Glm 4 9b

🚀 GLM-4-9B

GLM-4-9B is an open-source version in the latest generation of pre-trained models GLM-4 series launched by Zhipu AI. It excels in multiple aspects such as semantics, mathematics, reasoning, code, and knowledge.

🚀 Quick Start

On August 12, 2024, the code in this repository has been updated to use transformers>=4.44.0. Please update your dependencies in a timely manner.

GLM-4-9B is an open-source version in the latest generation of pre-trained models GLM-4 series launched by Zhipu AI. In the evaluations of multiple datasets in semantics, mathematics, reasoning, code, and knowledge, both GLM-4-9B and its human preference-aligned version GLM-4-9B-Chat have demonstrated superior performance over Llama-3-8B. In addition to multi-round conversations, GLM-4-9B-Chat also has advanced functions such as web browsing, code execution, custom tool invocation (Function Call), and long-text reasoning (supporting a maximum context of 128K). This generation of models has added multi-language support, covering 26 languages including Japanese, Korean, and German. We have also launched the GLM-4-9B-Chat-1M model supporting a 1M context length (approximately 2 million Chinese characters) and the multimodal model GLM-4V-9B based on GLM-4-9B. GLM-4V-9B has the ability for multi-round conversations in both Chinese and English at a high resolution of 1120 * 1120. In multi-modal evaluations in multiple aspects such as comprehensive Chinese and English abilities, perceptual reasoning, character recognition, and chart understanding, GLM-4V-9B has demonstrated superior performance over GPT-4-turbo-2024-04-09, Gemini 1.0 Pro, Qwen-VL-Max, and Claude 3 Opus.

We evaluated the GLM-4-9B base model on some typical tasks, and the results are as follows:

For more inference code and dependency information, please visit our github.

This repository is the base version of GLM-4-9B, supporting an 8K context length.

📄 License

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

📚 Documentation

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

@misc{glm2024chatglm,
      title={ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools}, 
      author={Team GLM and Aohan Zeng and Bin Xu and Bowen Wang and Chenhui Zhang and Da Yin and Diego Rojas and Guanyu Feng and Hanlin Zhao and Hanyu Lai and Hao Yu and Hongning Wang and Jiadai Sun and Jiajie Zhang and Jiale Cheng and Jiayi Gui and Jie Tang and Jing Zhang and Juanzi Li and Lei Zhao and Lindong Wu and Lucen Zhong and Mingdao Liu and Minlie Huang and Peng Zhang and Qinkai Zheng and Rui Lu and Shuaiqi Duan and Shudan Zhang and Shulin Cao and Shuxun Yang and Weng Lam Tam and Wenyi Zhao and Xiao Liu and Xiao Xia and Xiaohan Zhang and Xiaotao Gu and Xin Lv and Xinghan Liu and Xinyi Liu and Xinyue Yang and Xixuan Song and Xunkai Zhang and Yifan An and Yifan Xu and Yilin Niu and Yuantao Yang and Yueyan Li and Yushi Bai and Yuxiao Dong and Zehan Qi and Zhaoyu Wang and Zhen Yang and Zhengxiao Du and Zhenyu Hou and Zihan Wang},
      year={2024},
      eprint={2406.12793},
      archivePrefix={arXiv},
      primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
}