KoboldAI VS transformers

Pygmalion for chatting, Erebus for story writing, Wizard Vicuna Uncensored for general use including chatting, story writing or instructing, to name a few. There's lots more, but most are just the raw models that users are expected to load themselves so there isn't anything quite like ChatGPT where you just load up a single website and have all of them there. You'll either have to set up a local install using KoboldAI for running only on GPU, KoboldCPP for running only on CPU with optional splitting between CPU and GPU, or Oobabooga for CPU, GPU and splitting between CPU and GPU, if you have a powerful enough PC to run these models yourself (a PC with a 3090 can load up to a 30B model entirely in GPU, or a 65B model if you have 64GBs of RAM and a decently powerful CPU). If you don't have a powerful enough PC then you'll have to use something like KoboldAI Lite (website version) or SillyTavern to use the horde, a crowdsourced AI chatbot/LLM runner that lets people provide their hardware for others to use.

INFO | __main__:general_startup:1312 - Running on Repo: https://github.com/0cc4m/koboldai Branch: latestgptq

Occam seems to be trying or adding that into Kobold (https://github.com/0cc4m/KoboldAI/tree/4bit-plugin)

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