LeanDojoChatGPT VS FlexGen

Check out this paper:

https://leandojo.org/

People have already trained models to assist suggestion tactics. They then linked it up to ChatGPT to interactively solve proofs.

In this scenario, ChatGPT asks the model for tactic suggestions, applies it to the proof and uses the feedback from Lean to then proceed with the next step.

FYI, The programmatic interface to Lean was written by an OpenAI employee who was on the Lean team a few years ago.

Also, check out Lean’s roadmap. They aspire to position Lean to becoming a target for LLMs because it has been designed for verification from the ground up.

As math and compsci nerds contribute to mathlib, all of those proofs are also building up a huge corpus that will likely be leveraged for both verification and optimization.

If AI can make verification a lot easier, then we’re likely going to see verification change programming similarly to the way it changed electronics.

Good questions!

Nowadays, there is indeed a movement towards interoperability between the various proof assistants, one of these bridge-building projects is called Dedukti: https://deducteam.github.io/ It's a challenging project because the different proof assistants which are currently in use differ in their foundational perspectives and their idioms. The question how to best formalize mathematics is still an open research problem, just as the question how to best develop programs, but we already have quite a good understanding of many important issues in this area.

Also, by now there are attempts to use AI for discovering proofs, see for instance https://leandojo.org/ or https://github.com/lean-dojo/LeanDojoChatGPT.

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.