llama-int8 VS FlexGen

If anyone is interested in running this at home, please follow the llama-int8 project [1]. LLM.int8() is a recent development allowing LLMs to run in half the memory without loss of performance [2]. Note that at the end of [2]'s abstract, the authors state "This result makes such models much more accessible, for example making it possible to use OPT-175B/BLOOM on a single server with consumer GPUs. We open-source our software." I'm very thankful we have researchers like this further democratizing access to this data and prying it out of the hands of the gatekeepers who wish to monetize it.

[1] https://github.com/tloen/llama-int8

[2] https://arxiv.org/abs/2208.07339

To add a bit more context, the code other people linked (https://github.com/tloen/llama-int8) assumes single GPU. So if you want to run it on 2x3090, you'll need to modify it a bit:

LLaMA, Pythia, RWKV, Flan-T5 (self-hosted), FlexGen

> With no real knowledge of LLM and only recently started to understand what LLM terms mean, such as 'model, inference, LLM model, intruction set, fine tuning' whatelse do you think is required to make a took like yours?

This was mee a few weeks ago. I got interested in all this when FlexGen (https://github.com/FMInference/FlexGen) was announced, which allowed to run inference using OPT model on consumer hardware. I'm an avid user of Stable Diffusion, and I wanted to see if I can have an SD equivalent of ChatGPT.

Not understanding the details of hyperparameters or terminology, I basically asked ChatGPT to explain to me what these things are:

   Explain to someone who is a software engineer with limited knowledge of ML terms or linear algebra, what is "feed forward" and "self-attention" in the context of ML and large language models. Provide examples when possible.