This recipe is outdated, we recommend using symmetric quantization. You can remove --asym from the command.
A sample command to generate an INT4 model.
auto-round \
--model EleutherAI/gpt-j-6b \
--device 0 \
--group_size 128 \
--bits 4 \
--iters 1000 \
--nsamples 512 \
--asym \
--format 'auto_gptq,auto_round' \
--output_dir "./tmp_autoround"Install lm-eval-harness from source, we used the git id 96d185fa6232a5ab685ba7c43e45d1dbb3bb906d ##pip install auto-gptq[triton] ##pip install triton==2.2.0
lm_eval --model hf --model_args pretrained="./",autogptq=True,gptq_use_triton=True --device cuda:0 --tasks lambada_openai,hellaswag,piqa,winogrande,truthfulqa_mc1,openbookqa,boolq,rte,arc_easy,arc_challenge,mmlu --batch_size 32| Metric | FP16 | INT4 |
|---|---|---|
| Avg. | 0.5039 | 0.5034 |
| mmlu | 0.2694 | 0.2793 |
| lambada_openai | 0.6831 | 0.6790 |
| hellaswag | 0.4953 | 0.4902 |
| winogrande | 0.6409 | 0.6401 |
| piqa | 0.7541 | 0.7465 |
| truthfulqa_mc1 | 0.2020 | 0.2179 |
| openbookqa | 0.2900 | 0.2900 |
| boolq | 0.6544 | 0.6554 |
| rte | 0.5451 | 0.5271 |
| arc_easy | 0.6692 | 0.6734 |
| arc_challenge | 0.3396 | 0.3387 |