---
base_model:
- meta-llama/Llama-2-13b-chat-hf
datasets:
- NingLab/MMECInstruct
license: cc-by-4.0
library_name: transformers
pipeline_tag: image-text-to-text
---

# CASLIE-L

This repository contains the models for "[Captions Speak Louder than Images: Generalizing Foundation Models for E-commerce from High-quality Multimodal Instruction Data](https://huggingface.co/papers/2410.17337)".

**Project Page**: [https://ninglab.github.io/CASLIE/](https://ninglab.github.io/CASLIE/)
**Code Repository**: [https://github.com/ninglab/CASLIE](https://github.com/ninglab/CASLIE)

## Introduction
Leveraging multimodal data to drive breakthroughs in e-commerce applications through Multimodal Foundation Models (MFMs) is gaining increasing attention. This work introduces [MMECInstruct](https://huggingface.co/datasets/NingLab/MMECInstruct), the first-ever, large-scale, and high-quality multimodal instruction dataset for e-commerce. We also develop CASLIE, a simple, lightweight, yet effective framework for integrating multimodal information for e-commerce. Leveraging MMECInstruct, we fine-tune a series of e-commerce MFMs within CASLIE, denoted as CASLIE models.

## CASLIE Models
The CASLIE-L model is instruction-tuned from the large base model [Llama-2-13b-chat](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf).

## Sample Usage (Modality-unified Inference)
To conduct inference with the CASLIE models, refer to the following example directly from the [official GitHub repository](https://github.com/ninglab/CASLIE#modality-unified-inference).

`$model_path` is the path of the instruction-tuned model.

`$task` specifies the task to be tested.

`$output_path` specifies the path where you want to save the inference output.

Example:
```
python inference.py --model_path NingLab/CASLIE-M --task answerability_prediction --output_path ap.json
```

## Citation
```bibtex
@article{ling2024captions,
    title={Captions Speak Louder than Images (CASLIE): Generalizing Foundation Models for E-commerce from High-quality Multimodal Instruction Data},
    author={Ling, Xinyi and Peng, Bo and Du, Hanwen and Zhu, Zhihui and Ning, Xia},
    journal={arXiv preprint arXiv:2410.17337},
    year={2024}
}
```