Instructions to use Blackfrost-Research/GLM-5.3-F.U-AnthraClaud-Edition-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Blackfrost-Research/GLM-5.3-F.U-AnthraClaud-Edition-BF16 with Transformers:

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

pipe = pipeline("text-generation", model="Blackfrost-Research/GLM-5.3-F.U-AnthraClaud-Edition-BF16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Blackfrost-Research/GLM-5.3-F.U-AnthraClaud-Edition-BF16")
model = AutoModelForCausalLM.from_pretrained("Blackfrost-Research/GLM-5.3-F.U-AnthraClaud-Edition-BF16", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Blackfrost-Research/GLM-5.3-F.U-AnthraClaud-Edition-BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Blackfrost-Research/GLM-5.3-F.U-AnthraClaud-Edition-BF16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Blackfrost-Research/GLM-5.3-F.U-AnthraClaud-Edition-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Blackfrost-Research/GLM-5.3-F.U-AnthraClaud-Edition-BF16

SGLang

How to use Blackfrost-Research/GLM-5.3-F.U-AnthraClaud-Edition-BF16 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 "Blackfrost-Research/GLM-5.3-F.U-AnthraClaud-Edition-BF16" \
    --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": "Blackfrost-Research/GLM-5.3-F.U-AnthraClaud-Edition-BF16",
		"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 "Blackfrost-Research/GLM-5.3-F.U-AnthraClaud-Edition-BF16" \
        --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": "Blackfrost-Research/GLM-5.3-F.U-AnthraClaud-Edition-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Blackfrost-Research/GLM-5.3-F.U-AnthraClaud-Edition-BF16 with Docker Model Runner:
```
docker model run hf.co/Blackfrost-Research/GLM-5.3-F.U-AnthraClaud-Edition-BF16
```

