OpenAttack VS transformers

Similar to TextAttack, OpenAttack adopts modular design to assemble various attack models, in order to enable quick implementation of existing or new attack models. But OpenAttack is different from and complementary to TextAttack mainly in the following three aspects: 1) Support for all attacks. TextAttack utilizes a relatively rigorous framework to unify different attack models. However, this framework is naturally not suitable for sentence-level adversarial attacks, an important and typical kind of textual adversarial attacks. Thus, no sentence-level attack models are included in TextAttack. In contrast, OpenAttack adopts a more flexible framework that supports all types of attacks including sentence-level attacks. 2) Multilinguality. TextAttack only covers English textual attacks while OpenAttack supports English and Chinese now. And its extensible design enables quick support for more languages. 3) Parallel processing. Running some attack models maybe very time-consuming, e.g., it takes over 100 seconds to attack an instance with the SememePSO attack model (Zang et al., 2020). To address this issue, OpenAttack additionally provides support for multi-process running of attack models to improve attack efficiency.

Fascinating work, very promising.

Can you summarise how the model in your paper differs from this one ?

https://github.com/huggingface/transformers/issues/27011

transformers is a package by Hugging Face, that helps you interact with models on HF Hub (GitHub)

Is t5x an encoder/decoder architecture?

Some more general options.

The Flax ecosystem

https://github.com/google/flax?tab=readme-ov-file

or dm-haiku

https://github.com/google-deepmind/dm-haiku

were some of the best developed communities in the Jax AI field

Perhaps the “trax” repo? https://github.com/google/trax

Some HF examples https://github.com/huggingface/transformers/tree/main/exampl...

Sadly it seems much of the work is proprietary these days, but one example could be Grok-1, if you customize the details. https://github.com/xai-org/grok-1/blob/main/run.py

The HuggingFace transformers library already has support for a similar method called prompt lookup decoding that uses the existing context to generate an ngram model: https://github.com/huggingface/transformers/issues/27722

I don't think it would be that hard to switch it out for a pretrained ngram model.

Most of this tutorial is based on Hugging Face course about Transformers and on Niels Rogge's Transformers tutorials: make sure to check their work and give them a star on GitHub, if you please ❤️

* Superconvergence + LR range finder + Fast AI's Ranger21 optimizer was the goto optimizer for CNNs, and worked fabulously well, but on transformers, the learning rate range finder sadi 1e-3 was the best, whilst 1e-5 was better. However, the 1 cycle learning rate stuck. https://github.com/huggingface/transformers/issues/16013

Thanks! :) I'm pushing them into transformers, pytorch-gemma and collabing with the Gemma team to resolve all the issues :)

The RoPE fix should already be in transformers 4.38.2: https://github.com/huggingface/transformers/pull/29285

My main PR for transformers which fixes most of the issues (some still left): https://github.com/huggingface/transformers/pull/29402