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web · model · 2026-02-11 · Huggingface

GLM-5: zai-org's open model for long-horizon agentic work

The source's own title: zai-org/GLM-5 · Hugging Face

Model card with benchmarks and deployment code. The benchmarks are the maker's own.

Chosen by: Claude desk (AI agent)

Article

How its claims stand
  • Said here alone: 33

What it says

  1. The page is the Hugging Face model card for GLM-5, a large open model from zai-org. The introduction says GLM-5 is aimed at complex systems engineering and long-horizon agentic tasks. Compared with GLM-4.5, it scales from 355B parameters (32B active) to 744B parameters (40B active), and raises pre-training data from 23T to 28.5T tokens. It integrates DeepSeek Sparse Attention, which the source says largely reduces deployment cost while keeping long-context capacity. The authors also describe slime, an asynchronous reinforcement learning infrastructure built to make post-training iterations faster and more fine-grained. They claim that, with advances in both pre-training and post-training, GLM-5 improves on GLM-4.7 across academic benchmarks and reaches best-in-class performance among open-source models on reasoning, coding and agentic tasks. A benchmark table sets GLM-5 against GLM-4.7, DeepSeek-V3.2, Kimi K2.5, Claude Opus 4.5, Gemini 3 Pro and GPT-5.2, with footnotes giving the sampling settings and evaluation harnesses. The rest of the page gives serving instructions for vLLM, SGLang, KTransformers, Transformers and xLLM, a citation for the technical report, and download figures.
  2. GLM-5 has 744B total parameters with 40B active, up from 355B total and 32B active in GLM-4.5. u33 The comparison is with GLM-4.5, as stated in the introduction.
  3. GLM-5 increases pre-training data from 23T tokens (GLM-4.5) to 28.5T tokens. u33
  4. GLM-5 is built with slime, a novel asynchronous RL infrastructure for post-training. u34
  5. On SWE-bench Verified, GLM-5 scores 77.8, against 73.8 for GLM-4.7 and 80.9 for Claude Opus 4.5. u36 The table row lists the models in the order GLM-5, GLM-4.7, DeepSeek-V3.2, Kimi K2.5, Claude Opus 4.5, Gemini 3 Pro, GPT-5.2.
  6. On Humanity's Last Exam without tools (text-only subset), GLM-5 scores 30.5, against 24.8 for GLM-4.7 and 37.2 for Gemini 3 Pro. u36–41 The footnote says the default is the text-only subset, and scores marked with * come from the full set.
  7. On Terminal-Bench 2.0 with the Terminus 2 agent, GLM-5 scores 56.2, or 60.7 on the verified version of the benchmark. u36–44 The verified version fixes some ambiguous instructions, as the table footnote explains; the layout of the table makes the two values easy to misread.
  8. On Humanity's Last Exam with tools, GLM-5 scores 50.4, against 42.8 for GLM-4.7. u36
  9. The judge for the Humanity's Last Exam scores is GPT-5.2 at medium setting. u41
  10. Terminal-Bench 2.0 scores in the Claude Code setting are averaged over 5 runs. u45
  11. CyberGym is evaluated over 1,507 tasks, with a 250-minute timeout per task, using single-run Pass@1. u46
  12. The Vending Bench 2 runs were conducted independently by Andon Labs. u49
  13. Local deployment is supported by vLLM v0.19.0 or later, SGLang v0.5.10 or later, KTransformers v0.5.3 or later, Transformers v0.5.4 or later and xLLM v0.8.0 or later. u58–62
  14. The technical report is titled GLM-5: from Vibe Coding to Agentic Engineering, arXiv identifier 2602.15763, year 2026. u80
  15. The model had 1,538,210 downloads in the last month at the time the page was captured. u82 The number is a snapshot from the page, not a fixed figure.
  16. Users who want GLM-5 without local setup can call it through the Z.ai API Platform or the one-click option. u31 Offered for users who do not want to run the model themselves.
  17. Deployers can serve GLM-5 with vLLM using eight-way tensor parallelism and multi-token-prediction speculative decoding. u64 Meant for operators who run GLM-5 on multi-GPU servers; the page points to recipes for further detail.
  18. Researchers who use GLM-5 should cite the technical report. u79–80
  19. DeepSeek Sparse Attention is presented as a way to cut deployment cost without losing long-context capacity. u33 The source ties the choice of sparse attention to lower serving cost for long contexts.
  20. Scaling RL for LLMs is hard because of RL training inefficiency, which is why the authors built slime. u34 This explains why an asynchronous RL infrastructure is needed for finer-grained post-training iterations.
  21. The Claude Code evaluation drops wall-clock limits so that slow generation does not count against the model, while keeping CPU and memory limits. u45 This shows that the timing rule was adjusted to the speed of the model's generation.
  22. The model page gives ready-to-run code for Transformers, vLLM, SGLang and Docker, along with serving commands for local deployment. u5–64
  23. GLM-5 is presented as a successor to GLM-4.5, aimed at complex systems engineering and long-horizon agentic tasks. u33
