Is Liquid AI still using the liquid neural network architecture?
Liquid AI reveals 8B-A1B MoE trained on 38T
61–70 of 101 posts
Re: Liquid AI reveals 8B-A1B MoE trained on 38T
#62Earlier quoted context omitted.
doesnt seem unreasonable.
These faux questions always have a valid interpretation that the asker doesn't admit (for some reason). The model is then castigated for not making an opinionated choice
As a test, explaining away peculiar answers by imagining unlikely outlier scenarios is not the counter you seem to think it is.
For most of them, we’d worry that a human answerer using maximum effort to produce the same outcome was having a stroke.
Re: Liquid AI reveals 8B-A1B MoE trained on 38T
#63Homeopathic AI
I'd normally call that a low-effort, troll comment. But, thinking on it, you may have a great metaphor.
They keep promising great performance out of models whose key ingredient (parameters) they are diluting. Many seem to be in a competition saying they're getting smaller and higher performance at the same time. Then, the homeopathic models don't perform as well as real models when independently tested. Again, spot on.
Re: Liquid AI reveals 8B-A1B MoE trained on 38T
#64Earlier quoted context omitted.
to your point, when my Aussie friends first mentioned a "car park" to my north american born self, i wondered _momentarily_ what that was, then realized it's sort of a fun name for what i would call a parking lot.
I've never thought of it as a fun term before. We use "park" as "I will park the car" not park as in "amusement park"
Why isn’t the “pantry” called the “food store”?
Re: Liquid AI reveals 8B-A1B MoE trained on 38T
#65Question: I have a dirty car and the car wash is just 50 meters away. Should I walk or drive to the carwash? Answer: . . . . So, unless you have a compelling reason not to, walk to the car wash.
doesnt seem unreasonable.
maybe unreasoning.
also, naysayers apparently DO have a compelling reason.
Re: Liquid AI reveals 8B-A1B MoE trained on 38T
#66Earlier quoted context omitted.
Oh, I'm interested - do you have any docs with human responses to that?
“Car Wash” test with 53 models https://news.ycombinator.com/item?id=47128138 This article has a graph of the human response rates. About 70% correct on average. Accuracy depends on the country (maybe a language barrier?). See also original thread on the car wash thing. I want to wash my car. The car wash is 50 meters away. Should I walk or drive? https://news.ycombinator.com/item?id=47031580
this reminds me, I grew up in an area of the US where the pinnacle of existence was spending the whole weekend doing chores such as very publicly washing your own car in your driveway
if you were an able bodied man there is no other duty. the same for shoveling snow, or mowing a lawn, cleaning up inside the house
these are all things I've rejected and exempt myself from
but I'm beginning to remember large swaths of society live under that regime, so driving to a car wash wouldn't be an option at all. you wash your car and have a separate desire to walk to the car wash for some other reason
I could see people thinking its a trick question, or just scoffing at the idea people wash their cars at the car wash and pollute the data for AIs in annotation work.
Re: Liquid AI reveals 8B-A1B MoE trained on 38T
#67At some point we have to be running into some inherent mathematical limits of knowledge compression, right? No way the knowledge benchmarks on these 8B models will keep getting better without overfitting on these benchmarks
Re: Liquid AI reveals 8B-A1B MoE trained on 38T
#68I just tested this on a bug fixing benchmark I'm working on. It did not perform as well as I expected. Qwen2.5-Coder-3B (2 years old) outperformed it by a wide range -> fixing ~50% of bugs whereas this model only fixed ~12%. Granted, it's not a coder specific model, but given its benchmark performance to Gemma models, and that it's two years newer, and that it's an MoE with 8B total params, I expected it to be more c…
I personally find any model smaller than something like Qwen 3.6 35B-A3B (8-bit quantization, about 49GB memory usage when loaded into llama.cpp) to be too "stupid" for reliable use. I would much rather not run the model on my local laptop hardware and offload that to some system sitting under my desk in my home office, accessible via VPN, than take the risk of using an unreliable and flaky tool for the convenience o…
Have you seen the 8bit quantisation matter a lot? The "consensus" in r/LocalLlama is that up to 4 bits the loss is tolerable.
Re: Liquid AI reveals 8B-A1B MoE trained on 38T
#69At some point we have to be running into some inherent mathematical limits of knowledge compression, right? No way the knowledge benchmarks on these 8B models will keep getting better without overfitting on these benchmarks
If you give the model access to specialized tools (e.g. web search for question answering) the knowledge doesn't have to be stored in the model weights, which leaves some room for improvement. You'd still be overfitting to benchmarks (since different tasks might require different tools) but not necessarily to specific benchmark questions, so within-domain generalization could be quite good.
As an example for a similar approach, Teapot AI has trained very small models https://teapotai.com/models to only answer questions where the answer can be found within the context window, and although not perfect, they do quite well at this compared to larger, more general models.
Re: Liquid AI reveals 8B-A1B MoE trained on 38T
#70Earlier quoted context omitted.
I personally find any model smaller than something like Qwen 3.6 35B-A3B (8-bit quantization, about 49GB memory usage when loaded into llama.cpp) to be too "stupid" for reliable use. I would much rather not run the model on my local laptop hardware and offload that to some system sitting under my desk in my home office, accessible via VPN, than take the risk of using an unreliable and flaky tool for the convenience o…
Have you seen the 8bit quantisation matter a lot? The "consensus" in r/LocalLlama is that up to 4 bits the loss is tolerable.
Absolutely. Difference in Q6 vs Q8 is not as immediately noticeable, but if I test by starting from a blank slate context and giving it the same complicated task with Q4 vs a Q8 GGUF file loaded, the difference is apparent. The Q4 will struggle or do 'stupid' things with even simple bash or python. Q4 might not be as noticeable for conversational purely text one on one interaction with an LLM, but when you dig deeper into something that's more esoteric in a training dataset than a chat conversation, absolutely a big gap there.
I think some of the folks in the local llm social media communities are using them for things like company-hosted customer service chat bots, or purely english text writing stuff where Q4 will probably not cause a problem. For more discrete technical work I stick pretty much exclusively to Q8.