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)

Notebooks
Google Colab
Kaggle
Local Apps

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
```

DFlash compatibility with AutoRound (W4A16) quantized Qwen3.5-9B in vLLM?

by Vishva007 - opened Mar 24

Discussion

Vishva007

Mar 24

Hi,

I’m exploring DFlash speculative decoding with Qwen 3.5 models and wanted to clarify compatibility with quantized setups.

I’m currently using an AutoRound-quantized model:
Vishva007/Qwen3.5-9B-W4A16-AutoRound

Since DFlash support in vLLM is still under development (PRs #36847 and #36767), I had a few questions:

Is this z-lab/Qwen3.5-9B-DFlash model compatible with AutoRound W4A16 quantized models?
Does DFlash currently require FP16/BF16 weights for the draft model, or can it work with 4-bit quantized weights?
What is the recommended way to test DFlash in vLLM right now, given that support is still in PR stage?
Are there any known limitations similar to Eagle3 (BLR2/Eagle3-Qwen3.5-9B), which doesn’t seem to work with quantized models?

I’m very interested in experimenting with DFlash and would appreciate any guidance or examples to get started.

Thanks in advance!

Agnellino

Apr 13

+1, I'd be so glad if you guys built a working version for Q4_K_M quantization !

TomLucidor

10 days ago

Are there better and/or alternative quants?

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