Model Details
This model is an int4 model with group_size 128 of microsoft/phi-2 generated by intel/auto-round.
How To Use
Use the model
INT4 Inference with AutoGPTQ
Install AutoGPTQ from source first
from transformers import AutoModelForCausalLM, AutoTokenizer
quantized_model_dir = "Intel/phi-2-int4-inc"
tokenizer = AutoTokenizer.from_pretrained(quantized_model_dir)
model = AutoModelForCausalLM.from_pretrained(quantized_model_dir, device_map="auto", trust_remote_code=True)
text = "There is a girl who likes adventure,"
inputs = tokenizer(text, return_tensors="pt", return_attention_mask=False).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=50)
text = tokenizer.batch_decode(outputs)[0]
print(text)
Evaluate the model
Install lm-eval-harness from source, and the git id f3b7917091afba325af3980a35d8a6dcba03dc3f is used
Since we encountered an issue evaluating this model with lm-eval, we opted to evaluate the qdq model instead. In our assessment, we found that its accuracy closely matches that of the real quantized model in most cases except for some small models like opt-125m.
| Metric | FP16 | INT4 qdq |
|---|---|---|
| Avg. | 0.6155 | 0.6163 |
| mmlu | 0.5448 | 0.5417 |
| lambada_openai | 0.6268 | 0.6225 |
| hellaswag | 0.5585 | 0.5498 |
| winogrande | 0.7530 | 0.7545 |
| piqa | 0.7867 | 0.7824 |
| truthfulqa_mc1 | 0.3133 | 0.3060 |
| openbookqa | 0.4000 | 0.4100 |
| boolq | 0.8339 | 0.8327 |
| rte | 0.6245 | 0.6643 |
| arc_easy | 0.7997 | 0.7955 |
| arc_challenge | 0.5290 | 0.5196 |
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 microsoft/phi-2 \
--device 0 \
--group_size 128 \
--bits 4 \
--iters 1000 \
--enable_minmax_tuning \
--disable_quanted_input \
--deployment_device 'gpu' \
--scale_dtype 'fp32' \
--eval_bs 32 \
--output_dir "./tmp_autoround" \
--amp
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:
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} }
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