fickling VS FlexGen

You're right! You should probably use Trail of Bits Fickling tool to investigate. https://github.com/trailofbits/fickling

I tested the Anything V3 pruned from Hugging Face, and indeed nothing funny in its pickle. I used the Fickling library to decompile it. I do not use Windows so my interests in .ckpt security are largely related to Pickle exploits— which could extract malicious code from a data file and then do something with it, but the data files themselves are not executed. I will edit this comment with lines referencing that data file.

I've been using Diffusion Bee on my Mac, and it's just gained the ability to import models (which it converts), but it is unpickling to do so— but barely. It unpickles, figures out what sort of data is in every data file and then computes what it wants from them on its own. I would love it to not use unpickling at all, so my intention is if I can figure it out, to write a script to decode the pickle file (with Fickling or otherwise) and then just do the weight calculation/assignment.

Something you won't gather from skim-reading the headline is that this is that the author has also created a tool, Fickling: https://github.com/trailofbits/fickling - to aid in playing around with pickle files.

From the article: [Fickling] can help you reverse engineer, test, and even create malicious pickle files.

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.