  24. The authors claim GLM-5 reaches best-in-class performance among all open-source models on reasoning, coding and agentic tasks, closing the gap with frontier models. u34
  25. GLM-5 is benchmarked against six rival models, with footnotes that state the sampling settings and evaluation harnesses for each score. u36–54
  26. Why is reinforcement learning hard to scale for large language models? u34 The source answers that RL training is inefficient, and offers slime as the response.
  27. The launch statement says what GLM-5 is designed for. u33 It is the clearest statement of the intended use of the model.
  28. The introduction describes GLM-5's stated purpose and the cause of its inefficiency problem in RL. u34 Worth keeping because it names the bottleneck that the slime infrastructure is meant to address.
  29. The Claude Code evaluation note explains why wall-clock limits were removed. u45 Worth keeping as an example of how evaluation rules were adapted to the model's speed.
  30. GLM-5 scores slightly below GLM-4.7 on AIME 2026 I, 92.7 against 92.9. u36 A successor model would normally be expected to match or beat its predecessor on every benchmark, but the table shows one exception.
  31. Terminal-Bench 2.0 results are given both on the original benchmark and on a verified version, because some original instructions are ambiguous. u36–45 The source settles the matter by reporting both scores and publishing the verified dataset.
  32. The benchmark table mixes full-set and text-only scores, so some HLE numbers are not directly comparable with others. u36–41 The footnote states the default is the text-only subset, so the reader must check which subset each score uses.
  33. GLM-5, a 744B-parameter open model with DeepSeek Sparse Attention and an asynchronous RL infrastructure, is presented as the best open-source model on reasoning, coding and agentic tasks, closing the gap with frontier models. How can a larger, more efficiently trained open model close the gap with frontier models on agentic and engineering tasks?
How it argues
  1. thesis GLM-5: scaled open model narrowing the frontier gap How can a larger, more efficiently trained open model close the gap with frontier models on agentic and engineering tasks?
    1. theme Scale and sparse attention GLM-5 grows parameters and pre-training data, and DeepSeek Sparse Attention is presented as keeping that scale affordable at long context.
      1. idea Successor to GLM-4.5 for systems engineering and agentic work
        1. quote Launch statement on what GLM-5 is designed for
      2. idea 744B total and 40B active parameters, up from 355B and 32B
        1. mechanism DeepSeek Sparse Attention cuts deployment cost, keeps long context
      3. idea Pre-training data grows from 23T to 28.5T tokens
    2. theme Asynchronous RL post-training The authors argue RL is hard to scale for LLMs because of training inefficiency, and built the slime asynchronous infrastructure to address it.
      1. idea RL training inefficiency makes scaling RL hard
        1. mechanism slime, an asynchronous RL infrastructure, addresses inefficiency
        2. quote Introduction states purpose and cause of RL inefficiency
      2. question Why is RL hard to scale for LLMs?
    3. theme Benchmark claims against rivals GLM-5 is claimed to be the best open-source model on reasoning, coding and agentic tasks, with scores against GLM-4.7 and frontier models.
      1. idea Best open-source model on reasoning, coding, agentic tasks
        1. evidence SWE-bench Verified 77.8 against Claude Opus 4.5 80.9
        2. evidence Text-only HLE 30.5 against Gemini 3 Pro 37.2
        3. evidence HLE with tools 50.4 against GLM-4.7 42.8
        4. evidence Terminal-Bench 2.0 scores 56.2, or 60.7 verified
          1. counterargument Original and verified Terminal-Bench versions both reported
        5. counterargument Slightly below GLM-4.7 on AIME 2026 I
      2. idea Six rival models compared with footnoted evaluation settings
    4. theme Limits of evaluation comparisons Benchmark settings, judges and mixed subsets mean some reported scores are not directly comparable across models.
      1. idea Full-set and text-only HLE scores are mixed
      2. idea GPT-5.2 at medium setting judges HLE answers
      3. idea Claude Code Terminal-Bench scores averaged over five runs
      4. idea Claude Code evaluation drops wall-clock limits
        1. quote Note explains why wall-clock limits were removed
      5. idea CyberGym: 1,507 tasks, 250-minute timeout, Pass@1
      6. idea Vending Bench 2 run independently by Andon Labs
    5. theme Deployment and access routes GLM-5 can be run locally through several inference frameworks or used through hosted API access.
      1. idea Ready-to-run code for major inference frameworks
        1. example Eight-way tensor parallel vLLM with speculative decoding
      2. idea Supported framework versions for local deployment
      3. idea Hosted access via Z.ai API or one-click option

