sqlitestudio VS transformers

DB4S provides only one algorithm based on official SQLite cipher. You can encrypt your database with another in SQLiteStudio or sqlite-gui (I'm an author). Both applications use SQLite3 Multiple Ciphers-library.

SQLiteStudio is my favourite - https://sqlitestudio.pl/

The most popular apps areDB4S and SQLiteStudio. If you are planning to run long time queries, then you might encounter with problems by running them in parallel in these tools. To run several queries in a real parallel mode you can use Navicat for SQLite or my sqlite-gui.

If you are up to learn SQL queries, I would suggest using SQLite. It stores all information in a single file. You even can use https://sqlitestudio.pl solution so it can be a little more visual.

DB Browser for SQLite and SQLiteStudio both exists, not sure if this is what you are looking for.

Perhaps the simplest, for your case, would be a tool made specifically for SQLite, such as SQLite Studio.

They're database files that you can inspect with a program like DB Browser or SQLiteStudio. You should be able to import permissions.sqlite to different Firefox profiles by just copying it to the right profile directory.

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.

I am aware that using mixed precision reduces the stability of weight and there will be little consistency but don't expect it to be this much. I have attached the graph of evaluation metrics. If someone can give me some insight into this issue, that would be great.