Models/Blackfrost/BF-GLM52-BF16-001

GLM-5.2 — De-Risked BF16 Master

Public repositoryFree · Ungated

Z.AI · GLM-5.2 · BF16 · free public download

README.mdBF-GLM52-BF16-001 · free public access
text-generationbf16mixture-of-expertspublicfree

GLM-5.2 — De-Risked BF16 Master

The full-precision GLM-5.2 reference master for evaluation and conversion.

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Free and ungated

Download the public weights directly from Hugging Face. No payment, account, or access request is required. Public GLM-5.2 and GLM-5.3 downloads remain available independently of the paid store.

License terms

The MIT license requires the Zhipu AI copyright and permission notice to accompany every copy or substantial portion of the upstream material. The repository’s existing license terms and notices still apply. Read the upstream license ↗

Release identity and selection guidance

Edition roleReference master
Weight package~1.35 TiB · BF16 · 158 safetensors shards
Intended fitReference evaluation and downstream conversion where the full-precision GLM-5.2 release is required.
Choose this edition whenChoose this edition when retaining the BF16 master matters more than the smaller footprint of the NVFP4 editions.
Deployment boundaryNo serving runtime or hardware target is documented for this edition; validate runtime support and capacity before deployment.
Verified repository revision9df496ae4b30 ↗

Technical specifications

Base modelzai-org/GLM-5.2
ArchitectureGlmMoeDsaForCausalLM
Upstream licenseMIT ↗
Release identifierBF-GLM52-BF16-001
Parameters~753B total · Mixture-of-Experts
PrecisionBF16 · full-precision safetensors
Footprint~1.35 TiB · 158 shards
Context1,048,576 positions

What is available

The model weights and supporting files published in the named repository, including configuration, tokenizer files when present, and documentation. Review the model card for its current file inventory and usage terms.

Compute, hosting, installation, custom engineering, and ongoing support are separately scoped. Explore hosting or custom services.

Deployment notice

Validate model behavior, runtime compatibility, infrastructure capacity, and operational controls against your workload. Listed configurations are references, not performance guarantees.