---
license: apache-2.0
datasets:
- teowu/Q-Instruct
language:
- en
library_name: transformers
---

```bibtex
@misc{wu2023qinstruct,
      title={Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models}, 
      author={Haoning Wu and Zicheng Zhang and Erli Zhang and Chaofeng Chen and Liang Liao and Annan Wang and Kaixin Xu and Chunyi Li and Jingwen Hou and Guangtao Zhai and Geng Xue and Wenxiu Sun and Qiong Yan and Weisi Lin},
      year={2023},
      eprint={2311.06783},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```

```bibtex
@misc{ye2023mplugowl2,
      title={mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration}, 
      author={Qinghao Ye and Haiyang Xu and Jiabo Ye and Ming Yan and Anwen Hu and Haowei Liu and Qi Qian and Ji Zhang and Fei Huang and Jingren Zhou},
      year={2023},
      eprint={2311.04257},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** Q-Future Project @S-Lab NTU, led by @teowu
- **Model type:** Multi-modality Causal Language Model
- **Language(s) (NLP):** English
- **License:** Apache License
- **Finetuned from model [optional]:** mPLUG-Owl2

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/Q-Future/Q-Instruct
- **Paper [optional]:** https://arxiv.org/abs/2311.06783
- **Demo [optional]:** https://huggingface.co/spaces/teowu/Q-Instruct-on-mPLUG-Owl-2

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

Install:

```shell
git clone https://github.com/X-PLUG/mPLUG-Owl.git
cd mPLUG_Owl/mPLUG_Owl2/ 
pip install -e .
```

Use:

```python
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from mplug_owl2.mm_utils import get_model_name_from_path
from eval_scripts.mplug_owl_2.run_mplug_owl2 import eval_model
model_path = "teowu/mplug_owl2_7b_448_qinstruct_preview_v0.1" 
prompt = "Rate the quality of the image. Think step by step."
image_file = "fig/sausage.jpg"
args = type('Args', (), {
    "model_path": model_path,
    "model_base": None,
    "model_name": get_model_name_from_path(model_path),
    "query": prompt,
    "conv_mode": None,
    "image_file": image_file,
    "sep": ",",
})()
eval_model(args)
```

### Downstream Use [optional]

Not Yet Supported.

### Out-of-Scope Use

This model should be used for low-level visual perception and understanding tasks. It is not intended as a general-purpose visual assistant.


## Bias, Risks, and Limitations

See our paper section F.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data


https://huggingface.co/datasets/teowu/Q-Instruct

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

TBA.


## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** NVIDIA A100 80G
- **Hours used:** 256 GPU Hours (32 GPU*8 hours)
- **Cloud Provider:** N/A
- **Compute Region:** Asia Pacific
- **Carbon Emitted:** N/A



## Model Card Contact

Haoning Wu, @teowu