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---
license: apache-2.0
datasets:
- NeelNanda/pile-10k
---
## Model Details
This model is an int4 model with group_size 32 sym of [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct) generated by [intel/auto-round](https://github.com/intel/auto-round), we will generate group_size 128 later
## How To Use
### INT4 Inference with AutoGPTQ
```python
##pip install auto-gptq==0.7.1
from transformers import AutoModelForCausalLM,AutoTokenizer
quantized_model_dir = "Intel/Qwen2-1.5B-Insturct-int4-inc"
tokenizer = AutoTokenizer.from_pretrained(quantized_model_dir)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
text = "There is a girl who likes adventure,"
text = "Once upon a time,"
inputs = tokenizer(text, return_tensors="pt").to(model.device)
print(tokenizer.decode(model.generate(**inputs, max_new_tokens=50, do_sample=False)[0]))
##There is a girl who likes adventure, she is always looking for new things to do. She is very adventurous and loves to try new things. She is always looking for new things to do and is always looking for new things to try. She is always looking for new things to do and
##Once upon a time, there was a young boy named Jack. He lived in a small village in England. Jack was very curious and loved to explore the world around him. One day, he decided to go on a journey to find a magical tree that could grant him three
```
### Evaluate the model
pip3 install lm-eval==0.4.2
```bash
lm_eval --model hf --model_args pretrained="Intel/Qwen2-1.5B-Instuct-int4-inc" --device cuda:0 --tasks lambada_openai,hellaswag,piqa,winogrande,truthfulqa_mc1,openbookqa,boolq,arc_easy,arc_challenge,mmlu --batch_size 16
```
| Metric-1.5B-instruct | BF16 | INT4 |
| -------------------- | ------ | --------- |
| Avg | 0.5518 | 0.5474 |
| mmlu | 0.5565 | 0.5499 |
| lambada_openai | 0.6484 | 0.6412 |
| hellaswag | 0.4935 | 0.4859 |
| winogrande | 0.6543 | 0.6606 |
| piqa | 0.7612 | 0.7573 |
| truthfulqa_mc1 | 0.2827 | 0.2766 |
| openbookqa | 0.2780 | 0.274 |
| boolq | 0.7654 | 0.7639 |
| arc_easy | 0.7024 | 0.6949 |
| arc_challenge | 0.3754 | 0.3695 |
### Reproduce the model
Here is the sample command to reproduce the model
```bash
git clone https://github.com/intel/auto-round
cd auto-round/examples/language-modeling
pip install -r requirements.txt
python3 main.py \
--model_name Qwen/Qwen2-1.5B-Instruct \
--device 0 \
--group_size 32 \
--bits 4 \
--sym \
--iter 1000 \
--deployment_device 'gpu' \
--output_dir "./tmp_autoround"
```
## 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](https://github.com/intel/neural-compressor)
* Intel Extension for Transformers [link](https://github.com/intel/intel-extension-for-transformers)
## 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 and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} }
[arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round) |