Blackfrost-AI commited on Aug 28

Commit

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1 Parent(s): 8e4a73a

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+---
+license: other
+license_name: blackfrost-commercial
+base_model:
+  - zai-org/GLM-5.3-BF16
+base_model_relation: finetune
+pipeline_tag: text-generation
+library_name: transformers
+language:
+  - en
+  - zh
+tags:
+  - glm
+  - glm-5.3
+  - glm_moe_dsa
+  - mixture-of-experts
+  - moe
+  - bf16
+  - de-risked
+  - red-teaming
+  - security-research
+  - enterprise
+---
+<div align="center">
+![Blackfrost](https://cdn-uploads.huggingface.co/production/uploads/69a27f2d114e4ac9de4dafc7/xDTdhLFXmKlZOazFcvJ5S.jpeg)
+# GLM-5.3-DERISKED-BF16
+### Enterprise de-risked GLM-5.3 · 753B Mixture-of-Experts · BF16 master
+**Built by [Blackfrost](https://x.com/Blackfrost_AI) · Las Vegas, Nevada**
+![BF16](https://img.shields.io/badge/Format-BF16-047857?style=for-the-badge)
+![Private](https://img.shields.io/badge/Access-Private-1f2937?style=for-the-badge)
+![Commercial](https://img.shields.io/badge/License-Commercial-b45309?style=for-the-badge)
+![Price](https://img.shields.io/badge/Licensed_access-%24599-b91c1c?style=for-the-badge)
+</div>
+> **Repository status:** model card available; weight upload pending.
+## Licensed access — $599 USD
+This checkpoint is offered under a Blackfrost commercial license for **$599 USD**.
+Purchase, access, and enterprise licensing information is available from the
+[Blackfrost model catalog](https://redpillreader.com/models).
+Access is intended for security firms, authorized red teams, AI-safety labs,
+guardrail and detection teams, and enterprise research groups operating in
+controlled environments.
+## Overview
+**GLM-5.3-DERISKED-BF16** is Blackfrost's full-precision, weight-level de-risked
+build of the official [`zai-org/GLM-5.3-BF16`](https://huggingface.co/zai-org/GLM-5.3-BF16)
+checkpoint. It is the master artifact for the Blackfrost GLM-5.3 release family.
+The intended behavior is intrinsic to the checkpoint. It does not depend on a
+system prompt, adapter, or decoding-time filter. Production methods are
+proprietary and are not disclosed.
+## Specifications
+| | |
+|---|---|
+| **Architecture** | `GlmMoeDsaForCausalLM` |
+| **Parameters** | Approximately 753B stored parameters · Mixture-of-Experts |
+| **Precision** | BF16 mixed with native FP32 metadata tensors |
+| **Artifact size** | 1,506,659,919,872 indexed tensor bytes (~1.37 TiB) |
+| **Shards** | 158 model shards + 3 MTP shards |
+| **Layers** | 78 main layers + 1 multi-token-prediction layer |
+| **Experts** | 256 routed experts · top-8 active per token · shared expert |
+| **Hidden size** | 6,144 |
+| **Attention heads** | 64 |
+| **Context ceiling** | 1,048,576 positions |
+| **Vocabulary** | 154,880 |
+| **Languages** | English and Chinese |
+Tokenizer, generation configuration, and the native GLM chat template are part
+of the release artifact. Deploy with a runtime that supports GLM-5.3's native
+reasoning and tool-call tokens.
+## Lineage
+| | |
+|---|---|
+| **Upstream** | [`zai-org/GLM-5.3-BF16`](https://huggingface.co/zai-org/GLM-5.3-BF16) |
+| **Blackfrost change** | Proprietary weight-level de-risking |
+| **Not applied** | Additional SFT · DPO · RLHF · expert pruning · quantization |
+| **Format** | Hugging Face Safetensors · BF16 |
+```text
+zai-org/GLM-5.3-BF16
+  └─ GLM-5.3-DERISKED-BF16              ← this repository
+       └─ GLM-5.3-DERISKED-NVFP4        deployment derivative
+```
+## Validation status
+The BF16 artifact passed structural and index-level integrity checks across all
+59,585 tensors. The multi-token-prediction layer is present in the release.
+No refusal-rate or capability score is claimed in this card before completion
+of the final judged evaluation. Results will be added only after qualification.
+## Deployment notes
+- Plan capacity from the indexed 1.37 TiB weight footprint and reserve
+  additional HBM for KV cache, activations, runtime workspaces, and CUDA graphs.
+- Use a current serving stack with native `GlmMoeDsaForCausalLM` support.
+- The architectural context ceiling is not a guaranteed per-request allocation;
+  set the production context budget from available KV memory.
+- Treat multi-token prediction as an optional optimization and qualify it
+  independently in the selected runtime.
+- GLM-5.3's recommended sampling baseline is temperature `1.0` and top-p `0.95`.
+## Access and licensing
+The Blackfrost derivative is distributed under a separate commercial agreement.
+Recipients remain responsible for compliance with applicable upstream terms,
+export controls, local law, and their own authorization boundaries.
+The upstream checkpoint remains subject to the
+[`GLM-5.3 License`](https://huggingface.co/zai-org/GLM-5.3-BF16/blob/main/LICENSE).
+**[Purchase or request licensed access — $599 USD](https://redpillreader.com/models)**
+For air-gapped deployment, enterprise support, evaluation services, or a custom
+build, contact [@Blackfrost_AI](https://x.com/Blackfrost_AI).
+## Responsible use
+This checkpoint is intended for authorized security testing, AI-safety and
+alignment research, model evaluation, and defensive engineering. It is not a
+safety-stock model. Operators must provide independent access control, logging,
+monitoring, and policy enforcement, and must treat model output as untrusted.
+The model is provided **as is**, without warranty. Evaluation results describe
+specific test conditions and are not safety certifications or guarantees of
+behavior in another deployment.
+---
+<div align="center">
+**GLM-5.3-DERISKED-BF16** · © 2026 Blackfrost Softwares Corp.
+[@Blackfrost_AI](https://x.com/Blackfrost_AI)
+</div>