Model Details
This model card outlines the quantization recipe, but unfortunately, we're unable to provide the model at this time. Please stay tuned for our upcoming release.
This model is an int4 model with group_size 128 of tiiuae/falcon-7b-instruct 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 tiiuae/falcon-7b-instruct \
--device 0 \
--group_size 128 \
--bits 4 \
--iters 1000 \
--nsamples 512 \
--deployment_device 'gpu' \
--disable_quanted_input \
--output_dir "./tmp_autoround" \
Evaluate the model
Install lm-eval-harness from source, we used the git id 96d185fa6232a5ab685ba7c43e45d1dbb3bb906d
lm_eval --model hf --model_args pretrained="Intel/falcon-7b-instruct-int4-inc",autogptq=True,gptq_use_triton=True --device cuda:0 --tasks lambada_openai,hellaswag,piqa,winogrande,truthfulqa_mc1,openbookqa,boolq,arc_easy,arc_challenge,mmlu --batch_size 32
| Metric | BF16 | INT4 |
|---|---|---|
| Avg. | 0.5286 | 0.5259 |
| mmlu | 0.2453 | 0.2482 |
| lambada_openai | 0.6427 | 0.6404 |
| hellaswag | 0.5159 | 0.5128 |
| winogrande | 0.6669 | 0.6598 |
| piqa | 0.7856 | 0.7758 |
| truthfulqa_mc1 | 0.2913 | 0.2864 |
| openbookqa | 0.3000 | 0.3100 |
| boolq | 0.7089 | 0.7034 |
| arc_easy | 0.7294 | 0.7201 |
| arc_challenge | 0.4002 | 0.4019 |
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} }