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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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--- |
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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. |
