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We all hear GPT-3 being called a large language model (LLM), but is it really more of a framework since you can use GPT-3 with your own dataset, to train your own version of a GPT-3 model?

My understanding is that a model is the result of training, and you can use one of many frameworks/libraries to train the model (ex: tensor flow). If GPT-3 was just a model, you wouldn't be able to train with your own data on it, right? So that makes GPT-3 a framework?

Can anyone help me to better understand the AI terminology for this?

Rubén
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rekay
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  • This question not look as a good fit for [so]. It might be on-topic in [ai.se] as they question about Artificial Intelligence definitions and terminology are on-topic on that site. It also might be on-topic on other [se] sites. – Rubén Mar 02 '23 at 05:33

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The terminology used is model.

A model in LLM is defined as a mathematical representation of language which is used to make predictions based on probabilities. Basically GPT was trained by turning works (tokens) into mathematical representations. In most cases each work is represented by a 1500 feature array (known in machine learning as a vector).

In the case of GPT-3, the latest model 'davinici-003' uses probability to make predictions on the response it gives based on the training it was provided.

With GPT-3 you can fine-tune the model to perform actions it hasn't been trained on before. It is still referred to as a model even though you can fine-tune it.

Kane Hooper
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