Instructions to use z-lab/Qwen3.5-27B-DFlash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use z-lab/Qwen3.5-27B-DFlash with Transformers:

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

pipe = pipeline("text-generation", model="z-lab/Qwen3.5-27B-DFlash", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("z-lab/Qwen3.5-27B-DFlash", trust_remote_code=True)
model = AutoModel.from_pretrained("z-lab/Qwen3.5-27B-DFlash", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use z-lab/Qwen3.5-27B-DFlash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "z-lab/Qwen3.5-27B-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/Qwen3.5-27B-DFlash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/z-lab/Qwen3.5-27B-DFlash

SGLang

How to use z-lab/Qwen3.5-27B-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/Qwen3.5-27B-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/Qwen3.5-27B-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/Qwen3.5-27B-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/Qwen3.5-27B-DFlash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use z-lab/Qwen3.5-27B-DFlash with Docker Model Runner:
```
docker model run hf.co/z-lab/Qwen3.5-27B-DFlash
```

Qwen3.5-4B/9B dflash supports VL mode

by huzhua - opened Apr 11

Discussion

huzhua

Apr 11

Good work, now I see the speed up on my GPU with text mode, not sure if it is workable for VL mode for qwen3.5-4B/9B model? BTW, does it work for int4 quantization? Thanks.

jianchen0311

Z Lab org Apr 11

Yes, we tested Qwen3.5-9B-DFlash on various VL tasks and it works well.

Note that the speedup here is end-to-end, which includes the prefill time, so DFlash’s speedup is amortized by the long prefill time of VL input. But the acceptance length is still generally good.

For quantized target model, yes DFlash should be able to work with int4 target model, achieving similar acceptance length with the BF16 one.

huzhua

Apr 18

VL works well on our int4 model also. furthermore, I found one interesting thing that, with longer context, the acceptance rate will drop dramatically, even for the test bench which initially has very high acceptance. Did you meet the similar issue? Any method to solve it? Thanks.

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