Open source is the only way forward with AI.

China "open sources" a lot of their technologies. They treat it as a form of competition, we'll show you how to do x but you show us how to do u and whoever is better at both wins out, there's a lot of short videos on how BYD taught other Chinese EV manufacturers and even Ford how their automated manufacturing plants work. The end result, everything becomes a highly optimized process. Glad to see they're also adopting this framework with open sourcing AI development.

This is also a reason why there's a hugeee cultural clash with US IP theft.

We already do. Can you fix so the logical conclusion happens pls

It depends how much they’ve got to offer beyond AI. If the only thing they offer is AI (like OpenAI), yeah, they’re in trouble.

This is what Open AI wants you to think. Because Open AI is burning money at an unprecedented rate and still raising money. If DeepSeek is able to do what they do if a fraction of the money, the VCs and Microsoft will begin asking questions.

A bit like a syllable when you are talking about text based responses. 20 tokens a second is faster than most people could read the output so that's sufficient for a real time feeling "chat".

It's the generation speed. Internally LLMs use tokens which represent either words or parts of words and map them to integer values. The model then does it's prediction on which integer is most likely to come after the input. How the words are split up is an implementation detail that can vary from model to model

Not somebody who knows a lot about this stuff, as I'm a bit of an AI Luddite, but I know just enough to answer this!

"Tokens" are essentially just a unit of work -- instead of interacting directly with the user's input, the model first "tokenizes" the user's input, simplifying it down into a unit which the actual ML model can process more efficiently. The model then spits out a token or series of tokens as a response, which are then expanded back into text or whatever the output of the model is.

I think tokens are used because most models use them, and use them in a similar way, so they're the lowest-level common unit of work where you can compare across devices and models.

it's a little similar to words per second

Not an answer to your question, but I thought this was a nice article for getting some basic grounding on the new AI stuff: https://arstechnica.com/science/2023/07/a-jargon-free-explanation-of-how-ai-large-language-models-work/

No, people who want something approaching chatgpt but local want to run at least deepseek V3 32B.

Qwen at least fares much worse for my usage as do deepseek V3 under 32B.