---
title: MRLoRA_Experts
canonical_url: "https://www.modelscope.cn/models/MLLM-CL/MRLoRA_Experts"
md_url: "https://www.modelscope.cn/models/MLLM-CL/MRLoRA_Experts.md"
repository: MLLM-CL/MRLoRA_Experts
last_updated: 2025-10-10
license: "Apache License 2.0"
pipeline_tag: visual-question-answering
tasks:
  - visual-question-answering
base_model:
  - llava-hf/llava-1.5-7b-hf
  - OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-7B
base_model_relation: adapter
library_name:
  - pytorch
frameworks:
  - Pytorch
language:
  - en
downloads: 32
stars: 0
---

# MRLoRA_Experts

> MRLoRA_Experts - MLLM-CL 在 ModelScope 开源的模型。MLLM-CL Benchmark Description MLLM-CL is a novel benchmark encompassing domain and ability continual learning, where the former focuses on independently and identically distributed (IID) evaluation across evolving…

MLLM-CL/MRLoRA_Experts 是 ModelScope 魔搭社区上的visual-question-answering模型，采用 Apache License 2.0 许可，基于 llava-hf/llava-1.5-7b-hf、OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-7B 构建。

- **Repository**: MLLM-CL/MRLoRA_Experts
- **License**: Apache License 2.0
- **Tasks**: visual-question-answering
- **Base model**: llava-hf/llava-1.5-7b-hf, OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-7B
- **Downloads**: 32
- **Stars**: 0
- **Last updated**: 2025-10-10

Source: https://www.modelscope.cn/models/MLLM-CL/MRLoRA_Experts

---

## MLLM-CL Benchmark Description
MLLM-CL is a novel benchmark encompassing domain and ability continual learning, where the former focuses on independently and identically distributed (IID) evaluation across evolving mainstream domains, 
whereas the latter evaluates on non-IID scenarios with emerging model ability.
For more details, please refer to: 

**MLLM-CL: Continual Learning for Multimodal Large Language Models** [[paper](https://arxiv.org/abs/2506.05453)], [[HF paper](https://huggingface.co/papers/2506.05453)], [[code](https://github.com/bjzhb666/MLLM-CL/)].

[Hongbo Zhao](https://scholar.google.com/citations?user=Gs22F0UAAAAJ&hl=zh-CN), [Fei Zhu](https://impression2805.github.io/), [Haiyang Guo](https://ghy0501.github.io/guohaiyang0501.github.io/), [Meng Wang](https://moenupa.github.io/), Rundong Wang, [‪Gaofeng Meng](https://scholar.google.com/citations?hl=zh-CN&user=5hti_r0AAAAJ), [‪Zhaoxiang Zhang‬](https://scholar.google.com/citations?hl=zh-CN&user=qxWfV6cAAAAJ)

## Usage
This repo is used to open-source all the experts in MLLM-CL experiments, including 4 branches (DCL_InternVL, DCL_LLaVA, ACL_InternVL, ACL_LLaVA).

## Citation
```
@article{zhao2025mllm,
  title={MLLM-CL: Continual Learning for Multimodal Large Language Models},
  author={Zhao, Hongbo and Zhu, Fei and Guo, Haiyang and Wang, Meng and Wang, Rundong and Meng, Gaofeng and Zhang, Zhaoxiang},
  journal={arXiv preprint arXiv:2506.05453},
  year={2025}
}
```
## Contact
Please post an issue on our GitHub.

## About us: MLLM-CL Community

We are the members from MLLM-CL, an open-source community focused on Continual learning of Multimodal Large Language Models. 
If you are interested in our community, feel free to contact us on GitHub or by email.