
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| "text": "If you have any questions, please drop us a line below and if you want to see more videos like this in the future, please like and subscribe.", | |
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| "text": "Thanks for watching.", | |
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