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Liquid AI reveals 8B-A1B MoE trained on 38T

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Re: Liquid AI reveals 8B-A1B MoE trained on 38T

#71

Post by nullpoint420 »

tingletech wrote: post #19
cwnyth wrote: post #13

Earlier quoted context omitted.

I'm surprised these models haven't picked this up yet in the training data. Both Claude and ChatGPT missed that one when I posed the question to them last year.

Why would a model know that one washes cars at a car wash? We don't clean our bodies at the body wash or clean the kitchen at the kitchen wash.

Ok im supposed to assume that a model doesn’t know cars get washed at a car wash?

But then im supposed to give it access to write code in my repositories. Sorry, what are you trying to get at here?

Re: Liquid AI reveals 8B-A1B MoE trained on 38T

#73

Post by [deleted] »

onlyrealcuzzo wrote: post #40

I 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…

[deleted]

Re: Liquid AI reveals 8B-A1B MoE trained on 38T

#74

Post by ValdikSS »

elorant wrote: post #6

Wow, this is fucking phenomenal. I fed it a long transcript asking it to create a summary and it executed it extremely well. For an 8B model this is quite impressive.

Bad at translation, at least to Russian. Very fast though, about 2x faster than Gemma 4 e2b on my CPU.

Re: Liquid AI reveals 8B-A1B MoE trained on 38T

#75

Post by rbanffy »

yieldcrv wrote: post #66
andai wrote: post #57

Earlier quoted context omitted.

“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 s…

Sometimes I miss washing my car on the driveway. I guess I’m far less emotionally attached to my car now than I was in the 1980s.

Re: Liquid AI reveals 8B-A1B MoE trained on 38T

#76

Post by solarkraft »

peder wrote: post #50
debazel wrote: post #47

Earlier quoted context omitted.

I tried it with OpenCode and it is borderline incapable of using tool calls, so that might be why it is doing so bad on your test.

I just did the same. Absolutely awful. I assume OpenCode's heavy context is a problem, and it's probably better to use Liquid's own OpenCode alternative for this.

Where can I find that agent harness? A look at their Docs and asking Gemini yielded no results.

Edit: Is it this? https://github.com/Liquid4All/cookbook/tree/main/examples/lo...

FYI: Opencode is very well tuned for Qwen models, but I haven’t found it that rare for niche models to perform badly in it.

Re: Liquid AI reveals 8B-A1B MoE trained on 38T

#77

Post by theanonymousone »

walrus01 wrote: post #70
theanonymousone wrote: post #68

Earlier quoted context omitted.

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…

Thanks a lot. How about Q8 vs FP16/BF16? Have you checked them too?

Re: Liquid AI reveals 8B-A1B MoE trained on 38T

#78

Post by intothemild »

walrus01 wrote: post #59
satvikpendem wrote: post #58

Earlier quoted context omitted.

Qwen 3.6 27B dense is much better than the 35B MoE model for coding, not sure if you've tried that yet.

yes, I have, I use both. 27B slower in tok/s due to density, obviously, 35B-A3B for speed on simpler tasks.

You should enable MTP now that its available.

LLamaCPP has had some massive updates in the last week or so.

Re: Liquid AI reveals 8B-A1B MoE trained on 38T

#79

Post by ahoka »

kennywinker wrote: post #32
halJordan wrote: post #22

Earlier quoted context omitted.

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

That’s not what’s happening. The question is revealing that the model has a model of language but not of reality. It knows what words go together, but not real-world concepts.

This. LLMs are marketed on the false premise of all knowledge, intelligence and wisdom being possible to be encoded in language only.

Re: Liquid AI reveals 8B-A1B MoE trained on 38T

#80

Post by bradfa »

theanonymousone wrote: post #77
walrus01 wrote: post #70

Earlier quoted context omitted.

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…

Thanks a lot. How about Q8 vs FP16/BF16? Have you checked them too?

Q8 quant is very minimal fall off in terms of KLD against the lab 16 bit. If you have the memory for BF16 KV-cache (which is usually easier to stomach) then the Q8 is very close. But even Q8 quant model with Q8 KV-cache is very close.

Smaller quants for the model start to fall off but more importantly, smaller KV-cache quants fall off much faster so avoid less than Q8 there.

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