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📍Experience the larger-scale CogVLM model (GLM-4V) on the ZhipuAI Open Platform.
- 🔥🔥 News:
2024/5/24: We have released the Int4 version model, which requires only 16GB of video memory for inference. You can also run on-the-fly int4 version by passing--quant 4. - 🔥 News:
2024/5/20: We released the next generation model CogVLM2, which is based on llama3-8b and is equivalent (or better) to GPT-4V in most cases ! Welcome to download!
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 Demo | 📙 Official Demo 🤖 ModelScope |
| Int4 model | 🤗 Huggingface 🤖 ModelScope | 🤗 Huggingface 🤖 ModelScope |
| 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 folder includes:
- CLI demo.
- CLI demo with multiple GPUs .
- Web demo by chainlit.
- API server with OpenAI format.
- Int4 is enabled easily with
--quant 4with 16GB memory usage.
-
finetune_demo folder includes.
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}
}