# Initial Testing 2024-04-25

Some folks are claiming there's something funky going on with GGUF quanting for Llama 3 models.  I don't disagree.

Some of those people are speculating that it has something to do with converting the raw weights from bf16 to fp16 instead
of converting to fp32 as an intermediate step.  I think that's bollocks.  There is no logical or mathmatical justification for
how that could possibly matter.

So to test this crazy theory, I downloaded Undi95/Meta-Llama-3-8B-Instruct-hf and converted it to GGUF three ways:
- fp16 specifically with `--outtype f16`
- fp32 specifically with `--outtype f32`
- "Auto" with no outtype specified

I then quantized each of these conversions to Q4_K_M and ran perplexity tests on everything using my abbreviated wiki.short.raw 
text file.  The results:

````
FP16 specified:  size 14.9GB    PPL @ fp16 9.5158 +/- 0.15418    PPL @ Q4km 9.6414 +/- 0.15494
FP32 specified:  size 29.9GB    PPL @ fp32 9.5158 +/- 0.15418    PPL @ Q4km 9.6278 +/- 0.15466
None specified:  size 29.9GB    PPL @ ???? 9.5158 +/- 0.15418    PPL @ Q4km 9.6278 +/- 0.15466
````


As you can see, converting to fp32 has no meaningful effect on PPL compared to converting to fp16.  PPL is identical at full weight, 
and the miniscule loss shown at Q4km is will within the margin of error.  There will no doubt be some people who will claim 
"PpL iSn'T gOoD eNoUgH!!1!".  For those people, I have uploaded all GGUFs used in this test.  Feel free to use those files to do 
more extensive testing on your own time.  I consider the matter resolved until somebody can conclusively demonstrate otherwise.


# Continued Experiments 2024-05-11

As an imatrix enjoyer, it has been bugging me whether the precision of the quant used to generate the imatrix actually 
matters.  Scuttlebut says "yes, but only a little".  Logically, I don't think it should matter to a meaningful extent.  PPL 
scales, so a value that is relatively important at fp16 should also register as relatively important at Q8 or even Q4.  

To test this theory properly, I took failspy/Llama-3-8B-Instruct-abliterated and converted it to GGUF in both fp16 and fp32 
formats.  I then quantized each of those GGUFs to both Q8_0 and Q4_0.  I then generated imatrices for each of those six 
GGUFs.  Then I created eight GGUFs quantized at Q4_k_m:

 - fp32 GGUF, fp32 imatrix
 - fp16 GGUF, fp16 imatrix
 - fp32 GGUF, fp32->Q8 imatrix
 - fp16 GGUF, fp16->Q8 imatrix
 - fp32 GGUF, fp32->Q4 imatrix
 - fp16 GGUF, fp16->Q4 imatrix
 - fp32 GGUF, no imatrix
 - fp16 GGUF, no imatrix

I ran PPL against all 8 quants, as well as the full fp16 and fp32 GGUFs.  Results:

<in progress>

Conclusion:

<in progress>