Instructions to use z-lab/Qwen3.5-397B-A17B-DFlash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use z-lab/Qwen3.5-397B-A17B-DFlash with Transformers:

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

pipe = pipeline("text-generation", model="z-lab/Qwen3.5-397B-A17B-DFlash", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("z-lab/Qwen3.5-397B-A17B-DFlash", trust_remote_code=True)
model = AutoModel.from_pretrained("z-lab/Qwen3.5-397B-A17B-DFlash", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use z-lab/Qwen3.5-397B-A17B-DFlash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "z-lab/Qwen3.5-397B-A17B-DFlash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "z-lab/Qwen3.5-397B-A17B-DFlash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/z-lab/Qwen3.5-397B-A17B-DFlash

SGLang

How to use z-lab/Qwen3.5-397B-A17B-DFlash 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 "z-lab/Qwen3.5-397B-A17B-DFlash" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "z-lab/Qwen3.5-397B-A17B-DFlash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "z-lab/Qwen3.5-397B-A17B-DFlash" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "z-lab/Qwen3.5-397B-A17B-DFlash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use z-lab/Qwen3.5-397B-A17B-DFlash with Docker Model Runner:
```
docker model run hf.co/z-lab/Qwen3.5-397B-A17B-DFlash
```

jianchen0311 commited on Jun 15

Commit

2c71e1b

verified ·

1 Parent(s): 9cf52e1

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@@ -37,7 +37,7 @@ DFlash uses a lightweight block diffusion draft model to propose multiple tokens
 ### Installation
-DFlash serving requires a recent SGLang build with DFlash v2 support. We recommend installing SGLang from a commit at or after `ec36dde58083aca8f26c3740332498a11a06debf`, which includes DFlash v2, DFlash sliding-window draft attention, and the required FlashInfer integration.
 ```bash
 pip install -U \

 ### Installation
+DFlash serving requires a recent SGLang build. We recommend installing SGLang from a commit at or after `ec36dde58083aca8f26c3740332498a11a06debf`, which includes DFlash v2 overlap scheduling, DFlash sliding-window draft attention, and the required FlashInfer integration.
 ```bash
 pip install -U \