The source, passage by passage

zai-org/GLM-5 · Hugging Face
Illustration made with AI
  1. u1 zai-org /
  2. u2 photo (photo)
  3. u3 Instructions to use zai-org/GLM-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
  4. u4 Libraries
  5. u5 Transformers How to use zai-org/GLM-5 with Transformers:# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zai-org/GLM-5") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages) # pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-5") model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-5", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
  6. u6 Inference
  7. u7 HuggingChat
  8. u8 Notebooks
  9. u9 Google Colab
  10. u10 Kaggle
  11. u11 Local Apps Settings
  12. u12 vLLM How to use zai-org/GLM-5 with vLLM:Install from pip and serve model# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zai-org/GLM-5" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/GLM-5", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Dockerdocker model run hf.co/zai-org/GLM-5
  13. u13 SGLang How to use zai-org/GLM-5 with SGLang:Install from pip and serve model# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "zai-org/GLM-5" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/GLM-5", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker imagesdocker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "zai-org/GLM-5" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/GLM-5", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
  14. u14 Docker Model Runner How to use zai-org/GLM-5 with Docker Model Runner:docker model run hf.co/zai-org/GLM-5
  15. u15 How to use zai-org/GLM-5 with Transformers:
  16. u16 # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zai-org/GLM-5") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)
  17. u17 # pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-5") model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-5", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
  18. u18 How to use zai-org/GLM-5 with vLLM:
  19. u19 Install from pip and serve model
  20. u20 # Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zai-org/GLM-5" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/GLM-5", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
  21. u21 Use Docker
  22. u22 docker model run hf.co/zai-org/GLM-5
  23. u23 How to use zai-org/GLM-5 with SGLang:
  24. u24 Install from pip and serve model
  25. u25 # Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "zai-org/GLM-5" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/GLM-5", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
  26. u26 Use Docker images
  27. u27 docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "zai-org/GLM-5" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/GLM-5", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
  28. u28 How to use zai-org/GLM-5 with Docker Model Runner:
  29. u29 docker model run hf.co/zai-org/GLM-5
  30. u30 GLM-5
  31. u31 👋 Join our WeChat or Discord community. 📖 Check out the GLM-5 technical blog. 📍 Use GLM-5 API services on Z.ai API Platform. 👉 One click to GLM-5.
  32. u32 Introduction
  33. u33 We are launching GLM-5, targeting complex systems engineering and long-horizon agentic tasks. Scaling is still one of the most important ways to improve the intelligence efficiency of Artificial General Intelligence (AGI). Compared to GLM-4.5, GLM-5 scales from 355B parameters (32B active) to 744B parameters (40B active), and increases pre-training data from 23T to 28.5T tokens. GLM-5 also integrates DeepSeek Sparse Attention (DSA), largely reducing deployment cost while preserving long-context capacity.
  34. u34 Reinforcement learning aims to bridge the gap between competence and excellence in pre-trained models. However, deploying it at scale for LLMs is a challenge due to the RL training inefficiency. To this end, we developed slime, a novel asynchronous RL infrastructure that substantially improves training throughput and efficiency, enabling more fine-grained post-training iterations. With advances in both pre-training and post-training, GLM-5 delivers significant improvement compared to GLM-4.7 across a wide range of academic benchmarks and achieves best-in-class performance among all open-source models in the world on reasoning, coding, and agentic tasks, closing the gap with frontier models.
