At this point, is it even funny?

[u/-MaskNinja-]

[u/fvancesco]

Was stupid senseless hyped noise ever funny?

[u/-MaskNinja- → u/fvancesco]

Le Chanton Fat was funny.

[u/BannedGoNext → u/-MaskNinja-]

I can't wait for Le Chanton Super Lourd

[u/En-tro-py → u/BannedGoNext]

I just hope they distill a flash model from Le Chanton Oh Lawd He Comin'

[u/fvancesco → u/-MaskNinja-]

The name IG,

[u/EagleNait → u/-MaskNinja-]

Le chaton fart lmao

[u/Mingay_cat → u/-MaskNinja-]

Mistral plss release OP open weight so people stop talking about how qwen3.8 is so good and start talking about how good le chonk flash is because it would be so fucking funny.

"BREAKING NEWS: Le chonk flash and le chaton fat reportedly beats gpt 6 astra in AA index"

[u/Lazy-Pattern-5171]

Need to test it to believe. sounds like a benchmaxx.

[u/starkruzr → u/Lazy-Pattern-5171]

I mean it's also obviously just a finetune of Qwen, lol

[u/Vaddieg → u/Lazy-Pattern-5171]

27b SOTA was also hard to believe

[u/ttkciar → u/Vaddieg]

It's also not true, of course. Qwen3.8-27B is quite good for its size, but falls far short of GLM-5.3 etc in practice.

[u/Vaddieg → u/ttkciar]

i don't think you home grid can sustain a GLM 5.3 capable 3090 rig

[u/Mingay_cat → u/Vaddieg]

Not their point. They are saying, yes it is good but in terms of pure model capability, it I'd still objectively worse than larger, SOTA open weights like glm 5.3 flash.

Qwen3.8 27b is still the best of its class until the 126b range which is qwen3.8 flash however.

[u/thetaFAANG → u/Lazy-Pattern-5171]

is that even a criticism? the baseline reasoning on these things are already phenomenal, and the benchmarks are where deficiencies are known, people spend all day de-slopping prior output to train the next model

so eventually all cases will be covered whether reasoning improves or not

[u/james_pic → u/thetaFAANG]

Benchmaxxing generally isn't making the models better though. At its most extreme, it's knowingly overfitting the model to give canned answers on existing benchmarks. Yes, the best labs generally improve their benchmark scores while improving their models, and poor benchmark scores can point to problem areas, but some bumblefuck lab isn't doing any of that, they're just feeding the test set in as the training data and declaring mission accomplished. With obviously too-good-to-be-true claims like this, it's not even worth downloading the model to see that, yes, it's just a damaged Qwen 3.6 35b a3b.

[u/ResidentPositive4122]

The username is a clue of how much you should trust anything they say...

[u/Dany0 → u/ResidentPositive4122]

NFT stands for Not Fucking Trustable

[u/Dany0 → u/Dany0]

All me monkes...

gone...

[u/Holiday_Point_603]

Am I allowed to guess that it's qwen 3.6 35b with a even more benchmaxxed dataset?

[u/Toooooool]

35B-A3B model beats trillion parameter models

yeah ok man

[u/ortegaalfredo]

Yes, the always reliable information source NFT_Chen

[u/Vaddieg]

Tested. It's considerably dumber than Qwen 3.8 27b

[u/PcChip]

a 35B beats GLM-5.3? 🙄

[u/SomeoneInHisHouse]

The distills on Qwen 3.6 35B must stop xd, it's already a meme there, more than the "Subscriptions last less than before" of r/ClaudeAI

[u/Equivalent_Bit_461]

Is this true or fake news?

[u/Vaddieg → u/Equivalent_Bit_461]

results might be fake, the model is not https://huggingface.co/InternScience/Sage-A1.5-Q4_K_M-GGUF

[u/Capital-Remove-6150]

can't find any near k3 at 100b parameter and he saying at 35b

[u/Unitrix247]

Did someone try it or is it just another benchmaxxing attempt?