transformers VS aitextgen

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.

It's literally a wrapper for the ChatGPT API (currently). I have another library for training models from scratch but haven't had time to work on it.

I'm experimenting with https://github.com/minimaxir/aitextgen for some some simple tasks. It is pretty much a wrapper around gpt2 and gpt neox models.

I'm sorry if this is the wrong place to ask, but I wasn't sure where else to turn. Several of us have already opened an issue with AITextGen, but it seems that the maintainer isn't particularly active these days. I'm a fairly proficient developer (self-taught), and I know my way around ML, but I was not formally-educated in deep learning. A lot of Pytorch-Lightning looks like black magic, to me. I suspect that I'm missing an important detail that would be fairly simple for many of you to identify.

To train small gpt-like models, there's also aitextgen: https://github.com/minimaxir/aitextgen

It's actually relatively easy to train your own GPT model and there are multiple tools out there that make it almost just plug and play: https://github.com/minimaxir/aitextgen

I've been learning python and for the past few days, I've been playing around with the aitextgen library.

Then I ran that through the ai and got it to spit out cards that looked like that training data. I used aitextgen. So I let it run for like 4 hours and it thinks it has made 10,000 rows of cards. But some of these cards are duplicates to each other or to cards that already exist, or use a card name that already exists in the original game, or have like 20 '|' characters in one row, or have zero '|'. So I run a script to remove all of these cards like that, and I end up with like 2,000-4,500 cards that are "functional".

If you search this subreddit, you should find lots of discussions about it, as well as alternatives like GPT-J (open source). If you'd like to experiment with GPT-2 for text generation, try https://github.com/minimaxir/aitextgen. It's fun to play with.

Hey HN! I've been lurking for a while now and I've finally created something that I feel is worth sharing.

I've called this project "Tensorpedia." At its core, Tensorpedia takes in a title and utilizes it as a prompt for GPT-2 to synthesize the introductory part of a Wikipedia article. The machine learning stuff is written using a wonderful library called aitextgen [0], using Wikipedia's "Vital Articles" as a data set [1]. The server is written in Node, and it uses Redis as an article cache. If you want to read my article about it (for some reason), you can check it out here [2].

I created this project to get more experience with server technologies. While I wouldn't say it's a complicated application, I learned quite a lot from it.

Additionally, as I was inspired by all of those this-x-doesn't-exist projects from a while back, this project is mostly for fun. As such, I don't know how much practical use it has, but I've generated some pretty hilarious articles from it.

[0] https://github.com/minimaxir/aitextgen

[1] https://en.wikipedia.org/wiki/Wikipedia:Vital_articles/Level...

[2] https://jonahsussman.net/posts/2022-01-this-wiki-dne/

If by downloaded you mean clone the gpt-2 github repo it doesn't come with those scripts. I personally played around with https://github.com/minimaxir/aitextgen which is a simple wrapper around the gpt-2 code, it comes with some very clear usage. (Shout out to minimaxir and everyone else involved in aitextgen for making using gpt-2 easy to use!)