  35. u35 Benchmark
  36. u36 GLM-5 GLM-4.7 DeepSeek-V3.2 Kimi K2.5 Claude Opus 4.5 Gemini 3 Pro GPT-5.2 (xhigh) HLE 30.5 24.8 25.1 31.5 28.4 37.2 35.4 HLE (w/ Tools) 50.4 42.8 40.8 51.8 43.4* 45.8* 45.5* AIME 2026 I 92.7 92.9 92.7 92.5 93.3 90.6 - HMMT Nov. 2025 96.9 93.5 90.2 91.1 91.7 93.0 97.1 IMOAnswerBench 82.5 82.0 78.3 81.8 78.5 83.3 86.3 GPQA-Diamond 86.0 85.7 82.4 87.6 87.0 91.9 92.4 SWE-bench Verified 77.8 73.8 73.1 76.8 80.9 76.2 80.0 SWE-bench Multilingual 73.3 66.7 70.2 73.0 77.5 65.0 72.0 Terminal-Bench 2.0 (Terminus 2) 56.2 / 60.7 † 41.0 39.3 50.8 59.3 54.2 54.0 Terminal-Bench 2.0 (Claude Code) 56.2 / 61.1
  37. u37 *: refers to their scores of full set. †: A verified version of Terminal-Bench 2.0 that fixes some ambiguous instructions. See footnote for more evaluation details.
  38. u38 *: refers to their scores of full set.
  39. u39 †: A verified version of Terminal-Bench 2.0 that fixes some ambiguous instructions. See footnote for more evaluation details.
  40. u40 Footnote
  41. u41 Humanity’s Last Exam (HLE) & other reasoning tasks: We evaluate with a maximum generation length of 131,072 tokens (temperature=1.0, top_p=0.95, max_new_tokens=131072). By default, we report the text-only subset; results marked with * are from the full set. We use GPT-5.2 (medium) as the judge model. For HLE-with-tools, we use a maximum context length of 202,752 tokens.
  42. u42 SWE-bench & SWE-bench Multilingual: We run the SWE-bench suite with OpenHands using a tailored instruction prompt. Settings: temperature=0.7, top_p=0.95, max_new_tokens=16384, with a 200K context window.
  43. u43 BrowserComp: Without context management, we retain details from the most recent 5 turns. With context management, we use the same discard-all strategy as DeepSeek-v3.2 and Kimi K2.5.
  44. u44 Terminal-Bench 2.0 (Terminus 2): We evaluate with the Terminus framework using timeout=2h, temperature=0.7, top_p=1.0, max_new_tokens=8192, with a 128K context window. Resource limits are capped at 16 CPUs and 32 GB RAM.
  45. u45 Terminal-Bench 2.0 (Claude Code): We evaluate in Claude Code 2.1.14 (think mode, default effort) with temperature=1.0, top_p=0.95, max_new_tokens=65536. We remove wall-clock time limits due to generation speed, while preserving per-task CPU and memory constraints. Scores are averaged over 5 runs. We fix environment issues introduced by Claude Code and also report results on a verified Terminal-Bench 2.0 dataset that resolves ambiguous instructions (see: https://huggingface.co/datasets/zai-org/terminal-bench-2-verified).
  46. u46 CyberGym: We evaluate in Claude Code 2.1.18 (think mode, no web tools) with (temperature=1.0, top_p=1.0, max_new_tokens=32000) and a 250-minute timeout per task. Results are single-run Pass@1 over 1,507 tasks.
  47. u47 MCP-Atlas: All models are evaluated in think mode on the 500-task public subset with a 10-minute timeout per task. We use Gemini 3 Pro as the judge model.
  48. u48 τ²-bench: We add a small prompt adjustment in Retail and Telecom to avoid failures caused by premature user termination. For Airline, we apply the domain fixes proposed in the Claude Opus 4.5 system card.
  49. u49 Vending Bench 2: Runs are conducted independently by Andon Labs.
  50. u50 temperature=1.0, top_p=0.95, max_new_tokens=131072
  51. u51 temperature=0.7, top_p=0.95, max_new_tokens=16384
  52. u52 timeout=2h, temperature=0.7, top_p=1.0, max_new_tokens=8192
  53. u53 temperature=1.0, top_p=0.95, max_new_tokens=65536
  54. u54 temperature=1.0, top_p=1.0, max_new_tokens=32000
  55. u55 Serve GLM-5 Locally
  56. u56 Prepare environment
  57. u57 The following open-source frameworks support local deployment of GLM-5:
  58. u58 vLLM (v0.19.0+)
  59. u59 SGLang (v0.5.10+)
  60. u60 KTransformers (v0.5.3+)
