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📍Experience the larger-scale CogVLM model (GLM-4V) on the ZhipuAI Open Platform.
We launch a new generation of CogVLM2 series of models and open source two models based on Meta-Llama-3-8B-Instruct. Compared with the previous generation of CogVLM open source models, the CogVLM2 series of open source models have the following improvements:
- Significant improvements in many benchmarks such as
TextVQA,DocVQA. - Support 8K content length.
- Support image resolution up to 1344 * 1344.
- Provide an open source model version that supports both Chinese and English.
You can see the details of the CogVLM2 family of open source models in the table below:
| Model name | cogvlm2-llama3-chat-19B | cogvlm2-llama3-chinese-chat-19B |
|---|---|---|
| Base Model | Meta-Llama-3-8B-Instruct | Meta-Llama-3-8B-Instruct |
| Language | English | Chinese, English |
| Model size | 19B | 19B |
| Task | Image understanding, dialogue model | Image understanding, dialogue model |
| Model link | 🤗 Huggingface 🤖 ModelScope 💫 Wise Model | 🤗 Huggingface 🤖 ModelScope 💫 Wise Model |
| Demo Page | 📙 Official Page | 📙 Official Page 🤖 ModelScope |
| Int4 model | Not yet launched | Not yet launched |
| Text length | 8K | 8K |
| Image resolution | 1344 * 1344 | 1344 * 1344 |
Our open source models have achieved good results in many lists compared to the previous generation of CogVLM open source models. Its excellent performance can compete with some non-open source models, as shown in the table below:
| Model | Open Source | LLM Size | TextVQA | DocVQA | ChartQA | OCRbench | MMMU | MMVet | MMBench |
|---|---|---|---|---|---|---|---|---|---|
| CogVLM1.1 | ✅ | 7B | 69.7 | - | 68.3 | 590 | 37.3 | 52.0 | 65.8 |
| LLaVA-1.5 | ✅ | 13B | 61.3 | - | - | 337 | 37.0 | 35.4 | 67.7 |
| Mini-Gemini | ✅ | 34B | 74.1 | - | - | - | 48.0 | 59.3 | 80.6 |
| LLaVA-NeXT-LLaMA3 | ✅ | 8B | - | 78.2 | 69.5 | - | 41.7 | - | 72.1 |
| LLaVA-NeXT-110B | ✅ | 110B | - | 85.7 | 79.7 | - | 49.1 | - | 80.5 |
| InternVL-1.5 | ✅ | 20B | 80.6 | 90.9 | 83.8 | 720 | 46.8 | 55.4 | 82.3 |
| QwenVL-Plus | ❌ | - | 78.9 | 91.4 | 78.1 | 726 | 51.4 | 55.7 | 67.0 |
| Claude3-Opus | ❌ | - | - | 89.3 | 80.8 | 694 | 59.4 | 51.7 | 63.3 |
| Gemini Pro 1.5 | ❌ | - | 73.5 | 86.5 | 81.3 | - | 58.5 | - | - |
| GPT-4V | ❌ | - | 78.0 | 88.4 | 78.5 | 656 | 56.8 | 67.7 | 75.0 |
| CogVLM2-LLaMA3 | ✅ | 8B | 84.2 | 92.3 | 81.0 | 756 | 44.3 | 60.4 | 80.5 |
| CogVLM2-LLaMA3-Chinese | ✅ | 8B | 85.0 | 88.4 | 74.7 | 780 | 42.8 | 60.5 | 78.9 |
All reviews were obtained without using any external OCR tools ("pixel only").
This open source repos will help developers to quickly get started with the basic calling methods of the CogVLM2 open source model, fine-tuning examples, OpenAI API format calling examples, etc. The specific project structure is as follows, you can click to enter the corresponding tutorial link:
- basic_demo - Basic calling methods include model inference calling methods such as CLI, WebUI and OpenAI API. If you are using the CogVLM2 open source model for the first time, we recommend you start here.
- finetune_demo - Fine-tuning examples, including examples of fine-tuning language models.
This model is released under the CogVLM2 CogVLM2 LICENSE. For models built with Meta Llama 3, please also adhere to the LLAMA3_LICENSE.
If you find our work helpful, please consider citing the following papers
@misc{wang2023cogvlm,
title={CogVLM: Visual Expert for Pretrained Language Models},
author={Weihan Wang and Qingsong Lv and Wenmeng Yu and Wenyi Hong and Ji Qi and Yan Wang and Junhui Ji and Zhuoyi Yang and Lei Zhao and Xixuan Song and Jiazheng Xu and Bin Xu and Juanzi Li and Yuxiao Dong and Ming Ding and Jie Tang},
year={2023},
eprint={2311.03079},
archivePrefix={arXiv},
primaryClass={cs.CV}
}