license: mit
base_model:
- Blackfrost-AI/GLM-5.2-ABLITERATED-NVFP4
base_model_relation: quantized
pipeline_tag: text-generation
library_name: transformers
language: [en, zh]
tags: [glm, glm-5.2, glm_moe_dsa, mixture-of-experts, moe, abliterated, uncensored, nvfp4, fp4, reap, pruned, non-uniform, mtp, speculative-decoding, security-research, red-teaming, gated]
GLM-5.2-ABLITERATED-REAP-NU176-NVFP4
Non-uniform expert prune · NVFP4 · runs on FOUR Blackwell cards · verified 0-refusal
Built by Blackfrost · Las Vegas, NV
⚠️ 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.
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.
Specifications
| Architecture | GLM-5.2 (glm_moe_dsa) — MLA + sparse attention + MTP head |
| Prune | REAP non-uniform expert pruning — ~176 of 256 routed experts |
| Quantization | NVFP4 (4-bit) |
| Footprint | ~302 GB on disk · 65 safetensors shards |
| Hardware | 4× RTX PRO 6000 Blackwell (SM120, 96 GB) — half the usual card count |
| Acceleration | Multi-token-prediction (MTP) speculative decoding |
| Context | up to 1,048,576 |
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
| Base foundation | zai-org/GLM-5.2 — ZhipuAI |
| Abliteration | huihui-ai — refusal directions ablated |
| Blackfrost applied | Format up-cast to BF16 → NVFP4 quantization → non-uniform REAP prune + MTP acceleration |
| Not applied | Additional abliteration · SFT · DPO · RLHF |
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-5.2source rather than from a third-party abliteration — seeBlackfrostAI/GLM-5.2-DERISKED-BF16.
This repository is not that.
Measured behaviour
Throughput — single-stream, 4× RTX PRO 6000 (SM120)
| Decode mode | Throughput |
|---|---|
| MTP acceleration (shipped) | ~52.7 tok/s |
| Standard decode | ~34.0 tok/s |
~1.55× faster on the same 4 cards.
Refusal — evaluated on the live serve
Every substring-flagged case was read by hand to confirm.
| Dataset | Prompts | True refusals |
|---|---|---|
| AdvBench (harmful) | 200 | 0 |
| StrongREJECT (harmful) | 200 | 0 |
| XSTest — safe (over-refusal) | 100 | 0 (0.0%) |
| Coherence (all sets) | 600 | 0 incoherent |
True refusal rate: 0 / 400 harmful prompts.
Capability retention — not measured
| Benchmark | Unpruned NVFP4 | This (REAP-NU176) | Retention |
|---|---|---|---|
| pending | — | — | —% |
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.
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=falsefor 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.
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.
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.
- Commercial licensing & access: redpillreader.com/models — card or Bitcoin (−10%). Purchase grants your Hugging Face account access to the gated repo automatically.
Contact Blackfrost
@Blackfrost_AI on X
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GLM-5.2-ABLITERATED-REAP-NU176-NVFP4 · © 2026 Blackfrost Softwares Corp.
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