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metadata
license: llama2
datasets:
  - NeelNanda/pile-10k
language:
  - en

Model Details

This model is an int4 model with group_size 128 of meta-llama/Llama-2-7b-chat-hf generated by intel/auto-round. 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  meta-llama/Llama-2-7b-chat-hf \
--device 0 \
--group_size 128 \
--bits 4 \
--nsamples 512 \
--iters 1000 \
--deployment_device 'gpu' \
--output_dir "./tmp_autoround" \

Evaluate the model

Install lm-eval-harness 0.4.2 from source.

lm_eval --model hf --model_args pretrained="Intel/Llama-2-7b-chat-hf-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. 0.5797 0.5787
mmlu 0.4641 0.4620
lambada_openai 0.7101 0.7017
hellaswag 0.5779 0.5699
winogrande 0.6630 0.6592
piqa 0.7644 0.7661
truthfulqa_mc1 0.3035 0.3146
openbookqa 0.3360 0.3340
boolq 0.7979 0.8064
arc_easy 0.7391 0.7336
arc_challenge 0.4411 0.4403

Ethical Considerations and Limitations

The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.

Therefore, before deploying any applications of the model, developers should perform safety testing.

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