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It's not storing any text? The weights are floating point numbers - the "text" is in some extremely high dimensional embedding space.

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Of course it's storing text. GPT was trained for less than one epoch; they just continually throw new text in there and it mostly just remembers it (= learns it = compresses it). It's not simply "a high dimensional embedding" because words aren't differentiable; you'll get different words if you round off your "coordinates".

If you go to https://beta.openai.com/playground/ and prompt it "Read me the book Alice in Wonderland" it will quote you word for word the original book.

GPT's compression of text is a model of probabilities for the next token in a sequence, where a token is a bit of text from a vocabulary of ~52,000. You can definitely reduce the precision of the parameters that determine that model without hurting the model's overall accuracy much (consider truncating a probability like 98.0000001221151240690% to 98.0%).

Empirically, people have quantized the weights of language models down to INT4 with very little loss in accuracy; see GLM-130B: https://arxiv.org/abs/2210.02414

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