GLM-4V-9B

GLM-4V-9B is an open source multimodal version of the latest generation of pre-trained models in the GLM-4 series launched by Zhipu AI. GLM-4V-9B has the ability to conduct multi-round conversations in Chinese and English at a high resolution of 1120 * 1120. In multimodal evaluations of comprehensive Chinese and English abilities, perceptual reasoning, text recognition, and chart understanding, GLM-4V-9B has shown superior performance over GPT-4-turbo-2024-04-09, Gemini 1.0 Pro, Qwen-VL-Max, and Claude 3 Opus.

Multimodal

GLM-4V-9B is a multimodal language model with visual understanding capabilities. The evaluation results of its related classic tasks are as follows:

	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

This repository is the model repository of GLM-4V-9B, supporting 8K context length.

Quick Start

Welcome to visit our github to view more execution codes.


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 = 'discribe this image'
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]))

License

The use of the GLM-4 model weights needs to comply with the LICENSE.

Citation

If you find our work helpful, please consider citing the following papers.

@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}
}