license: apache-2.0
datasets:
- NeelNanda/pile-10k
Model Details: gemma-7b-int4-inc
This model is an int4 model with group_size 128 of google/gemma-7b generated by intel/auto-round. 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 google/gemma-7b \
--device 0 \
--group_size 128 \
--bits 4 \
--iters 1000 \
--enable_minmax_tuning \
--minmax_lr 2e-3 \
--low_gpu_mem_usage \
--deployment_device 'gpu' \
--scale_dtype 'fp32' \
--eval_bs 32 \
--output_dir "./tmp_autoround" \
--amp
Evaluate the model
Install lm-eval-harness 0.4.2 from source. Install the latest AutoGPTQ from source first
lm_eval --model hf --model_args pretrained="Intel/gemma-7b-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. | 0.6302 | 0.6262 |
| mmlu | 0.6199 | 0.6111 |
| lambada_openai | 0.7316 | 0.7252 |
| hellaswag | 0.6059 | 0.6033 |
| winogrande | 0.7506 | 0.7451 |
| piqa | 0.8014 | 0.8058 |
| truthfulqa_mc1 | 0.3121 | 0.2889 |
| openbookqa | 0.3220 | 0.3380 |
| boolq | 0.8339 | 0.8281 |
| arc_easy | 0.8253 | 0.8152 |
| arc_challenge | 0.4991 | 0.5017 |
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} }