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Cerebras-GPT: A Family of Open, Compute-Efficient, Large Language Models

cerebras.net

151–160 of 231 posts

Re: Cerebras-GPT: A Family of Open, Compute-Efficient, Large Language Models

#151
post #126

Earlier quoted context omitted.

That's how the brain evolved, after all: take incoming input, process according to previous patterns, output a reaction to peripheral nervous system that will enhance survival. The evolutionary purpose of a cortex is to store a statistical model of an animal's environment based on past stimulus. Human intelligence is just a complicated permutation of these initial systemic parameters.

That’s why people have to take courses in reasoning and logic to be able to successfully apply it. It’s not like reasoning and logic are natural human skills. They’re learned through reinforcement learning by repeatedly exposing our brain to patterns again and again until we can train our stochastic expectation juice to not hallucinate an answer.

And it takes ~20 years to train a new brain so it can coherently answer questions about a wide variety of topics. Even worse, you can't even copy-paste it once you're done!

Re: Cerebras-GPT: A Family of Open, Compute-Efficient, Large Language Models

#152
post #58

> Trained using the Chinchilla formula, these models provide the highest accuracy for a given compute budget. I'm confused as to why 111 million parameter models are trained with the Chinchilla formula. Why not scale up the training data? If you're training smaller models, surely optimizing performance is better than optimizing total compute. Seems like a silly misunderstanding of the Chinchilla paper, but I'm sure I…

The point of those smaller models is for the "Cerebras Scaling Law for Compute-Optimal Training" which is the straight line plot in the image at the top of their webpage when you click the link. They want you to think it's reasonable that because the line is so straight (on a flops log scale) for so long, it could be tempting to extrapolate the pile-loss consequences of continuing compute-optimal training for larger…

> the extrapolation can't continue linearly much further if for no other reason than the test loss isn't going to go below zero

Isn’t the test loss logarithmic? If so it sure can go below zero.

Re: Cerebras-GPT: A Family of Open, Compute-Efficient, Large Language Models

#153
post #151

Earlier quoted context omitted.

That’s why people have to take courses in reasoning and logic to be able to successfully apply it. It’s not like reasoning and logic are natural human skills. They’re learned through reinforcement learning by repeatedly exposing our brain to patterns again and again until we can train our stochastic expectation juice to not hallucinate an answer.

And it takes ~20 years to train a new brain so it can coherently answer questions about a wide variety of topics. Even worse, you can't even copy-paste it once you're done!

It arguably needs much less training data though.

Re: Cerebras-GPT: A Family of Open, Compute-Efficient, Large Language Models

#154

Here are the zero-shot accuracy numbers posted in the Huggingface evaluations for Cerebras-GPT 13B vs. the results of LLaMa 13B in their paper: Model BoolQ PIQA SIQA HellaSwag WinoGrande ARC-e ARC-c OBQA LLaMa 13B 78.1 80.1 50.4 79.2 73 74.8 52.7 56.4 Cerebras-GPT 13B - 76.6 - 51.3 64.6 71.4 36.7 28.6

Have these models been trained on the same dataset? Otherwise it is apples to oranges comparison.

Re: Cerebras-GPT: A Family of Open, Compute-Efficient, Large Language Models

#155
post #131

Earlier quoted context omitted.

Not exactly the same, but from: https://www.instagram.com/p/CQdBiVyh5C2/?hl=en Now that the cat is out of the bag, or, should I say the confetti is out of the… can?

Is it strange that I think it's more likely that LLMs generalize and synthesize new things out of old patterns instead of just copying a singular example.

It’s plausible that the above quote made it a closer association for GPT to come up with.

Re: Cerebras-GPT: A Family of Open, Compute-Efficient, Large Language Models

#157

This type of article (or press release, or whatever you want to call it) is exactly what makes the future so interesting. The cat is out of the bag, the genie is out of the bottle, the confetti has left the cannon[0]. It's tempting to see a world dominated by Google Bard, ChatGPT, Bing Search, etc. And no doubt, they will be huge players, with services that are far more powerful than anything that can be run on the e…

Serious question: is it typical to describe client-side computing as "on the edge"? I thought running something on the edge referred to running it in close network proximity to the user, rather than users having control and running things themselves.

“Edge computing” arguably implies there’s a network you are connected t, that you’re on the edge of, so I wouldn’t apply the term to applications that can function completely offline. With edge computing there’s usually still a notion of having some sort of internet integration, like IoT devices.

Re: Cerebras-GPT: A Family of Open, Compute-Efficient, Large Language Models

#158

Earlier quoted context omitted.

Indeed but this is zero-shot performance. Fine-tuning for a task should get you pretty good results. I'm interested in seeing the results of an Alpaca method against this Cerebras 13B model.

>I'm interested in seeing the results of an Alpaca method You're talking apples to oranges. The "Alpaca method" is a dataset generation method. Nothing about Alpaca's training method is novel, interesting, or efficient. Alpaca used the same standard training method everyone else uses, A100 clusters. If you mean LoRA/PEFT training which people used to replicate Alpaca then that is also apples to oranges because LoRA/P…

One could take the alpaca dataset and fine tune using the LoRA/PEFT method and compare to the Stanford alpaca fine tuned llama model.

Presumably…

Re: Cerebras-GPT: A Family of Open, Compute-Efficient, Large Language Models

#159

FYI: Cerebras's nodes are very different than your typical Nvidia training nodes: https://www.anandtech.com/show/16626/cerebras-unveils-wafer-... Each individual "chip" has 40GB of SRAM vs ~76MB for the Nvidia H100, and networked pools of external RAM, SSDs and such. Thats why the training architecture is so different.

It's a pretty mad architecture tbh. Compile times must be absolutely insane. Also Tesla's Dojo also uses a manufacturing technique that has basically obsoleted their WSI design already.

Re: Cerebras-GPT: A Family of Open, Compute-Efficient, Large Language Models

#160
post #58

Earlier quoted context omitted.

The point of those smaller models is for the "Cerebras Scaling Law for Compute-Optimal Training" which is the straight line plot in the image at the top of their webpage when you click the link. They want you to think it's reasonable that because the line is so straight (on a flops log scale) for so long, it could be tempting to extrapolate the pile-loss consequences of continuing compute-optimal training for larger…

> the extrapolation can't continue linearly much further if for no other reason than the test loss isn't going to go below zero Isn’t the test loss logarithmic? If so it sure can go below zero.

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