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Low Ppl benchmark results #9

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waters222 opened this issue Dec 7, 2023 · 1 comment

waters222 commented Dec 7, 2023 •

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Hi.
I am in the process of adding QuiP inference support into ExllamaV2
and this is the PR

The problem I am having right now is the my Ppl testing results is kind worse compare to your blog results.

so I am wondering is there something wrong with my implementation or any other reasons.

Ppl Benchmarks

using dataset: [wikitext-2-v1_validation_0000.parquet]
(https://huggingface.co/datasets/wikitext/tree/refs%2Fconvert%2Fparquet/wikitext-2-v1/validation)

Model	Performance
2Bit
Llama-2-7b-E8P-2Bit	8.7339
Llama2-7b-exl2-2.5bpw	8.0745
Llama-2-13b-E8P-2Bit	7.1207
Llama2-13b-exl2-2.5bpw	7.2741
Llama-2-70b-E8P-2Bit	6.2192
Llama2-70b-exl2-2.5bpw	5.8270
4Bit
Llama-2-7b-HI-4Bit-Packed	6.0748
Llama2-7b-exl2-4.0bpw	6.0300
Llama-2-13b-HI-4Bit-Packed	7.4169
Llama2-13b-exl2-4.0bpw	5.4905

Contributor

tsengalb99 commented Dec 7, 2023

quip-sharp/lib/utils/gptq_data_utils.py

Line 12 in 6648e56

def get_wikitext2(nsamples, seed, seqlen, model):

This is where we sample wikitext2. You should check fp16 results on your dataset for an accurate comparison on that dataset.

tsengalb99 closed this as completed

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