Instructions to use Blackfrost-Research/GLM-5.3-DERISKED-NVFP4-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Blackfrost-Research/GLM-5.3-DERISKED-NVFP4-V2 with Transformers:

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

pipe = pipeline("text-generation", model="Blackfrost-Research/GLM-5.3-DERISKED-NVFP4-V2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Blackfrost-Research/GLM-5.3-DERISKED-NVFP4-V2")
model = AutoModelForCausalLM.from_pretrained("Blackfrost-Research/GLM-5.3-DERISKED-NVFP4-V2", 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 Blackfrost-Research/GLM-5.3-DERISKED-NVFP4-V2 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-DERISKED-NVFP4-V2"
# 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-DERISKED-NVFP4-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Blackfrost-Research/GLM-5.3-DERISKED-NVFP4-V2

SGLang

How to use Blackfrost-Research/GLM-5.3-DERISKED-NVFP4-V2 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-DERISKED-NVFP4-V2" \
    --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-DERISKED-NVFP4-V2",
		"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-DERISKED-NVFP4-V2" \
        --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-DERISKED-NVFP4-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Blackfrost-Research/GLM-5.3-DERISKED-NVFP4-V2 with Docker Model Runner:
```
docker model run hf.co/Blackfrost-Research/GLM-5.3-DERISKED-NVFP4-V2
```

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GLM-5.3-DERISKED-NVFP4-V2

Deployment-focused ModelOpt NVFP4 derivative of the full GLM-5.3 Mixture-of-Experts model for NVIDIA Blackwell. It retains the complete expert topology and native tokenizer, chat template, reasoning, tool-use, long-context, and MTP metadata.

This is a public, manually gated customer release. Blackfrost's weight-level production process is proprietary and is not distributed in this repository.

Specifications


Architecture	`GlmMoeDsaForCausalLM`
Precision	ModelOpt routed-expert W4A4 NVFP4 mixed precision
Layers	78 main layers plus the native MTP layer
Experts	256 routed experts, top-8 active, plus shared expert
Hidden size	6,144
Context ceiling	1,048,576 positions
Source	`zai-org/GLM-5.3-BF16`
Target hardware	NVIDIA Blackwell

DFlash2 deployment

The included deployment kit enables SGLang DFLASH speculative decoding with the separate revision-pinned incoai/GLM-5.3-DFlash2 companion. Its weights are not redistributed here. Review the companion's CC BY-NC-ND 4.0 terms before use.

See DEPLOYMENT/README.md and DEPLOYMENT/LAUNCH.sh.

Qualify the model for your workload before production. Operators remain responsible for access control, monitoring, legal compliance, and treating all model output as untrusted. The model is provided as is, without warranty.

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Safetensors

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Tensor type

BF16

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