README.md

---
license: apache-2.0
datasets:
- NeelNanda/pile-10k
---
## Model Details

This model is an int4 model with group_size128 and sym quantization of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)  generated by [intel/auto-round](https://github.com/intel/auto-round). If you need AutoGPTQ format, please load the model with revision 5973e3a



### How To Use


### INT4 Inference


```python
##pip install auto-round
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,
                                             ## revision="5973e3a" ##AutoGPTQ format
                                            )
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)
"""
There is a girl who likes adventure,
She loves to explore and to venture.
She travels to faraway lands,
And meets people from different lands.
She learns new languages and cultures,
And makes friends with all kinds of people.
She is curious and brave and
"""
```

### Intel Gaudi-2 INT4 Inference
docker image with Gaudi Software Stack is recommended. More details can be found in [Gaudi Guide](https://docs.habana.ai/en/latest/).
```python
import habana_frameworks.torch.core as htcore
import habana_frameworks.torch.hpu as hthpu

from auto_round import AutoRoundConfig
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).to('hpu').to(bfloat16)
text = "下面我来介绍一下阿里巴巴公司,"
inputs = tokenizer(text, return_tensors="pt").to(model.device)
print(tokenizer.decode(model.generate(**inputs, max_new_tokens=50, do_sample=False)[0]))

```


### Evaluate the model 

pip install lm-eval==0.4.4

```bash
auto-round --eval --model Intel/phi-2-int4-inc --device cuda:0 --tasks lambada_openai,hellaswag,piqa,winogrande,truthfulqa_mc1,openbookqa,boolq,arc_easy,arc_challenge,mmlu --batch_size 16
```



| Metric         | FP16   | INT4   |
| -------------- | ------ | ------ |
| Avg.           | 0.6131 | 0.6087 |
| mmlu           | 0.5334 | 0.5417 |
| lambada_openai | 0.6243 | 0.6088 |
| hellaswag      | 0.5581 | 0.5520 |
| winogrande     | 0.7522 | 0.7577 |
| piqa           | 0.7867 | 0.7911 |
| truthfulqa_mc1 | 0.3097 | 0.2962 |
| openbookqa     | 0.4040 | 0.3900 |
| boolq          | 0.8346 | 0.8333 |
| arc_easy       | 0.8001 | 0.7980 |
| arc_challenge  | 0.5282 | 0.5179 |



### Generate the model

Here is the sample command to generate the model

```bash
auto-round \
--model  microsoft/phi-2 \
--device 0 \
--group_size 128 \
--bits 4 \
--iters 1000 \
--nsamples 512 \
--format "auto_round" \
--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.