Model Details
This model is an int2 model with group_size 32 of mistralai/Mistral-7B-Instruct-v0.2 generated by intel/auto-round. The model size of it is 2.6 Gb. Inference of this model is compatible with AutoGPTQ's Kernel.
Reproduce the model
Here is the sample command to reproduce the model
git clone https://github.com/intel/auto-round
cd auto-round/examples/language-modeling
pip install -r requirements.txt
python3 main.py \
--model_name mistralai/Mistral-7B-Instruct-v0.2 \
--device 0 \
--group_size 32 \
--bits 2 \
--nsamples 512 \
--iters 200 \
--minmax_lr 0.01 \
--deployment_device 'auto_round' \
--output_dir "./tmp_autoround" \
Evaluate the model
Install lm-eval-harness 0.4.2 from source.
git clone https://github.com/intel/auto-round
cd auto-round/examples/language-modeling
pip install -r requirements.txt
python3 eval_042/evaluation.py --model_name ./tmp_autoround --eval_bs 16 --tasks lambada_openai,hellaswag,piqa,winogrande,truthfulqa_mc1,openbookqa,boolq,arc_easy,arc_challenge,mmlu
| Metric | FP16 | INT2 |
|---|---|---|
| Avg. | 0.6591 | 0.6014 |
| mmlu | 0.5877 | 0.5140 |
| lambada_openai | 0.7155 | 0.6295 |
| hellaswag | 0.6602 | 0.5856 |
| winogrande | 0.7411 | 0.6835 |
| piqa | 0.8014 | 0.7748 |
| truthfulqa_mc1 | 0.5251 | 0.4651 |
| openbookqa | 0.3520 | 0.2900 |
| boolq | 0.8529 | 0.8226 |
| arc_easy | 0.8136 | 0.7647 |
| arc_challenge | 0.5418 | 0.4846 |
Caveats and Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
Here are a couple of useful links to learn more about Intel's AI software:
Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.
Cite
@article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao}, journal={arXiv preprint arXiv:2309.05516}, year={2023} }