Bartowski PRO

The test mark was after initial upload and after people pointed it out :) glad it is a good label though

This argument really doesn't make any sense to me.. surely if you're aiming for the most accurate overall representation anyone can see that gathering as many data points across a diverse area would yield the most useful results? Sure ideally your single light will probably get a reasonably close overall value.. but also it might not?

Additionally, I think his point was that you don't necessarily want to increase performance against a given corpus, but rather increase faithfulness to the original model against a given corpus

You may be able to keep PPL the same or better than the original while simultaneously veering far from what the original model would have generated, which while great for that corpus of text, is not what the intention of the quantization itself is (in fact many people worry about this a lot, fearing that the quantization will favour the text used as a reference, which I'm luckily seeing is not what happens at least for imatrix)

The fact that 2 models can have identical PPL scores yet generate completely different text should be proof enough that PPL only tells a tiny part of a story. Yes it's good to know the model is good, but when quantizing I don't need to know how good it is, I need to know how similar it is to the original.

I suppose that's reasonable, I guess why I like KLD more is that I breaks it down into percentages, like mean, max, 99.99%, etc etc, where PPL is just a single all encompassing number that's more difficult to interpret

I don't know if I can put much value into IQ6 outperforming fp16 because lately we've been seeing benchmarks where Q3 beats bf16, so while useful I don't know that they can't definitively tell us quant quality, but I do think it's a good proof of competency

This is why KLD to me provides at least a slightly clearer image of how well the quantization does at recreating the original model. I see what you're saying still about PPL but (at least how llama.cpp does it) KLD gives a more thorough look. That and TOP p is nice to see how often the models agree on the token

That's not an invalid point, but also when the final goal is quantization that 0.03% is negligible compared to the rest of the losses.

If you're talking about running at full precision, yeah, bf16 > fp16 by all means

I'd also prefer to see KLD of fp16 vs bf16 since PPL is, to me, pretty meaningless. I'm sure it has value and probably more than I give it, but unless it's PPL against the dataset it was trained on I don't really find much merit to it.

I appreciate the breakdown though, and even 0.4% is not enough to worry me when again the final goal is quantization, not to run it at that DTYPE.

To that end, do you happen to know if when quantizing from BF16.. does it get converted to FP16 first? Does it even matter? BF16 -> Q8 vs BF16 -> FP16 -> Q8, I wonder how different it would be. Gut instinct says it's in the 0.01% range.

Bf16 can't be offloaded to GPUs so imatrix becomes slow to make :')

I suppose I should add, that this is more valuable as a pseudo comparison to bf16

Since bf16 can represent the range (1, -1) with more precision than fp16, there is much debate as to whether it's safe to convert from bf16 to fp16, or if you should keep bf16, or even upcast to fp32, in order to preserve the original quality of the model for as long as possible before quantizing to 8 bits

This test shows that fp16 is capable of represent 99.97% of the weights in an FP32 model precisely, and therefore represents a negligible at best difference

Additionally, since the weights it can't represent are between 6e-5 and -6e-5, the weights it can't represent are so small that they most likely do not contribute to the finally output of the model and are relatively safe to prune

also maybe there should be a new feature to be explicitly notified about new repositories

That would be amazing, probably for average users but especially for me, where I sometimes stumble upon a model uploaded days ago that I somehow didn't notice from a creator I enjoy

We will have to see if something like that is possible without cluttering up the profile pages too much. But we'll try.

That sounds awesome, could even consider something like a toggle in the settings for "show this model on my page" or something, and possibly as a variable when using huggingface-cli or the HF python API

I think we'll be doing a social features sprint soon and this is exactly the kind of feedback we need! Thank you so much!

Beautiful, I love this :D If you need feedback on anything specific feel free to reach out, would love to be a guinea pig or just early eyes !

Oh another big pain point: notifications

I would love to be able to subscribe to be notified of new models posted by people or organizations, but it's near impossible as is

I would love better filtering

First I think sort by created is broken, but haven't checked on desktop recently

Second, I would love date filtering, like show me trending models that were only posted or updated in the past 7 days and such

I'm happy to hear this too, money in the bank is good, but upwards momentum makes it so much easier to justify investing in new technology and improving things!

much more difficult though if you're trying to iterate, definitely an interesting final validation

oh god dammit haha, i did not think of that possibility AT ALL 🤦

KL Divergence is almost identical - though even then upsetting that it's "almost" - but yup there's huge differences in the top p...

====== Perplexity statistics ======
Mean PPL(Q)                   :   6.339378 ±   0.038949
Mean PPL(base)                :   6.337070 ±   0.038896
Cor(ln(PPL(Q)), ln(PPL(base))):  99.99%
Mean ln(PPL(Q)/PPL(base))     :   0.000364 ±   0.000067
Mean PPL(Q)/PPL(base)         :   1.000364 ±   0.000067
Mean PPL(Q)-PPL(base)         :   0.002308 ±   0.000427

====== KL divergence statistics ======
Mean    KLD:   0.000005 ±   0.000001
Maximum KLD:   0.113848
99.9%   KLD:   0.000346
99.0%   KLD:   0.000055
99.0%   KLD:   0.000055
Median  KLD:   0.000001
10.0%   KLD:  -0.000014
 5.0%   KLD:  -0.000021
 1.0%   KLD:  -0.000035
Minimum KLD:  -0.000120

====== Token probability statistics ======
Mean    Δp:  0.002 ± 0.000 %
Maximum Δp: 19.102%
99.9%   Δp:  0.417%
99.0%   Δp:  0.155%
95.0%   Δp:  0.067%
90.0%   Δp:  0.040%
75.0%   Δp:  0.010%
Median  Δp:  0.000%
25.0%   Δp: -0.007%
10.0%   Δp: -0.034%
 5.0%   Δp: -0.062%
 1.0%   Δp: -0.154%
 0.1%   Δp: -0.439%
Minimum Δp: -5.820%
RMS Δp    :  0.078 ± 0.016 %
Same top p: 99.927 ± 0.007 %