Instructions to use z-lab/Qwen3.5-9B-DFlash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use z-lab/Qwen3.5-9B-DFlash with Transformers:

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

pipe = pipeline("text-generation", model="z-lab/Qwen3.5-9B-DFlash", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("z-lab/Qwen3.5-9B-DFlash", trust_remote_code=True)
model = AutoModel.from_pretrained("z-lab/Qwen3.5-9B-DFlash", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use z-lab/Qwen3.5-9B-DFlash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "z-lab/Qwen3.5-9B-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/Qwen3.5-9B-DFlash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/z-lab/Qwen3.5-9B-DFlash

SGLang

How to use z-lab/Qwen3.5-9B-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/Qwen3.5-9B-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/Qwen3.5-9B-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/Qwen3.5-9B-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/Qwen3.5-9B-DFlash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use z-lab/Qwen3.5-9B-DFlash with Docker Model Runner:
```
docker model run hf.co/z-lab/Qwen3.5-9B-DFlash
```

jianchen0311 commited on Mar 12

Commit

a8c8dde

verified ·

1 Parent(s): 26fcd56

Update README.md

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README.md +7 -6

README.md CHANGED Viewed

@@ -49,6 +49,7 @@ python -m sglang.launch_server \
     --reasoning-parser qwen3 \
     --tool-call-parser qwen3_coder
 ```
 #### Early Results
 - Thinking: enabled
@@ -56,9 +57,9 @@ python -m sglang.launch_server \
 - Block size: 16
 | Dataset   | Accept Length |
 |-----------|---------------|
-| GSM8K     | 6.783 |
-| Math500   | 7.415 |
-| HumanEval | 7.940 |
-| MBPP      | 6.684 |
-| MT-Bench  | 5.567 |
-| Alpaca    | 5.107 |

     --reasoning-parser qwen3 \
     --tool-call-parser qwen3_coder
 ```
+> **Note:** For long-context or agentic usage (such as OpenClaw or Claude Code), consider adding `--speculative-dflash-draft-window-size WINDOW_SIZE` to enable sliding-window attention for the draft model. Because the draft model is trained on 4K context, this often improves performance on very long context (50K+ tokens).
 #### Early Results
 - Thinking: enabled
 - Block size: 16
 | Dataset   | Accept Length |
 |-----------|---------------|
+| GSM8K     | 6.709 |
+| Math500   | 7.388 |
+| HumanEval | 7.888 |
+| MBPP      | 6.617 |
+| MT-Bench  | 5.506 |
+| Alpaca    | 5.079 |