---
license: other
license_name: glm-4
license_link: https://huggingface.co/THUDM/glm-4v-9b/blob/main/LICENSE
 
language:
- zh
- en
tags:
- glm
- chatglm
- thudm


inference: false
---

# GLM-4V-9B

GLM-4V-9B 是智谱 AI 推出的最新一代预训练模型 GLM-4 系列中的开源多模态版本。
**GLM-4V-9B** 具备 1120 * 1120 高分辨率下的中英双语多轮对话能力，在中英文综合能力、感知推理、文字识别、图表理解等多方面多模态评测中，GLM-4V-9B 表现出超越 GPT-4-turbo-2024-04-09、Gemini
1.0 Pro、Qwen-VL-Max 和 Claude 3 Opus 的卓越性能。

### 多模态能力

GLM-4V-9B 是一个多模态语言模型，具备视觉理解能力，其相关经典任务的评测结果如下：

|                         | **MMBench-EN-Test** | **MMBench-CN-Test** | **SEEDBench_IMG** | **MMStar** | **MMMU** | **MME** | **HallusionBench** | **AI2D** | **OCRBench** |
|-------------------------|---------------------|---------------------|-------------------|------------|----------|---------|--------------------|----------|--------------|
|                         | 英文综合                | 中文综合                | 综合能力              | 综合能力       | 学科综合     | 感知推理    | 幻觉性                | 图表理解     | 文字识别         |
| **GPT-4o, 20240513**    | 83.4                | 82.1                | 77.1              | 63.9       | 69.2     | 2310.3  | 55                 | 84.6     | 736          |
| **GPT-4v, 20240409**    | 81                  | 80.2                | 73                | 56         | 61.7     | 2070.2  | 43.9               | 78.6     | 656          |
| **GPT-4v, 20231106**    | 77                  | 74.4                | 72.3              | 49.7       | 53.8     | 1771.5  | 46.5               | 75.9     | 516          |
| **InternVL-Chat-V1.5**  | 82.3                | 80.7                | 75.2              | 57.1       | 46.8     | 2189.6  | 47.4               | 80.6     | 720          |
| **LlaVA-Next-Yi-34B**   | 81.1                | 79                  | 75.7              | 51.6       | 48.8     | 2050.2  | 34.8               | 78.9     | 574          |
| **Step-1V**             | 80.7                | 79.9                | 70.3              | 50         | 49.9     | 2206.4  | 48.4               | 79.2     | 625          |
| **MiniCPM-Llama3-V2.5** | 77.6                | 73.8                | 72.3              | 51.8       | 45.8     | 2024.6  | 42.4               | 78.4     | 725          |
| **Qwen-VL-Max**         | 77.6                | 75.7                | 72.7              | 49.5       | 52       | 2281.7  | 41.2               | 75.7     | 684          |
| **GeminiProVision**     | 73.6                | 74.3                | 70.7              | 38.6       | 49       | 2148.9  | 45.7               | 72.9     | 680          |
| **Claude-3V Opus**      | 63.3                | 59.2                | 64                | 45.7       | 54.9     | 1586.8  | 37.8               | 70.6     | 694          |
| **GLM-4v-9B**           | 81.1                | 79.4                | 76.8              | 58.7       | 47.2     | 2163.8  | 46.6               | 81.1     | 786          |

**本仓库是 GLM-4V-9B 的模型仓库，支持`8K`上下文长度。**
## 运行模型

```python
import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda"

tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4v-9b", trust_remote_code=True)

query = '描述这张图片'
image = Image.open("your image").convert('RGB')
inputs = tokenizer.apply_chat_template([{"role": "user", "image": image, "content": query}],
                                       add_generation_prompt=True, tokenize=True, return_tensors="pt",
                                       return_dict=True)  # chat mode

inputs = inputs.to(device)
model = AutoModelForCausalLM.from_pretrained(
    "THUDM/glm-4v-9b",
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    trust_remote_code=True
).to(device).eval()

gen_kwargs = {"max_length": 2500, "do_sample": True, "top_k": 1}
with torch.no_grad():
    outputs = model.generate(**inputs, **gen_kwargs)
    outputs = outputs[:, inputs['input_ids'].shape[1]:]
    print(tokenizer.decode(outputs[0]))
```

## 协议

GLM-4 模型的权重的使用则需要遵循 [LICENSE](LICENSE)。

## 引用

如果你觉得我们的工作有帮助的话，请考虑引用下列论文。

```
@article{zeng2022glm,
  title={Glm-130b: An open bilingual pre-trained model},
  author={Zeng, Aohan and Liu, Xiao and Du, Zhengxiao and Wang, Zihan and Lai, Hanyu and Ding, Ming and Yang, Zhuoyi and Xu, Yifan and Zheng, Wendi and Xia, Xiao and others},
  journal={arXiv preprint arXiv:2210.02414},
  year={2022}
}
```

```
@inproceedings{du2022glm,
  title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling},
  author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie},
  booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={320--335},
  year={2022}
}
```

```
@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}
}
```