Instructions to use z-lab/Qwen3.6-27B-DFlash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use z-lab/Qwen3.6-27B-DFlash with Transformers:

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

pipe = pipeline("text-generation", model="z-lab/Qwen3.6-27B-DFlash", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("z-lab/Qwen3.6-27B-DFlash", trust_remote_code=True)
model = AutoModel.from_pretrained("z-lab/Qwen3.6-27B-DFlash", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use z-lab/Qwen3.6-27B-DFlash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "z-lab/Qwen3.6-27B-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/Qwen3.6-27B-DFlash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/z-lab/Qwen3.6-27B-DFlash

SGLang

How to use z-lab/Qwen3.6-27B-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/Qwen3.6-27B-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/Qwen3.6-27B-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/Qwen3.6-27B-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/Qwen3.6-27B-DFlash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use z-lab/Qwen3.6-27B-DFlash with Docker Model Runner:
```
docker model run hf.co/z-lab/Qwen3.6-27B-DFlash
```

Thank you!

by xneoenx - opened 13 days ago

Discussion

xneoenx

13 days ago

Hey Team,

Just wanted to drop a quick note here thanking you for your excellent work on this cutting edge field. The results have been phenomenal in all my tests.

Thank you!

chankhavu

13 days ago

They are still updating the model, it's not fully trained yet

xneoenx

12 days ago

Oh? Did I mess something up then?

8K raw /v1/completions results, each with an exact 8192-token prompt:
Case No DFlash DFlash
8192 -> 256 TTFT 32.304s TTFT 38.336s
E2E 48.873s E2E 42.579s
Gen 15.45 tok/s Gen 60.38 tok/s
Finish length Finish stop

8192 -> 2048 TTFT 32.290s TTFT 38.348s
E2E 165.223s E2E 72.972s
Gen 15.41 tok/s Gen 59.16 tok/s
Finish length Finish stop

8192 -> 10000 TTFT 32.295s TTFT 38.346s
E2E 691.417s E2E 228.615s
Gen 15.17 tok/s Gen 52.56 tok/s
Finish length Finish stop

yeanshuyu

5 days ago

thank you

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