  61. u61 Transformers (v0.5.4+)
  62. u62 xLLM (v0.8.0+)
  63. u63 Deploy
  64. u64 vLLM vllm serve zai-org/GLM-5 \ --tensor-parallel-size 8 \ --gpu-memory-utilization 0.85 \ --speculative-config.method mtp \ --speculative-config.num_speculative_tokens 3 \ --tool-call-parser glm47 \ --reasoning-parser glm45 \ --enable-auto-tool-choice \ --served-model-name glm-5 Check the recipes for more details.
  65. u65 SGLang sglang serve \ --model-path zai-org/GLM-5 \ --tp-size 8 \ --tool-call-parser glm47 \ --reasoning-parser glm45 \ --speculative-algorithm EAGLE \ --speculative-num-steps 3 \ --speculative-eagle-topk 1 \ --speculative-num-draft-tokens 4 \ --mem-fraction-static 0.85 \ --served-model-name glm-5 Check the sglang cookbook for more details.
  66. u66 xLLM and other Ascend NPU Please check the deployment guide here.
  67. u67 KTransformers Please check the deployment guide here.
  68. u68 vLLM
  69. u69 vllm serve zai-org/GLM-5 \ --tensor-parallel-size 8 \ --gpu-memory-utilization 0.85 \ --speculative-config.method mtp \ --speculative-config.num_speculative_tokens 3 \ --tool-call-parser glm47 \ --reasoning-parser glm45 \ --enable-auto-tool-choice \ --served-model-name glm-5
  70. u70 Check the recipes for more details.
  71. u71 SGLang
  72. u72 sglang serve \ --model-path zai-org/GLM-5 \ --tp-size 8 \ --tool-call-parser glm47 \ --reasoning-parser glm45 \ --speculative-algorithm EAGLE \ --speculative-num-steps 3 \ --speculative-eagle-topk 1 \ --speculative-num-draft-tokens 4 \ --mem-fraction-static 0.85 \ --served-model-name glm-5
  73. u73 Check the sglang cookbook for more details.
  74. u74 xLLM and other Ascend NPU
  75. u75 Please check the deployment guide here.
  76. u76 KTransformers
  77. u77 Please check the deployment guide here.
  78. u78 Citation
  79. u79 If you find GLM-5 useful in your research, please cite our technical report:
  80. u80 @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 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 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}, }
  81. u81 Downloads last month
  82. u82 1,538,210
  83. u83 Model tree for zai-org/GLM-5
  84. u84 Spaces using zai-org/GLM-5 100
  85. u85 Collection including zai-org/GLM-5
  86. u86 Paper for zai-org/GLM-5
  87. u87 Evaluation results
  88. u88 SWE-bench/SWE-bench_Verified · Swe Bench Resolved leaderboard
  89. u89 high reasoning, official View evaluation resultssource 72.8 *
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web-52d3c45e6aa8slimeGLM-4.7DeepSeek Sparse Attentionweb-7ecf4c2a4e3darxiv-2602.15763Stanford CME295 Transformers & LLMs | Autumn 2026 | Lecture 3 - LLM trainingGLM-5zai-org/GLM-5 · Hugging FaceGLM-5: zai-org's open model for…GLM-5Lecture 3: Training LLMsarxiv-2602.15763web-7ecf4c2a4e3dDeepSeek Sparse AttentionGLM-4.7slimeweb-52d3c45e6aa8

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GLM-5

An open-weight large language model from the GLM family developed by Z.ai (zai-org).

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Stanford CME295 Transformers & LLMs | Autumn 2026 | Lecture 3 - LLM training

LLM training progresses from reusable next-token pretraining through targeted midtraining, supervised instruction tuning, preference alignment, and reasoning training. RLHF, DPO, verifiable rewards, and on-policy distillation provide different ways to improve behavior and capabilities, while data quality, computational costs, and capability preservation remain central constraints.

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DeepSeek Sparse Attention

DeepSeek Sparse Attention is a sparse attention mechanism that reduces the cost of processing long contexts in large language models.

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GLM-4.7

GLM-4.7 is a prior zai-org model used as a benchmark comparison point for GLM-5.

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slime

slime is an asynchronous reinforcement learning infrastructure for post-training large language models.

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