mdda

Or : "The young GPU's illustrated human primer"

You've got to be careful with PDFs : We can't see how they are rendered internally for the LLM, so there may be differences in how it's treating the margin/gutters/bleeds that we should account for (and cannot).

"Make this better in a loop" is less powerful than using evolution on a population. While it may seem like evolution is just single steps in a loop, something qualitatively different occurs due to the population dynamics - since you get the opportunity for multiple restarts / interpolation (according to an LLM) between examples / and 'novelty' not being instantly rejected.

and (of course) the company will record the data so that the robots will be able to learn via imitation learning ASAP

I think the "Aha" is that the RL caused it to use an anthropomorphic tone.

One difference from the initial step is that the second time around includes the initial step and the aha comment in the context : It is, after all, just doing LLM token-wise prediction.

OTOH, the RL process means that it has potentially learned the impact of statements that it makes on the success of future generation. This self-direction makes it go somewhat beyond vanilla-LLM pattern mimicry IMHO.

"Track your potential while staying current" would be more apt

So you're the one in charge of the unix epoch rollover?

Google Colab gives you $free GPU (usually a 16Gb T4) preloaded with frameworks, ready to run. Later, you might be tempted by the Pro(+) version, but there's plenty of scope to move up the learning curve before spending any money.

Could you point to any resources online about how to do this? e.g. is this using 8-bit quantisation?

Isn't that for only one quarter of the A100?

"All models are licensed under CC-BY-NC 4.0" :

So, to clarify, does this mean that companies cannot use these models in the course of business, or is it more about selling the translation results directly?

Could be... Except their page (should you choose to believe it, of course) specifically addresses the advantages:

"""

"Advantages over Traditional GANs" : Thus, we observe that our model exhibits _better training stability_ and mode coverage.

"Why is Sampling from Denoising Diffusion Models so Slow?" : After training, we generate novel instances by sampling from noise and iteratively denoising it _in a few steps_ using our denoising diffusion GAN generator.

"""