Request: pretrained distilgpt2-medium, distilgpt2-large models
# Plans for distilgpt2-medium and distilgpt2-large

## Motivation
While distilgpt2 is useful, I was wondering if there are any plans to create a distilgpt2-medium and distilgpt2-large. I'm also wondering how the result of distilgpt2-medium compare to gpt2, and distilgpt2-large compare to gpt2-medium, in size and performance.

Maybe it's not even worth it to have those pretrained, if distilgpt2-medium is larger than gpt2 and perform worse.

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<|||||>I'd also be interested in this. The current distilgpt2 is great for use-cases that need cheap/fast compute, but distilled versions of the larger gpt2 models (medium, large, xl) would also be super useful. For example, I am able to fit up to gpt2-large on my GPU, but I'm unable to fit gpt2-xl, which means I can't use it. If there was a distilled version of gpt2-xl which was smaller, that might make it usable for more people.

Are there any plans to distill any larger versions of gpt2?

Thanks!<|||||>Yes we can probably work on that.
There is a bit of work + exploration to do: it is possible that we'll have to use model parallelism tricks to be able to train it in a reasonable time (I haven't checked yet).
Applying the distillation to gpt2-xl the way we did for distilgpt2 (same ratios) would still result in a model that is bigger than gpt2-medium (24L, 1600 hidden dim). Would that fit your use-case?

(sorry for the delayed answer, I don't usually check issues without being pinged/tagged).<|||||>> Applying the distillation to gpt2-xl the way we did for distilgpt2 (same ratios) would still result in a model that is bigger than gpt2-medium (24L, 1600 hidden dim). Would that fit your use-case?

Yes, if we could squish the performance of gpt2-xl into something sized between gpt2-medium and gpt2-large, that would be really useful!<|||||>> Yes we can probably work on that.
> There is a bit of work + exploration to do: it is possible that we'll have to use model parallelism tricks to be able to train it in a reasonable time (I haven't checked yet).
> Applying the distillation to gpt2-xl the way we did for distilgpt2 (same ratios) would still result in a model that is bigger than gpt2-medium (24L, 1600 hidden dim). Would that fit your use-case?
> 
> (sorry for the delayed answer, I don't usually check issues without being pinged/tagged).

Even a distilgpt2-large would work for my use case<|||||>I am also interested in a distilled version of the larger models. For our use-case, this would go a long way to improving cost/performance/feasibility.
<|||||>Bumping this - any word on availability of the medium/large distilled models ?<|||||>> Bumping this - any word on availability of the medium/large distilled models ?

I am currently working on it! :)
<|||||>any news on this?<|||||>Any news on this ? 😊<|||||>I would be extremely interested in having GPT2-XL distilled to the size of GPT2-L or smaller. Consumer-grade GPUs currently top out at around 8GB VRAM, which is enough to run inference using GPT2-L but is not enough for GPT2-XL. Unless you can find a beefier GPU than that, it will only become possible to efficiently run GPT2-XL on a desktop PC when someone trains a distilled model.