Instructions to use Blackfrost-Research/GLM-5.3-Derisked-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Blackfrost-Research/GLM-5.3-Derisked-FP8 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-FP8")
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-FP8")
model = AutoModelForCausalLM.from_pretrained("Blackfrost-Research/GLM-5.3-Derisked-FP8", 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-FP8 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-FP8"
# 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-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Blackfrost-Research/GLM-5.3-Derisked-FP8

SGLang

How to use Blackfrost-Research/GLM-5.3-Derisked-FP8 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-FP8" \
    --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-FP8",
		"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-FP8" \
        --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-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Blackfrost-Research/GLM-5.3-Derisked-FP8 with Docker Model Runner:
```
docker model run hf.co/Blackfrost-Research/GLM-5.3-Derisked-FP8
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

GLM-5.3-Derisked-FP8

Deployment-ready FP8 derivative of the full GLM-5.3 Mixture-of-Experts model. The model retains the complete expert topology and native GLM tokenizer, chat template, reasoning, tool-use, long-context, and MTP metadata.

This is a public, manually gated customer release. Weight access is reviewed by Blackfrost Research and distributed under a separate commercial agreement.

Specifications


Architecture	`GlmMoeDsaForCausalLM`
Precision	Dynamic block-128 E4M3 FP8
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`

Blackfrost's weight-level production process is proprietary and is not included in this repository. No adapter or client-side system prompt is required for the checkpoint's intended behavior.

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.

Use and limitations

Qualify capability, safety, long-context behavior, tool use, and speculative acceptance for your workload before production. Operators remain responsible for access control, monitoring, applicable-law compliance, and treating model output as untrusted. The model is provided as is, without warranty.

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Tensor type

BF16

F8_E4M3

F32

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zai-org/GLM-5.3-BF16

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