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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
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
Let them Fight
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.
Those cost efficiencies are also at the expense of the Chinese government. The massive investment is all part of their green revolution policy package.
It’s why Solar cells are also incredibly cheap to produce in China, and why they’re also mostly sold in China.
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)
It helps differentiate between GNU/Linux users and the five people who use GNU/Hurd
Judging by my bank account I’m transitioning to non-profit status as well.
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
thinking about Werner Von Braun and the peenemunde Yea I’m not sure anyone has a leg to stand on when it comes to “stolen” technology and space
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.
See you guys in 2040