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This model was contributed to Hugging Face Transformers on 2026-08-26.

GLM-5.3-Flash

Overview

The GLM-5.3-Flash model use this class. The implementation in transformers does not include an MTP layer.

GLM-5.3-Flash

GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks.

GLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. For the first time in the GLM series, we introduce a hybrid architecture combining sparse and linear attention, sharply reducing long-context serving costs while preserving precise long-context capabilities. The model also adopts Manifold-Constrained Hyper-Connections (mHC) to further improve scaling efficiency. Together with our latest 30T-token multimodal pre-training corpus, these changes enable GLM-5.3-Flash to deliver more intelligence with less compute.

Glm5NextConfig

[[autodoc]] Glm5NextConfig

Glm5NextTextConfig

[[autodoc]] Glm5NextTextConfig

Glm5NextVisionConfig

[[autodoc]] Glm5NextVisionConfig

Glm5NextPreTrainedModel

[[autodoc]] Glm5NextPreTrainedModel - forward

Glm5NextTextModel

[[autodoc]] Glm5NextTextModel - forward

Glm5NextModel

Glm5NextVisionModel

[[autodoc]] Glm5NextVisionModel - forward

[[autodoc]] Glm5NextModel - forward

Glm5NextForConditionalGeneration

[[autodoc]] Glm5NextForConditionalGeneration - forward

Glm5NextProcessor

[[autodoc]] Glm5NextProcessor

Glm5NextImageProcessor

[[autodoc]] Glm5NextImageProcessor

Glm5NextImageProcessorPil

[[autodoc]] Glm5NextImageProcessorPil

Glm5NextVideoProcessor

[[autodoc]] Glm5NextVideoProcessor