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Model Details: Mixtral-8x7B-Instruct-v0.1-int4-inc

This model is an int4 model with group_size 128 of mistralai/Mixtral-8x7B-Instruct-v0.1 generated by intel/auto-round. Layers "block_sparse_moe.gate" have not been quantized due to the exporting issue of AutoGPTQ format. Inference of this model is compatible with AutoGPTQ's Kernel.

How To Use

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/Mixtral-8x7B-Instruct-v0.1 \
--device 0 \
--group_size 128 \
--bits 4 \
--iters 1000 \
--enable_minmax_tuning \
--nsamples 512 \
--deployment_device 'gpu' \
--scale_dtype 'fp32' \
--eval_bs 32 \
--output_dir "./tmp_autoround" \
--amp 

Evaluate the model

Install lm-eval-harness from source, and the git id f3b7917091afba325af3980a35d8a6dcba03dc3f is used

lm_eval --model hf --model_args pretrained="Intel/Mixtral-8x7B-Instruct-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 FP16 INT4
Avg. 0.7000 0.6977
mmlu 0.6885 0.6824
lambada_openai 0.7718 0.7790
hellaswag 0.6767 0.6745
winogrande 0.7687 0.7719
piqa 0.8351 0.8335
truthfulqa_mc1 0.4969 0.4884
openbookqa 0.3680 0.3720
boolq 0.8850 0.8783
rte 0.7184 0.7004
arc_easy 0.8699 0.8712
arc_challenge 0.6220 0.6229

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.

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Dataset used to train Intel/Mixtral-8x7B-Instruct-v0.1-int4-inc