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---
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license: apache-2.0
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datasets:
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- liuhaotian/LLaVA-Pretrain
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- lmms-lab/LLaVA-NeXT-Data
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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---
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[[Paper]](https://arxiv.org/abs/2407.17331) [[GitHub]](https://github.com/deepglint/unicom)
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## Model
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We used [**MLCD**](https://huggingface.co/DeepGlint-AI/mlcd-vit-large-patch14-336) as the Vision Encoder in [LLaVA-Next](https://huggingface.co/lmms-lab/llava-next-qwen-32b).
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## Data
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Our model was trained on publicly available data from the [LLaVA-Pretrain](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain) and [LLaVA-NeXT-Data](https://huggingface.co/datasets/lmms-lab/LLaVA-NeXT-Data) datasets.
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## How to eval
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```shell
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pip install lmms-eval==0.2.0
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CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
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python -m accelerate.commands.launch \
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--main_process_port=12581 \
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--num_processes=8 \
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-m lmms_eval \
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--model llava \
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--model_args pretrained=DeepGlint-AI/llava-mlcd-qwen2.5-7b,conv_template=qwen_1_5 \
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--tasks mmbench,mme,mmmu,ocrbench,scienceqa,scienceqa_img,seedbench,gqa,pope,textvqa_val,ai2d,chartqa,docvqa_val,infovqa_val,mmstar \
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--batch_size 1 \
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--log_samples \
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--log_samples_suffix mlcd_llava_qwen2_7b \
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--output_path ./log
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```
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## Performance and Limitations
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In our experiments, we replaced the CLIP model in [LLaVA-NeXT](https://github.com/LLaVA-VL/LLaVA-NeXT) with the MLCD model to demonstrate the performance of the MLCD model in Multimodal Large Language Models (MLLMs). For the language model, we used [Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B). The evaluation results show that the modified model performs exceptionally well across multiple benchmarks, validating the effectiveness of the MLCD model within MLLMs.
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| Vision Tower | MLCD (ViT_L_14_336px) | CLIP (ViT_L_14_336px) |
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|:----------------|:-------------|:-------------|
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| LLM | Qwen2.5-7B | Qwen2.5-7B |
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| AI2D | **76.98** | 73.15 |
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| ScienceQA_img | **78.09** | 76.35 |
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| GQA | **64.17** | 63.31 |
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| InfoVQA_val | **43.48** | 38.88 |
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| MMBench_cn_dev | **74.83** | 72.51 |
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| MMBench_en_dev | **76.37** | 74.57 |
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| MME(cognition) | **432** | 384 |
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| MME(perception) | **1598** | 1512 |
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| SeedBench | **68.20** | 66.80 |
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| SeedBench_img | **73.75** | 72.72 |
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| MMStar | **50.98** | 48.98 |
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| MMMU | **44.30** | 44.20 |
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| OCRBench | **531.00** | 525.00 |
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| ChartQA | **67.84** | 66.52 |
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| DocVQA_val | **76.46** | 75.21 |
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| POPE | 88.69 | **88.83** |
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| TextVQA_val | 61.69 | **62.47** |
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### C. Limitations
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Models with larger datasets will perform better on more tasks. We are currently training such models and will soon make them available.
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## Acknowledgments
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We would like to express our gratitude to [Yumeng Wang](https://huggingface.co/devymex) for his significant contributions to the experimental validation in MLLMs. |
