Open-Weight Model Families · 2021

GLM: General Language Model Pretraining with Autoregressive Blank Infilling / GLM-130B

Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Zhilin Yang, Jie Tang, Aohan Zeng

Introduced the GLM pretraining objective — autoregressive blank infilling — which unifies bidirectional context and left-to-right generation in a single model, and scaled it into GLM-130B, an openly released English–Chinese bilingual foundation model.

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Plain-language summary

GLM masks spans of text and trains the model to regenerate them autoregressively while attending bidirectionally to the surrounding context, giving one model both the understanding strengths of masked encoders like BERT and the generation ability of decoder-only models. The team scaled the recipe to GLM-130B, a 130-billion-parameter bilingual model released with open weights and INT4 inference support so it could run on modest hardware. It anchored the GLM/ChatGLM family from Zhipu AI and Tsinghua and became one of the more capable early open bilingual foundation models.

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Provenance

Record ID
P-366
Record created
2026-07-13
Last reviewed
2026-07-14
Record version
2

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