From the model card
Fields below are copied from the tags and counters on the HuggingFace repository zai-org/GLM-5.2 at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- zai-org
- Pipeline tag
- text-generation
- Library
- Transformers
- Weight formats
- safetensors
- License tag
mit— read the license file in the repo before relying on it- Language tags
- English (en), Chinese (zh)
- Papers cited
- arXiv:2602.15763, arXiv:2603.12201
- Downloads (HF counter at last fetch)
- 1,185,123
- Likes (HF counter at last fetch)
- 5,072
- Model card
- https://huggingface.co/zai-org/GLM-5.2
Use cases
- Chinese-English bilingual document generation and summarization
- Conversational AI for Chinese-speaking user bases
- Research into MoE language model architectures from THUDM
- Comparison benchmark against Qwen and Baidu's Ernie family
Pros
- Exceptionally high community validation (4,417 likes, 667K+ downloads)
- MoE architecture enables large parameter counts with controlled inference cost
- Strong Chinese language coverage from a THUDM research lineage
- Published associated arxiv papers for academic citation
Cons
- MoE loading requires larger VRAM for all expert weights even at sparse activation
- glm_moe_dsa architecture requires GLM-specific inference code
- Model card detail level varies — English documentation often lags Chinese
- Licensing terms should be verified before commercial deployment
Tags
transformerssafetensorsglm_moe_dsatext-generationconversationalenzharxiv:2602.15763arxiv:2603.12201license:miteval-resultsendpoints_compatibleregion:us