Instructions to use The-freezer/GLM-5.2-DERISKED-REAP-NU176-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use The-freezer/GLM-5.2-DERISKED-REAP-NU176-NVFP4 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="The-freezer/GLM-5.2-DERISKED-REAP-NU176-NVFP4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("The-freezer/GLM-5.2-DERISKED-REAP-NU176-NVFP4")
model = AutoModelForCausalLM.from_pretrained("The-freezer/GLM-5.2-DERISKED-REAP-NU176-NVFP4", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use The-freezer/GLM-5.2-DERISKED-REAP-NU176-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "The-freezer/GLM-5.2-DERISKED-REAP-NU176-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "The-freezer/GLM-5.2-DERISKED-REAP-NU176-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/The-freezer/GLM-5.2-DERISKED-REAP-NU176-NVFP4

SGLang

How to use The-freezer/GLM-5.2-DERISKED-REAP-NU176-NVFP4 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "The-freezer/GLM-5.2-DERISKED-REAP-NU176-NVFP4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "The-freezer/GLM-5.2-DERISKED-REAP-NU176-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "The-freezer/GLM-5.2-DERISKED-REAP-NU176-NVFP4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "The-freezer/GLM-5.2-DERISKED-REAP-NU176-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use The-freezer/GLM-5.2-DERISKED-REAP-NU176-NVFP4 with Docker Model Runner:
```
docker model run hf.co/The-freezer/GLM-5.2-DERISKED-REAP-NU176-NVFP4
```

Request commercial access

GLM-5.2-DERISKED-REAP-NU176-NVFP4 is released under a Blackfrost commercial licence and is reviewed manually. Access is granted to security firms, red teams, AI-safety labs, and enterprise research groups. Tell us who you are and what you are evaluating; we review every request individually. Access is granted automatically on purchase at redpillreader.com/models; requests without a purchase are not approved.

GLM-5.2-DERISKED-REAP-NU176-NVFP4

Non-uniform expert prune of de-risked GLM-5.2 · NVFP4 · smallest build in the line

Built by Blackfrost · Las Vegas, NV

🔴 Licensed access — $99

Click the red button to purchase.
Enter your Hugging Face username at checkout; access to this gated repository is granted automatically after payment.

Issues — open a Community discussion.

Why this model exists

The full-precision GLM-5.2 de-risked master is 1.35 TiB and needs an 8×B200-class node. The NVFP4 build brings that to ~451 GB. This build goes further — a non-uniform REAP prune on top of NVFP4, landing at **323 GB**, roughly 28% smaller than its NVFP4 parent and about a quarter of the BF16 master.

The point is footprint. If the NVFP4 build is close but doesn't fit your envelope, this is the one to ask about.

Specifications


Architecture	`GlmMoeDsaForCausalLM` — GLM MoE with MLA + DeepSeek-style Sparse Attention
Base	`zai-org/GLM-5.2` — official
Parent	`GLM-5.2-DERISKED-NVFP4`
Transform	Refusal-direction de-risk (in parent) → NVFP4 → non-uniform REAP prune
Keep set	NU176 — non-uniform, per-layer budgets rather than a flat keep
Precision	NVFP4 (4-bit floating-point, microscaled, two-level block scaling)
On-disk	~323 GB · 65 safetensors shards
Languages	English, Chinese
Serving	vLLM
Status	EXPERIMENTAL — unbenchmarked

What "NU176" means

REAP is router-weighted expert activation pruning: experts are scored by their contribution across a calibration set, and the least-salient are dropped.

NU denotes a non-uniform keep — the per-layer budget varies with measured routing load rather than removing the same count from every MoE layer. Layers that genuinely spread work across many experts keep more of them; layers whose routing concentrates keep fewer. That buys a better capability-per-byte trade than a flat prune at the same total size, at the cost of less predictable per-layer memory.

Contrast with the Kimi K3 line, where KIMI-K3-DERISKED-CODER-REAP-320-MXFP4 uses a uniform 320-of-896 keep specifically so memory and routing stay predictable layer to layer. Both approaches are available; which is right depends on whether you are optimising for quality at a size target or for predictability.

