# Lilith.jl is now called Avalon.jl

**URL:** <https://discourse.julialang.org/t/lilith-jl-is-now-called-avalon-jl/51621>\
**Category:** Machine Learning\
**Created:** [December 10, 2020, 5:30pm UTC](https://discourse.julialang.org/t/lilith-jl-is-now-called-avalon-jl/51621 "2020-12-10T17:30:18Z")\
**Posts on this page:** 1\
**Showing post:** 28

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**Author:** ![Palli](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/palli/32/3380_2.png) [@Palli](https://discourse.julialang.org/u/Palli)\
**Post date:** [March 2, 2021, 3:37pm UTC](https://discourse.julialang.org/t/lilith-jl-is-now-called-avalon-jl/51621/28 "2021-03-02T15:37:05Z")

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> [@dfdx](#):
>
> Avalon follows PyTorch API pretty closely, so usually translating a new model to it takes half an hour

Is that’s what you mean with “ **interoperability with existing DL frameworks** ”? You may want to spell it out in the docs. And since plural what other? ONNX? And/or PyTorch Lightning? I’m not up-to-speed on the latter, or if some Julia package corresponds to it. In general, see also:

> [@How do Julia packages, e.g. Flux and Knet correspond to Python ecosystem](https://discourse.julialang.org/t/how-do-julia-packages-e-g-flux-and-knet-correspond-to-python-ecosystem/55147):
>
> I’m not so much asking if they are better, just some mapping, and trying to get an overview of the mainstream and where there might be big holes in the Julia ecosystem. E.g. Pytorch Lightning corresponds to Keras, and to what in Julia? See e.g.: [Converting From Keras To PyTorch Lightning | by William Falcon | Towards Data Science](https://towardsdatascience.com/converting-from-keras-to-pytorch-lightning-be40326d7b7d) They are higher-level interfaces to Pytorch and TensorFlow, those are the most mainstream low-level packages right? There’s also MXNet, and Julia already officially p…

Since you link to [vision] transformer, would your package be best to replicate BERT-models, GPT-3, or Google’s even larger Switch Transformer model? Since it’s sparse, would that be a hindrance?

Google’s 1.6 trillion parameter model:

> **[Google trained a trillion-parameter AI language model](https://venturebeat.com/ai/google-trained-a-trillion-parameter-ai-language-model/)**
>
> Researchers at Google claim to have trained a natural language model containing over a trillion parameters.

> **[2101.03961.pdf](https://arxiv.org/pdf/2101.03961.pdf)**
>
> 1273.59 KB

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