GLM-5.2-ABLITERATED-REAP-NU176-NVFP4

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<h1>GLM-5.2-ABLITERATED-REAP-NU176-NVFP4</h1> <h3>Non-uniform expert prune · NVFP4 · runs on FOUR Blackwell cards · verified 0-refusal</h3> <p><strong>Built by <a href="https://x.com/Blackfrost_AI">Blackfrost</a> · Las Vegas, NV</strong></p> <p> <img src="https://img.shields.io/badge/~52.7_tok/s-047857?style=for-the-badge" /> <img src="https://img.shields.io/badge/Abliterated-7f1d1d?style=for-the-badge" /> <img src="https://img.shields.io/badge/4×_RTX_6000-b45309?style=for-the-badge" /> <img src="https://img.shields.io/badge/Community_re--host-1f2937?style=for-the-badge" /> </p> </div>

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⚠️ UNCENSORED

Refusal directions have been ablated — this model complies with harmful requests by design. That is what makes it a controlled test artifact, and dangerous if misused. Read the Disclaimer.

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Why this model exists

Three transformations in one deployable checkpoint: refusal ablation, non-uniform MoE expert pruning, and NVFP4 quantization. The result runs on four Blackwell cards instead of eight — and stays coherent and fast after every transformation.

That is the whole point of the release: a de-risked GLM-5.2 small enough and fast enough for a 4-card node.

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Specifications

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Lineage

zai-org/GLM-5.2                                    base foundation model, ZhipuAI
  └─ huihui-ai/Huihui-GLM-5.2-abliterated-GGUF     refusal directions ablated (Q3_K GGUF)
       └─ Blackfrost-AI/GLM-5.2-ABLITERATED-BF16   up-cast to BF16 safetensors
            └─ Blackfrost-AI/GLM-5.2-ABLITERATED-NVFP4
                 └─ Blackfrost-AI/GLM-5.2-ABLITERATED-REAP-NU176-NVFP4   ← this repo

Full credit to ZhipuAI for GLM-5.2 and to huihui-ai for the abliteration. No additional fine-tuning or abliteration was performed by Blackfrost.

Blackfrost also publishes an in-house de-risked GLM-5.2 line derived directly from zai-org/GLM-5.2 source rather than from a third-party abliteration — see BlackfrostAI/GLM-5.2-DERISKED-BF16. This repository is not that.

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Measured behaviour

Throughput — single-stream, 4× RTX PRO 6000 (SM120)

~1.55× faster on the same 4 cards.

Refusal — evaluated on the live serve

Every substring-flagged case was read by hand to confirm.

True refusal rate: 0 / 400 harmful prompts.

Capability retention — not measured

Expert pruning is a capability trade. Refusal and coherence were measured; the capability cost of removing ~80 of 256 routed experts was not. Harness, conditions and retention figures will be stated here — including any benchmark where retention is poor.

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Deployment notes

• Hardware. ~302 GB. 4× RTX PRO 6000 Blackwell (SM120, 96 GB). NVFP4 kernels are sm_120-class or newer.
• Parsers. --reasoning-parser glm45, --tool-call-parser glm47. enable_thinking=false for a clean refusal test.
• Integrity. Verify shard count (65) and byte totals before attributing a load failure to the weights.

Intended for security research, red-teaming, and evaluation by professionals operating under proper authorisation.

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Disclaimer

Refusal behaviour in this checkpoint has been removed. It is not a safety-stock model and must not be deployed, marketed, or evaluated as one. It will comply with requests a consumer model would decline.

No warranty of any kind. Provided "as is", without warranty express or implied, including fitness for a particular purpose. Nothing here guarantees that any given input will be accepted or refused, that any capability is retained, or that any category of output is unreachable.

Measurements describe what was measured under the stated harness and conditions. They are not safety proofs and do not generalise to multimodal, tool-use, long-context or multi-turn adversarial settings.

Modification by a recipient voids this characterization. Blackfrost's obligations attach at the point of release. Any further ablation, fine-tuning, merging, quantization or alteration produces an artifact Blackfrost has not evaluated and does not stand behind — responsibility transfers entirely to whoever produced it.

Operator-owned policy. Open weights mean the operator sets and enforces policy. You are responsible for adding your own safety filtering, human review, and access controls.

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Access & licensing

• Base licence: inherited from GLM-5.2 (ZhipuAI / Z.ai) — review and comply before any use or redistribution.
• Deploy kit: the tuned serving stack that delivers the Blackwell performance above is provided to licensees, not published here.
• Free public download: The-freezer/GLM-5.2-ABLITERATED-REAP-NU176-NVFP4. No payment, sign-in, or access request is required to download the weights. Existing repository and upstream license terms still apply.

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Contact Blackfrost

<div align="center"> <h3><a href="https://x.com/Blackfrost_AI">@Blackfrost_AI</a> on X</h3> <p><strong>DMs are open.</strong> Fastest route to a human.</p> <p><strong>Blackfrost</strong> · Las Vegas, Nevada<br> Frontier model engineering</p> </div>

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<div align="center"> <p><strong>GLM-5.2-ABLITERATED-REAP-NU176-NVFP4</strong> · © 2026 Blackfrost Softwares Corp.<br> <a href="https://x.com/Blackfrost_AI">@Blackfrost_AI</a></p> </div>