I use a 16b reduction of deepseek-r1 on my pc at home and it’s definitely not total bullshit. It’s 10gb of local model that can solve mathematics and physics problems for you or program in python or bash. It doesn’t hallucinate (or I haven’t been able to elicit it), it’s aware of the extents of its knowledge. It works incredibly fast on an old ryzen 1600 with 6600xt. Having an open source reasoning AI that takes 10gb of SSD and about 13 gb of ram is so weird that the only thing weirder is seeing smart people dismiss it as bullshit out of hand.
OK, it just spits predicted tokens, but in answer to what you asked and sensitive to the context you provided and its predictions are arranged such that when you decode them into language they present evidence or arguments used in thinking or argumentation. It also forms conclusions, inferences and produces results to problems, if you allow me to recycle from a dictionary definition of “reasoning”. It’s not perfect and obviously you can’t cram a huge amount into a 16b distillation and it certainly can get things wrong, but you have to squint to not see reasoning when you ask it to guesstimate something or solve a mathematical problem. It is an LLM but there’s reasoning coming out?
Well, to be 100% fair, it’s all total bullshit.
I use a 16b reduction of deepseek-r1 on my pc at home and it’s definitely not total bullshit. It’s 10gb of local model that can solve mathematics and physics problems for you or program in python or bash. It doesn’t hallucinate (or I haven’t been able to elicit it), it’s aware of the extents of its knowledge. It works incredibly fast on an old ryzen 1600 with 6600xt. Having an open source reasoning AI that takes 10gb of SSD and about 13 gb of ram is so weird that the only thing weirder is seeing smart people dismiss it as bullshit out of hand.
It’s still an LLM right? I’m going to have to take issue with your use of the word ‘reasoning’ here
OK, it just spits predicted tokens, but in answer to what you asked and sensitive to the context you provided and its predictions are arranged such that when you decode them into language they present evidence or arguments used in thinking or argumentation. It also forms conclusions, inferences and produces results to problems, if you allow me to recycle from a dictionary definition of “reasoning”. It’s not perfect and obviously you can’t cram a huge amount into a 16b distillation and it certainly can get things wrong, but you have to squint to not see reasoning when you ask it to guesstimate something or solve a mathematical problem. It is an LLM but there’s reasoning coming out?