teowu/mplug_owl2_7b_448_qinstruct_preview_v0.1

@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}
}

@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

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

Model Description

• 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]

• 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

Direct Use

Install:

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

Use:

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

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

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

TBA.

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• 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