Difference in different quantization methods

[sussyboiiii]

Hello,
I'm wondering what quantization method or what you want to call it has the best output quality. Should you use q8_0, q4_0 or anything in between? I'm asking this question because the q8_0 version almost takes up as much space as the f16 version (13.5GB) but q4_0 only takes about 8GB, I'm talking about Vicuna 13b.
Thanks!

[ianscrivener]

The llama.cpp team does do some "perplexity" testing - which approximately determines "best output quality"

lowers score are better...

[ianscrivener]

More here: #406

[actually-a-cat]

quantize --help outputs a helpful table:

Allowed quantization types:
   2  or  Q4_0   :  3.50G, +0.2499 ppl @ 7B - small, very high quality loss - legacy, prefer using Q3_K_M
   3  or  Q4_1   :  3.90G, +0.1846 ppl @ 7B - small, substantial quality loss - legacy, prefer using Q3_K_L
   8  or  Q5_0   :  4.30G, +0.0796 ppl @ 7B - medium, balanced quality - legacy, prefer using Q4_K_M
   9  or  Q5_1   :  4.70G, +0.0415 ppl @ 7B - medium, low quality loss - legacy, prefer using Q5_K_M
  10  or  Q2_K   :  2.67G, +0.8698 ppl @ 7B - smallest, extreme quality loss - not recommended
  12  or  Q3_K   : alias for Q3_K_M
  11  or  Q3_K_S :  2.75G, +0.5505 ppl @ 7B - very small, very high quality loss
  12  or  Q3_K_M :  3.06G, +0.2437 ppl @ 7B - very small, very high quality loss
  13  or  Q3_K_L :  3.35G, +0.1803 ppl @ 7B - small, substantial quality loss
  15  or  Q4_K   : alias for Q4_K_M
  14  or  Q4_K_S :  3.56G, +0.1149 ppl @ 7B - small, significant quality loss
  15  or  Q4_K_M :  3.80G, +0.0535 ppl @ 7B - medium, balanced quality - *recommended*
  17  or  Q5_K   : alias for Q5_K_M
  16  or  Q5_K_S :  4.33G, +0.0353 ppl @ 7B - large, low quality loss - *recommended*
  17  or  Q5_K_M :  4.45G, +0.0142 ppl @ 7B - large, very low quality loss - *recommended*
  18  or  Q6_K   :  5.15G, +0.0044 ppl @ 7B - very large, extremely low quality loss
   7  or  Q8_0   :  6.70G, +0.0004 ppl @ 7B - very large, extremely low quality loss - not recommended
   1  or  F16    : 13.00G              @ 7B - extremely large, virtually no quality loss - not recommended
   0  or  F32    : 26.00G              @ 7B - absolutely huge, lossless - not recommended

The ppl column is perplexity increase relative to unquantized.
Q4_K_M, Q5_K_S and Q5_K_M are considered "recommended".

[sussyboiiii]

Thank you,
so I would probably want Q5_K_M, but I haven't heard of K_M so what would it be, would it be q5_0 or what would be the type I'd prefer?

[Green-Sky]

K-quantizations should be better, at the same file size, then the other ones. S M L means small medium large :)
more details can be found here: #1684

[sussyboiiii]

So this is a completely new quantization variant? Can this be done with the quantize executable from f16 to 5_K_M as well?

[Green-Sky]

yes, that is the intended procedure.

[sussyboiiii]

Thanks!

[Green-Sky]

1. i'm pretty sure the vicuna model is supplied as f16, so upconverting to f32 wont get you anything. besides doubling the intermediary file size :)
2. i think you need to specify the output model path, even if it is marked as optional in the help. might need to fix this.
   usage: bin/quantize [--help] [--allow-requantize] [--leave-output-tensor] model-f32.bin [model-quant.bin] type [nthreads]
   -->
   bin/quantize ~/desktop/vicuna/model-f32.bin ~/desktop/vicuna/model-q5_k_m.bin Q5_K_M

[Timophey999]

Брат, как сделать так, чтобы она информацию из реального времени брала?

[sussyboiiii]

I don't think it's possible with llama.cpp at the moment.

[ytm369]

Why q8, f16 and f32 are not recommended even if there is low quality loss.

[sussyboiiii]

Because they will use much more ram and run way slower.