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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:

  • Intel Neural Compressor link
  • Intel Extension for Transformers link

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} }

arxiv github

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Dataset used to train Intel/Mistral-7B-Instruct-v0.2-int2-inc