---
language:
  - en
pipeline_tag: text-generation
tags:
  - OpenVINO
  - Phi-3
  - PyTorch
  - weight_compression
  - optimum-intel
license: mit
 
library_name: transformers
---

# Phi-3-128K-Instruct-ov-fp16-int4-asym


## Model Description

This is a version of the original [Phi-3-128K-Instruct](https://huggingface.co/microsoft/Phi-3-128k-instruct) model, converted to OpenVINO™ IR (Intermediate Representation) format for optimized inference on Intel® hardware. This model is created using the procedures detailed in the [OpenVINO™ Notebooks](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks) repository.

## Intended Use
This model is designed for advanced natural language understanding and generation tasks, ideal for developers and researchers in both academic and commercial settings who require efficient AI capabilities for devices with limited computational power. It is not intended for use in creating or promoting harmful or illegal content, in accordance with the guidelines outlined in the Phi-3 Acceptable Use Policy.

## Licensing and Redistribution
This model is released under the [MIT license](https://huggingface.co/microsoft/Phi-3-128k-instruct/resolve/main/LICENSE). 

## Weight Compression Parameters
For more information on the parameters, refer to the [OpenVINO™ 2024.1.0 documentation](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html)

* mode: **INT4_ASYM**
* group_size: **128**
* ratio: **0.8**

## Running Model Inference

Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO™ backend:

```python
pip install --upgrade --upgrade-strategy eager "optimum[openvino]"

from optimum.intel.openvino import OVModelForCausalLM
from transformers import AutoTokenizer

model_id = "microsoft/Phi-3-128K-Instruct-ov-fp32-int4-asym"

# Initialize the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForCausalLM.from_pretrained(model_id)

pipeline = transformers.pipeline("text-generation", model=model, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto")
pipeline("i am in paris, plan me a 2 week trip")
```