ChatGpt already is multiple smaller models. Most guesses peg chatgpt4 as a 8x220 Billion parameter mixture of experts, or 8 220 billion parameter models squished together

My one dark hope is AI will be enough of an impetus for somebody to update DMCA

> pay once, get access to everything everywhere

> thinks about Elsevier

OH GOD PLEASE NO

This is interesting but I’ll reserve judgement until I see comparable performance past 8 billion params.

All sub-4 billion parameter models all seem to have the same performance regardless of quantization nowadays, so 3 billion is a little hard to see potential in.

I seriously doubt the viability of this, but I’m looking forward to being proven wrong.

The OSI just published a resultnof some of the discussions around their upcoming Open Source AI Definition. It seems like a good idea to read it and see some of the issues they’re trying to work around…

https://opensource.org/blog/explaining-the-concept-of-data-information

I would recommend instead to use the AI Horde: https://stablehorde.net/ It’s a collection of people hosting stable diffusion/text generation models

There’s also openrouter which can connect to ChatGPT with a token-based system. (They check your prompts for hornyposting though)

They should probably do the same with cascade, seeing as that license seems to be more restrictive as well

Judging by my bank account I’m transitioning to non-profit status as well.

https://www.gnu.org/software/gnuzilla/

In my experience these open models is where the real work is being done. The large supervised models like DALL-E etc are more flashy but there’s a lot more going on behind the scenes than the model itself so it feels like it’s hard to gauge the real progress being done

You could try Guix! It’s ostensibly source based but you can use precompiled binaries as well (using the substitute system)

It’s a source-first Functional package distro like Nix but uses Scheme to define everything from the packages to the way the init system (Shepherd) works.

It’s very different from other distros but between being functional, source-first, and having shepherd, I personally love it

It’s usually not the water itself but the energy used to “systemize” water from out-of-system sources

Pumping, pressurization, filtering, purifying all take additional energy.

The problem is notably “powerful”, AIs need pretty significant hardware to run well

As an example the snapdragon NPUs I think can barely handle 7B models.

This is because all LLMs function primarily based on the token context you feed it.

The best way to use any LLM is to completely fill up it’s history with relevant context, then ask your question.

Doesn’t this just do what gets done through convolution anyway?

What’s the point of this.

Ahhhh that makes a lot more sense, thanks!

I didn’t realize you could run something like this on your phone

This is a good move for international open source projects, with multiple lawsuits in multiple countries around the globe currently ongoing, the intellectual property nature of code made using AI isn’t really secure enough to open yourself up to the liability.

I’ve done the same internally at our company. You’re free to use whatever tool you want but if the tool you use spits out copyrighted code, and the law eventually has decided that model users instead of model trainers are liable for model output, then that’s on you buddy.

Doing gods work here