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
```

Qwen3.6-27B-DFlash-FP16 / README.md

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	---
	license: mit
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- dflash
	- speculative-decoding
	- draft-model
	base_model: z-lab/Qwen3.6-27B-DFlash
	---

	This model was [converted](https://github.com/deepsweet/bf16-to-fp16) to FP16 from [z-lab/Qwen3.6-27B-DFlash](https://huggingface.co/z-lab/Qwen3.6-27B-DFlash) BF16.

	## What is "DFlash"?

	>DFlash is a novel speculative decoding method that utilizes a lightweight block diffusion model for drafting. It enables efficient, high-quality parallel drafting that pushes the limits of inference speed.

	## What is "FP16"?

	"FP16" is M1/M2 Apple Silicon only optimization that leads to a very noticeable prompt processing boost. See ["Metal FP32 Vs BF16 Vs FP16 benchmark"](https://github.com/deepsweet/metal-fp32-bf16-fp16) and [jundot/omlx/pull/880](https://github.com/jundot/omlx/pull/880) for details.

	Use the original model if you have M3+ Apple Silicon.