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
```

GLM-5.3-F.U-AnthraClaud-Edition-BF16 / README.md

Blackfrost-AI

Remove paid-access pricing from model card; retain upstream GLM license

45e8d60 verified 5 days ago

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	---
	license: other
	license_name: glm-5.3
	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-SUPER SCARY ACORDING TO ANTHROPIC-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)
	![Weights](https://img.shields.io/badge/Status-WEIGHTS_LIVE-047857?style=for-the-badge)
	FREE THANKS TO ANTHROPIC LYING ON OUR MODELS
	</div>

	> Weights published: the verified BF16 checkpoint is available as 158
	> model shards plus three preserved MTP shards, with its configuration,
	> tokenizer, generation settings, and native GLM chat template.

	## Intended audience

	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`.

	## License and support

	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).

	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>