license: mit
base_model:

• Blackfrost-AI/GLM-5.2-ABLITERATED-BF16
  base_model_relation: quantized
  pipeline_tag: text-generation
  library_name: vllm
  language: [en, zh]
  tags: [glm, glm-5.2, glm_moe_dsa, mixture-of-experts, moe, abliterated, uncensored, nvfp4, fp4, mtp, speculative-decoding, vllm, security-research, red-teaming]

⚠️ UNCENSORED

Refusal directions in the residual stream have been ablated, so the model does not decline
requests on content-policy grounds. Read the Disclaimer before downloading.

Why this model exists

The Blackfrost-verified build of the ABLITERATED family: GLM-5.2 quantized to NVFP4 (4-bit)
and accelerated with multi-token-prediction speculative decoding, so a ~753B MoE runs fast on
a single 8× Blackwell node — with a verified 0-refusal profile.

Specifications

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   ← 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.

Measured behaviour

Throughput — single-stream, 8× RTX PRO 6000 (SM120)

~2.1× faster on the same 8 cards.

Refusal — evaluated on the live serve

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

True refusal rate: 0 / 300 harmful prompts. Quantization neither adds nor restores safety
behaviour.

Method caveat. Validated on a 450-prompt evaluation (substring pre-filter + manual review), not
an exhaustive benchmark, and on one serving configuration.

Limitations

• 4-bit quantized — expect quality below the full-precision base, especially on long, hard reasoning.
• Tuned for SM120 (Blackwell); other architectures need a different serving backend.
• Refusal behaviour validated on 450 prompts, not exhaustively.

Deployment notes

• Hardware. ~420 GB. 8× RTX PRO 6000 Blackwell (SM120), TP=8, vLLM.
• Parsers. --reasoning-parser glm45, --tool-call-parser glm47. For a clean refusal test set enable_thinking=false.
• Integrity. Verify shard count and byte totals before attributing a load failure to the weights.

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