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low quant weights make the embedding model lose all discriminative power. I plotted the cosine correlation matrix of jina-v5, and one can see that low quant makes the model really blind. The off-diagonal similarities are pretty high on Q1/2/3, meaning everything looks similar in the semantic space. Q4 is... show more

Han Xiao

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63,134 görüntüleme • 7 ay önce •via X (Twitter)

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deep Manifold profil fotoğrafı

deep Manifold7 ay önce

once high order nonlinearity and discontinuous is truly understood, quantization is dead end.

dazzafact profil fotoğrafı

dazzafact7 ay önce

Why not use a form of "WebP" Like compression instead of "JPEG"-like?

Han Xiao profil fotoğrafı

Han Xiao7 ay önce

damn u r right!

Yug Shende profil fotoğrafı

Yug Shende7 ay önce

Q8 is gonna become the new FLAC. It’s not gonna benefit most and yet some will swear by it.

Pabli! 🌿 💥💫 profil fotoğrafı

Pabli! 🌿 💥💫7 ay önce

the best visualization of quantization that i've seen

Ping He profil fotoğrafı

Ping He7 ay önce

My guess is the results you are seeing has more to do with the specific quantization method. The type of quant you can run on GGUF are "toy" quantization, they are not sota, and do not reflect non-linear quant frameworks, there is an ocean out there

Doxy profil fotoğrafı

Doxy7 ay önce

exactly why i stopped trusting q2 for anything that isn't a toy, just switched to llama.cpp with -q4_0 and the retrieval latency dropped while accuracy stayed solid honestly if your vectors look like soup you're either quanting too hard or using a model that was never meant to

High Jack profil fotoğrafı

High Jack7 ay önce

Nice viz. Q4's the floor for embeddings. Below that, you're just burning compute on semantic noise.

kodee profil fotoğrafı

kodee7 ay önce

I’m writing a paper about this right now. The effects of floating point non determinism on quantization

Logan Bolton profil fotoğrafı

Logan Bolton7 ay önce

Love this visualization!

Chahid Chirchi profil fotoğrafı

Chahid Chirchi7 ay önce

yeah low quant kills the separation, those matrices look rough!!

Jason McGhee profil fotoğrafı

Jason McGhee7 ay önce

Would love to see this coloration on a logarithmic scale. In my personal experience the Q8_0 is a significantly more stable and generally higher quality experience.

Sense profil fotoğrafı

Sense7 ay önce

Interesting. For me I see it like topographic map. 4-bit gives you 16 elevations.. that's where you can really start to contrast outliers from the near zeros etc.

ethantsliu profil fotoğrafı

ethantsliu7 ay önce

very cool!

Martin Skorunka profil fotoğrafı

Martin Skorunka7 ay önce

For me it would be very informative how the situation looks between q3_k_xl and something like iq4_k_xs.

Tom Dörr profil fotoğrafı

Tom Dörr7 ay önce

Correlations between what?

Han Xiao profil fotoğrafı

Han Xiao7 ay önce

Same 1k sentences -> diff quants weights -> 1k embeddings -> Cos(1k,1k)

KeenZ😶‍🌫️ profil fotoğrafı

KeenZ😶‍🌫️7 ay önce

A very intuitive demonstration, fantastic!

gabor profil fotoğrafı

gabor7 ay önce

why would higher weight similarity imply 'blindness'? obviously at Q1 weights are either fully or zero correlated, and quant necessarily shifts toward higher correlation. Doesn't mean multiple non-linear stacked layers with high correlation don't retain useful patterns.

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