This recipe is outdated, we recommend using symmetric quantization.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 mistralai/Mixtral-8x7B-v0.1 \
--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 f3b7917091afba325af3980a35d8a6dcba03dc3f
Download the model from hf(coming soon) or follow examples/language-modeling/scripts/Mixtral-8x7B-v0.1.sh to generate the model
lm_eval --model hf --model_args pretrained="Intel/Mixtral-8x7B-v0.1-int4-inc",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 | BF16 | INT4 |
|---|---|---|
| Avg. | 0.6698 | 0.6633 |
| mmlu | 0.6802 | 0.6693 |
| lambada_openai | 0.7827 | 0.7825 |
| hellaswag | 0.6490 | 0.6459 |
| winogrande | 0.7648 | 0.7514 |
| piqa | 0.8248 | 0.8210 |
| truthfulqa_mc1 | 0.3427 | 0.3219 |
| openbookqa | 0.3540 | 0.3560 |
| boolq | 0.8523 | 0.8474 |
| rte | 0.7076 | 0.6931 |
| arc_easy | 0.8430 | 0.8430 |
| arc_challenge | 0.5666 | 0.5648 |