datasets VS trax

I tried Librispeech, a very common dataset for speech recognition, in both HF and TFDS.

TFDS performed extremely bad.

First it failed because the official hosting server only allows 5 simultaneous connections, and TFDS totally ignored that and makes up to 50 simultaneous downloads and that breaks. I wonder if anyone actually tested this?

Then you need to have some computer with 30GB to do the preparation, which might fail on your computer. This is where I stopped. https://github.com/tensorflow/datasets/issues/3887. It might be fixed now but it took them 8 months to respond to my issue.

On HF, it just worked. There was a smaller issue in how the dataset was split up but that is fixed now, and their response was very fast and great.

but it looks like none of your plants are supported by the plantvillage model, or do I understand something wrong? https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/image_classification/plant_village.py#L57

To do our example, we're going to use some audio files released by Google.

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

and the implementation https://github.com/google/trax/blob/master/trax/models/resea... if you are interested.

Hope you get to look into this!

Thank you for developing the pipeline and amassing considerable compute for gathering and preprocessing this dataset!

I'm not sure if this is the right place to ask about this, but could you consider training an LLM using a more advanced, sparse transformer architecture (specifically, "Terraformer" from this paper https://arxiv.org/abs/2111.12763 and this codebase https://github.com/google/trax/blob/master/trax/models/resea... by Google Brain and OpenAI)? I understand the pressure to focus on training a straightforward LLaMA replication, but of course you see that it's a legacy dense architecture which limits its inference performance. This new architecture is not just an academic curiosity but is already validated at scale by Google, providing 10x+ inference performance boost on the same hardware.

Frankly, the community's compute budget - for training and for inference - isn't infinite, and neither is the public's interest in models that do not have advantage (at least in convenience) over closed-source ones; and so we should utilize both those resources as efficiently as possible. It could be a big step forward if you trained at least LLaMA-Terraformer-7B and 13B foundation models on the whole dataset.

But a couple years later they came out with open source implementations yeah: https://github.com/google/trax/tree/master/trax/models/reformer

Code: https://github.com/google/trax/blob/master/trax/examples/Terraformer_from_scratch.ipynb

Training language models is expensive. Train the biggest model you can afford. I assume you've tried the colab from the reformer GitHub: https://github.com/google/trax/tree/master/trax/models/reformer