Intended audience
Access is intended for security firms, authorized red teams, AI-safety labs,
guardrail and detection teams, and enterprise research groups operating in
controlled environments.
Overview
GLM-5.3-DERISKED-BF16 is Blackfrost's full-precision, weight-level de-risked
build of the official zai-org/GLM-5.3-BF16
checkpoint. It is the master artifact for the Blackfrost GLM-5.3 release family.
The intended behavior is intrinsic to the checkpoint. It does not depend on a
system prompt, adapter, or decoding-time filter. Production methods are
proprietary and are not disclosed.
Specifications
Table | |
|---|
| Architecture | GlmMoeDsaForCausalLM |
| Parameters | Approximately 753B stored parameters · Mixture-of-Experts |
| Precision | BF16 mixed with native FP32 metadata tensors |
| Artifact size | 1,506,659,919,872 indexed tensor bytes (~1.37 TiB) |
| Shards | 158 model shards + 3 MTP shards |
| Layers | 78 main layers + 1 multi-token-prediction layer |
| Experts | 256 routed experts · top-8 active per token · shared expert |
| Hidden size | 6,144 |
| Attention heads | 64 |
| Context ceiling | 1,048,576 positions |
| Vocabulary | 154,880 |
| Languages | English and Chinese |
Tokenizer, generation configuration, and the native GLM chat template are part
of the release artifact. Deploy with a runtime that supports GLM-5.3's native
reasoning and tool-call tokens.
Lineage
Table | |
|---|
| Upstream | zai-org/GLM-5.3-BF16 |
| Blackfrost change | Proprietary weight-level de-risking |
| Not applied | Additional SFT · DPO · RLHF · expert pruning · quantization |
| Format | Hugging Face Safetensors · BF16 |
zai-org/GLM-5.3-BF16
└─ GLM-5.3-DERISKED-BF16 ← this repository
└─ GLM-5.3-DERISKED-NVFP4 deployment derivative
Validation status
The BF16 artifact passed structural and index-level integrity checks across all
59,585 tensors. The multi-token-prediction layer is present in the release.
No refusal-rate or capability score is claimed in this card before completion
of the final judged evaluation. Results will be added only after qualification.
Deployment notes
- Plan capacity from the indexed 1.37 TiB weight footprint and reserve
additional HBM for KV cache, activations, runtime workspaces, and CUDA graphs.
- Use a current serving stack with native
GlmMoeDsaForCausalLM support.
- The architectural context ceiling is not a guaranteed per-request allocation;
set the production context budget from available KV memory.
- Treat multi-token prediction as an optional optimization and qualify it
independently in the selected runtime.
- GLM-5.3's recommended sampling baseline is temperature
1.0 and top-p 0.95.
License and support
Recipients remain responsible for compliance with applicable upstream terms,
export controls, local law, and their own authorization boundaries.
The upstream checkpoint remains subject to the
GLM-5.3 License.
For air-gapped deployment, enterprise support, evaluation services, or a custom
build, contact @Blackfrost_AI.
Responsible use
This checkpoint is intended for authorized security testing, AI-safety and
alignment research, model evaluation, and defensive engineering. It is not a
safety-stock model. Operators must provide independent access control, logging,
monitoring, and policy enforcement, and must treat model output as untrusted.
The model is provided as is, without warranty. Evaluation results describe
specific test conditions and are not safety certifications or guarantees of
behavior in another deployment.
GLM-5.3-DERISKED-BF16 · © 2026 Blackfrost Softwares Corp.
@Blackfrost_AI