Instructions to use deepsweet/Qwen3.6-27B-DFlash-FP16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepsweet/Qwen3.6-27B-DFlash-FP16 with Transformers:

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

pipe = pipeline("text-generation", model="deepsweet/Qwen3.6-27B-DFlash-FP16", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("deepsweet/Qwen3.6-27B-DFlash-FP16", trust_remote_code=True)
model = AutoModel.from_pretrained("deepsweet/Qwen3.6-27B-DFlash-FP16", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use deepsweet/Qwen3.6-27B-DFlash-FP16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deepsweet/Qwen3.6-27B-DFlash-FP16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepsweet/Qwen3.6-27B-DFlash-FP16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/deepsweet/Qwen3.6-27B-DFlash-FP16

SGLang

How to use deepsweet/Qwen3.6-27B-DFlash-FP16 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 "deepsweet/Qwen3.6-27B-DFlash-FP16" \
    --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": "deepsweet/Qwen3.6-27B-DFlash-FP16",
		"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 "deepsweet/Qwen3.6-27B-DFlash-FP16" \
        --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": "deepsweet/Qwen3.6-27B-DFlash-FP16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use deepsweet/Qwen3.6-27B-DFlash-FP16 with Docker Model Runner:
```
docker model run hf.co/deepsweet/Qwen3.6-27B-DFlash-FP16
```

Openclaw compatibility problem

by felkf - opened 9 days ago

Discussion

felkf

9 days ago

The noticeable improvement in token generation speed with dflash is really impressive. But if need structured output and tool use like OpenClose, dflash seems to be holding it back. Any suggestions?

deepsweet

Owner 9 days ago

•

edited 9 days ago

The way I understand it, the inference engine should not pass structured data / tools through the speculative route. I'd ask the original model authors first https://huggingface.co/z-lab/Qwen3.6-27B-DFlash, I might be wrong.

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