Why do all LLMs do a particle simulation when you ask them this prompt ? Qwen3.6, Qwen3.8 and Ling 3.0 Tiny all did the same thing !
I find Ling 3.0 tiny particularly interesting as it looks really nice for a tiny model with 7.9B total parameters, with only 1.3B parameters activated per token. Here is the result https://coolthing-ling-3-tiny.tiiny.site (sorry for the weird hosting, first I found that worked)
(it cost me almost 0 cents and done in 49 seconds)
> Why do all LLMs do a particle simulation when you ask them this prompt ? Qwen3.6, Qwen3.8 and Ling 3.0 Tiny all did the same thing !
Datasets contains lots of people sharing particles simulations in various ways, with a bunch of people replying "that's so cool" and similar, so 10 years later someone asks an LLM for "cool thing" and "particle simulations" rank pretty far up when it thinks about what others have called cool.
There is that, but RLHF is a stronger influence. People who asked to build a cool HTML and JavaScript thing, or things of that sort were more pleased to see working shaders and other cool visual effects that are obscure to create (for most). That gets fed back as a reward.
Also the reason LLMs are positive, enchanting, pleasant, glorifying, demagogues.
Not because it's skewed tone in the data. They are acute politicians.
The dense 27b Qwen on M4 Pro has a prompt processing speed of around 125tok/s which makes it ok to ask a quick question but impossible to use in an agent, as processing the first prompt of the agent with the tools and instruction can easily be 10 000 tokens
In this case the 35b a3b makes sense as it has a PP speed of around 800tok/s
True. This then boils down to a quality vs speed decision. the 3.8 27b is far better than 3.6 A3B from my experience. I'm happy taking the speed hit, given local models aren't as intelligent as frontier models. Anything that can get me closer to my CC experience both in reasonable speed and intelligence is worth it. With that said CC can also be slow at times, so it's locally the difference in experience is not always noticeable.
I find Ling 3.0 tiny particularly interesting as it looks really nice for a tiny model with 7.9B total parameters, with only 1.3B parameters activated per token. Here is the result https://coolthing-ling-3-tiny.tiiny.site (sorry for the weird hosting, first I found that worked)
(it cost me almost 0 cents and done in 49 seconds)