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Blackfrost-AI
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Model Details
Model Provider
Blackfrost-AI
Model Tree
Blackfrost-AI/GLM-5.2-ABLITERATED-NVFP4
Input Modalities
Output Modalities
Supported Functionality
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.
| 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 |
markdown
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.
| Decode mode | Throughput |
|---|---|
| MTP acceleration (shipped) | ~52.7 tok/s |
| Standard decode | ~34.0 tok/s |
~1.55× faster on the same 4 cards.
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.
| 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.
--reasoning-parser glm45, --tool-call-parser glm47. enable_thinking=false for a clean refusal test.Intended for security research, red-teaming, and evaluation by professionals operating under proper authorisation.
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.