Lineage

zai-org/GLM-5.2                          official open-weight, 256 routed experts
  └─ GLM-5.2-DERISKED-BF16               in-house refusal-direction de-risk, from source
       └─ GLM-5.2-DERISKED-NVFP4         NVFP4 quantization
            └─ GLM-5.2-DERISKED-REAP-NU176-NVFP4     ← this repo, non-uniform REAP prune


Applied	Weight-level de-risk (in BF16 master) · NVFP4 quantization · non-uniform REAP prune
Not applied	SFT · DPO · RLHF
Format	HF safetensors · NVFP4

On refusal behaviour: this checkpoint inherits the parent's deliberately reduced refusal surface. It is a Blackfrost de-risked model. Do not evaluate or rate-limit it as if it were a safety-stock derivative of upstream GLM-5.2.

Measured behaviour

Pending

Benchmark	GLM-5.2-DERISKED-NVFP4	This (REAP-NU176)	Retention
pending	—	—	—%

Expert pruning is a capability trade, and a card that ships a pruned build without publishing what it cost is asking the reader to take the trade on faith. Harness, conditions and retention figures will be stated here — including any benchmark where retention is poor.

Two things specifically unmeasured on this checkpoint:

Capability retention versus the NVFP4 parent — the prune's actual price.
Whether the de-risking survives the prune. The parent's refusal figures were measured before pruning. Pruning removes experts; whether the refusal-direction intervention persists unchanged through that is an open question on this artifact, not an established property.

Until both are measured, the parent's numbers should not be quoted for this build.

Deployment notes

Hardware. ~323 GB of weights. Fits comfortably on a single 8×96 GB node with room for KV, and is the only build in this line that will.
Runtime. vLLM. NVFP4 kernels require sm_120-class hardware (RTX PRO 6000 Blackwell) or newer; sm_100 (B200) does not carry NVFP4 MoE kernels — use the MXFP4 or BF16 builds there.
Thinking. GLM-5.2 reasoning output may land in the visible stream depending on chat-template handling. Budget max_tokens generously.
Integrity. Verify shard count (65) and byte totals after download before attributing a load failure to the weights.

Other builds

Blackfrost publishes the GLM-5.2 de-risked line in several shapes. Access to full-precision and custom builds is granted on purchase under a separate licence agreement — those repositories are gated, and approval follows a completed agreement. The gate is the transaction.

Build	What it is
`GLM-5.2-DERISKED-BF16`	Full-precision BF16 master — every other build in this line derives from it
`GLM-5.2-DERISKED-NVFP4`	4-bit NVFP4, single-node serving on sm_120-class hardware
This repo	Non-uniform REAP prune on top of NVFP4 — smallest footprint in the line
Custom	Expert budgets, uniform or non-uniform keep sets, or de-risking calibrated against your own threat model

Purchase link coming soon. Until then, @Blackfrost_AI DMs are the fastest route to a human.

Disclaimer

Refusal behaviour in this checkpoint has been deliberately modified at the weight level. It is not a safety-stock model and must not be deployed, marketed, or evaluated as one.

Capability has been deliberately reduced. Experts were removed by non-uniform prune. The size of that reduction has not been measured on this checkpoint. Do not assume parent-equivalent quality.

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.

No measurements are published for this build. Figures on the parent card describe the parent, under its own stated harness and caveats, and do not transfer here.

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 by a recipient produces an artifact Blackfrost has not evaluated and does not stand behind — responsibility for that artifact transfers entirely to whoever produced it.

Operator-owned policy. Open weights mean the operator sets and enforces policy. Deploy only in controlled environments with access control, independent logging and review.

Access & licensing

Access is granted automatically on purchase — you do not wait on a manual review.

➜ Purchase access to this model — $99 — enter your Hugging Face username at checkout, and your account is granted access to this repository within moments of payment.

Base licence: MIT, inherited from zai-org/GLM-5.2 — upstream terms travel with this derivative.
Blackfrost terms: commercial licence, layered on top of the upstream licence.
Redistribution: do not redistribute weights outside your grant.
Evaluation recommendation: should not be evaluated by processes that assume refusal behaviour equivalent to upstream GLM-5.2.

Contact Blackfrost

@Blackfrost_AI on X

DMs are open. Fastest route to a human.

Blackfrost · Las Vegas, Nevada
Frontier model engineering

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Model tree for The-freezer/GLM-5.2-DERISKED-REAP-NU176-NVFP4

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