--- base_model: zai-org/GLM-5.2 language: - en - zh library_name: transformers license: mit pipeline_tag: text-generation tags: - gguf - quantized - llama.cpp - glm --- # GLM-5.2 (Q4_K_M GGUF) This repository contains the **Q4_K_M quantized GGUF** version of [Z.ai's GLM-5.2](https://huggingface.co/zai-org/GLM-5.2). Due to the size of the model, the GGUF has been split into 8 parts: * `GLM-5.2-Q4_K_M-00001-of-00008.gguf` * `GLM-5.2-Q4_K_M-00002-of-00008.gguf` * `...` * `GLM-5.2-Q4_K_M-00008-of-00008.gguf` ### How to use split GGUF files To run this model, you need to download all 8 files into the same directory. Modern versions of [llama.cpp](https://github.com/ggerganov/llama.cpp) and compatible UIs (like LM Studio, Ollama, or text-generation-webui) will automatically detect and load all parts as long as you point them to the first file. **Using `llama.cpp` via CLI:** ```bash ./llama-cli -m models/GLM-5.2-Q4_K_M-00001-of-00008.gguf -p "Your prompt here" -n 512 ``` *(Alternatively, you can merge them back into a single file using the `llama-gguf-split` tool provided with llama.cpp, though it is not strictly necessary).* --- *The following is the original model card information for GLM-5.2:* 👋 Join our WeChat or Discord community. 📖 Check out the GLM-5.2 blog and GLM-5 Technical report . 📍 Use GLM-5.2 API services on Z.ai API Platform. 🔜 Try GLM-5.2 here . [ Paper ] [ GitHub ] ## Introduction We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a **solid 1M-token context**. GLM-5.2's new capabilities include: - **Solid 1M Context:** A solid 1M-token context that stably sustains long-horizon work - **Advanced Coding with Flexible Effort**: Stronger coding capabilities with multiple thinking effort levels to balance performance and latency - **Improved Architecture**: We propose [IndexShare](https://arxiv.org/abs/2603.12201), which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9× at a 1M context length. We also improve GLM-5.2’s MTP layer for speculative decoding, increasing the acceptance length by up to 20% - **Pure Open**: An MIT open-source license — no regional limits, technical access without borders  ## Benchmark |Benchmark|GLM-5.2|GLM-5.1|Qwen3.7-Max|MiniMax M3|DeepSeek-V4-Pro|Claude Opus 4.8|GPT-5.5|Gemini 3.1 Pro| |:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |Reasoning||||||||||| |HLE|40.5|31|41.4|37|37.7|49.8*|41.4*|45| |HLE (w/ Tools)|54.7|52.3|53.5|-|48.2|57.9*|52.2*|51.4*| |CritPt|20.9|4.6|13.4|3.7|12.9|20.9|27.1|17.7| |AIME 2026|99.2|95.3|97|-|94.6|95.7|98.3|98.2| |HMMT Nov. 2025|94.4|94|95|84.4|94.4|96.5|96.5|94.8| |HMMT Feb. 2026|92.5|82.6|97.1|84.4|95.2|96.7|96.7|87.3| |IMOAnswerBench|91.0|83.8|90|-|89.8|83.5|-|81| |GPQA-Diamond|91.2|86.2|90|93|90.1|93.6|93.6|94.3| |Coding||||||||||| |SWE-bench Pro|62.1|58.4|60.6|59|55.4|69.2|58.6|54.2| |NL2Repo|48.9|42.7|47.2|42.1|35.5|69.7|50.7|33.4| |DeepSWE|46.2|18|18|20|8|58|70|10| |ProgramBench|63.7|50.9|-|-|47.8|71.9|70.8|39.5| |Terminal Bench 2.1 (Terminus-2)|81.0|63.5|75|65|64| 85|84|74| |Terminal Bench 2.1 (Best Reported Harness)|82.7|69|-|-|-|78.9|83.4|70.7| |FrontierSWE (Dominance)|74.4|30.5|-|-|29.0|75.1|72.6|39.6| |PostTrainBench|34.3|20.1|-|-|-|37.2|28.4|21.6| |SWE-Marathon|13.0|1.0|-|-|-|26.0|12.0|4.0| |Agentic||||||||||| |MCP-Atlas (Public Set)|76.8|71.8|76.4|74.2|73.6|77.8|75.3|69.2| |Tool-Decathlon|48.2|40.7|-|-|52.8|59.9|55.6|48.8| ## Serve GLM-5.2 Locally GLM-5.2 supports deployment with the following frameworks. Feel free to try them out: - [SGLang](https://github.com/sgl-project/sglang) (v0.5.13.post1+) — see [cookbook](https://cookbook.sglang.io/autoregressive/GLM/GLM-5.2) - [vLLM](https://github.com/vllm-project/vllm) (v0.23.0+) — see [recipes](https://recipes.vllm.ai/zai-org/GLM-5.2) - [Transformers](https://github.com/huggingface/transformers) (v0.5.12+) — see [transformers docs](https://github.com/huggingface/transformers/blob/main/docs/source/en/model_doc/glm_moe_dsa.md) - [KTransformers](https://github.com/kvcache-ai/ktransformers) (v0.5.12+) — see [tutorial](https://github.com/kvcache-ai/ktransformers/blob/main/doc/en/kt-kernel/GLM-5.2-Tutorial.md) - For deployment on the `Ascend NPU` platform, inference frameworks such as vLLM-Ascend, xLLM and SGLang are supported — see [here](github.com/zai-org/GLM-5/blob/main/example/ascend.md). ## Citation If you find GLM-5.2 useful in your research, please cite our technical report: ```bibtex @misc{glm5team2026glm5vibecodingagentic, title={GLM-5: from Vibe Coding to Agentic Engineering}, author={GLM-5-Team and : and Aohan Zeng and Xin Lv and Zhenyu Hou and Zhengxiao Du and Qinkai Zheng and Bin Chen and Da Yin and Chendi Ge and Chenghua Huang and Chengxing Xie and Chenzheng Zhu and Congfeng Yin and Cunxiang Wang and Gengzheng Pan and Hao Zeng and Haoke Zhang and Haoran Wang and Huilong Chen and Jiajie Zhang and Jian Jiao and Jiaqi Guo and Jingsen Wang and Jingzhao Du and Jinzhu Wu and Kedong Wang and Lei Li and Lin Fan and Lucen Zhong and Mingdao Liu and Mingming Zhao and Pengfan Du and Qian Dong and Rui Lu and Shuang-Li and Shulin Cao and Song Liu and Ting Jiang and Xiaodong Chen and Xiaohan Zhang and Xuancheng Huang and Xuezhen Dong and Yabo Xu and Yao Wei and Yifan An and Yilin Niu and Yitong Zhu and Yuanhao Wen and Yukuo Cen and Yushi Bai and Zhongpei Qiao and Zihan Wang and Zikang Wang and Zilin Zhu and Ziqiang Liu and Zixuan Li and Bojie Wang and Bosi Wen and Can Huang and Changpeng Cai and Chao Yu and Chen Li and Chengwei Hu and Chenhui Zhang and Dan Zhang and Daoyan Lin and Dayong Yang and Di Wang and Ding Ai and Erle Zhu and Fangzhou Yi and Feiyu Chen and Guohong Wen and Hailong Sun and Haisha Zhao and Haiyi Hu and Hanchen Zhang and Hanrui Liu and Hanyu Zhang and Hao Peng and Hao Tai and Haobo Zhang and He Liu and Hongwei Wang and Hongxi Yan and Hongyu Ge and Huan Liu and Huanpeng Chu and Jia'ni Zhao and Jiachen Wang and Jiajing Zhao and Jiamin Ren and Jiapeng Wang Barb and Jiaxin Zhang and Jiayi Gui and Jiayue Zhao and Jijie Li and Jing An and Jing Li and Jingwei Yuan and Jinhua Du and Jinxin Liu and Junkai Zhi and Junwen Duan and Kaiyue Zhou and Kangjian Wei and Ke Wang and Keyun Luo and Laiqiang Zhang and Leigang Sha and Liang Xu and Lindong Wu and Lintao Ding and Lu Chen and Minghao Li and Nianyi Lin and Pan Ta and Qiang Zou and Rongjun Song and Ruiqi Yang and Shangqing Tu and Shangtong Yang and Shaoxiang Wu and Shengyan Zhang and Shijie Li and Shuang Li and Shuyi Fan and Wei Qin and Wei Tian and Weining Zhang and Wenbo Yu and Wenjie Liang and Xiang Kuang and Xiangmeng Cheng and Xiangyang Li and Xiaoquan Yan and Xiaowei Hu and Xiaoying Ling and Xing Fan and Xingye Xia and Xinyuan Zhang and Xinze Zhang and Xirui Pan and Xu Zou and Xunkai Zhang and Yadi Liu and Yandong Wu and Yanfu Li and Yidong Wang and Yifan Zhu and Yijun Tan and Yilin Zhou and Yiming Pan and Ying Zhang and Yinpei Su and Yipeng Geng and Yong Yan and Yonglin Tan and Yuean Bi and Yuhan Shen and Yuhao Yang and Yujiang Li and Yunan Liu and Yunqing Wang and Yuntao Li and Yurong Wu and Yutao Zhang and Yuxi Duan and Yuxuan Zhang and Zezhen Liu and Zhengtao Jiang and Zhenhe Yan and Zheyu Zhang and Zhixiang Wei graves and Zhuo Chen and Zhuoer Feng and Zijun Yao and Ziwei Chai and Ziyuan Wang and Zuzhou Zhang and Bin Xu and Minlie Huang and Hongning Wang and Juanzi Li and Yuxiao Dong and Jie Tang}, year={2026}, eprint={2602.15763}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={[https://arxiv.org/abs/2602.15763](https://arxiv.org/abs/2602.15763)}, } ```
