notebooks VS transformers

📓 Open the notebook for an example of how to run a batch transform job for inference.

In order to overwrite it, the package readme has some general information about it, and also there is an example in this jupyter notebook. We are doing what is necessary via the files inside sagemaker/code, which has the inference code following SageMaker requirements, and a requirements.txt, that has the necessary dependencies that will be installed when the endpoint gets created

You still haven’t explained your use-case for the model. You can look up “Open Domain QA” models. There are a lot of them, but they’re often restricted in how well they generalize and benefit from fine tuning. E.g., https://github.com/huggingface/notebooks/blob/main/longform-qa/Long_Form_Question_Answering_with_ELI5_and_Wikipedia.ipynb

(Updated Aug. 27, 2022) Colab notebook Stable Diffusion with diffusers by huggingface. GitHub repo. Video tutorial. Official Colab notebook. txt2img. Uses HuggingFace diffusers repo.

Looks like they updated the notebook 15 minutes ago. Hopefully it works now.

Basically, you can then do similar things using HuggingFace, as indeed many have (you can explore the models in their hub)[2]

[1] https://www.youtube.com/playlist?list=PLtmWHNX-gukKocXQOkQju...

[2] https://github.com/huggingface/notebooks/blob/main/examples/...

image classification: ViT, DeiT, BEiT, Swin Transformer, PoolFormer, ResNet, RegNet, ConvNeXT, Perceiver, ImageGPT, VAN. Check out the official example scripts, example notebooks.

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

If you want to tinker with the architecture Hugging Face has a FOSS implementation in transformers: https://github.com/huggingface/transformers/blob/main/src/tr...

If you want to reproduce the training pipeline, you couldn't do that even if you wanted to because you don't have access to thousands of A100s.