Qwen 3.6 27B DFlash GGUF
GGUF quantizations of z-lab DFlash draft model for Qwen 3.6 27B.
Use with BeeLlama.cpp, a llama.cpp fork with advanced quantization features.
Qwen3.6-27B-DFlash
This model is still under training, and inference engine support may not be fully available yet due to architectural changes, including causal SWA layers.
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.
This model is the drafter component. It must be used in conjunction with the target model Qwen/Qwen3.6-27B.
Quick Start
Installation
vLLM (We temporarily modify the installation through this PR to support interleaved SWA and ensure correct handling of target hidden states for optimal performance):
uv pip install vllm
uv pip install -U --torch-backend=auto "vllm @ git+https://github.com/vllm-project/vllm.git@refs/pull/40898/head"
SGLang:
uv pip install "git+https://github.com/sgl-project/sglang.git@refs/pull/23000/head#subdirectory=python"
Launch Server
vLLM:
vllm serve Qwen/Qwen3.6-27B \
--speculative-config '{"method": "dflash", "model": "z-lab/Qwen3.6-27B-DFlash", "num_speculative_tokens": 15}' \
--attention-backend flash_attn \
--max-num-batched-tokens 32768
SGLang:
# Optional: enable schedule overlapping (experimental, may not be stable)
# export SGLANG_ENABLE_SPEC_V2=1
# export SGLANG_ENABLE_DFLASH_SPEC_V2=1
# export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
python -m sglang.launch_server \
--model-path Qwen/Qwen3.6-27B \
--speculative-algorithm DFLASH \
--speculative-draft-model-path z-lab/Qwen3.6-27B-DFlash \
--speculative-num-draft-tokens 16 \
--tp-size 1 \
--attention-backend fa3 \
--mem-fraction-static 0.75 \
--mamba-scheduler-strategy extra_buffer \
--trust-remote-code
Usage
from openai import OpenAI
client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
response = client.chat.completions.create(
model="Qwen/Qwen3.6-27B",
messages=[{"role": "user", "content": "Write a quicksort in Python."}],
max_tokens=4096,
temperature=0.0
)
print(response.choices[0].message.content)
Benchmark Results
N/A
Acknowledgements
Special thanks to David Wang for his outstanding engineering support on this project. We are also grateful to Modal, InnoMatrix, and Yotta Labs for providing the compute resources used to train this draft model.
Citation
If you find DFlash useful, please cite our work. To share feedback on DFlash or request new model support, please fill out this form: DFlash Feedback.
@article{chen2026dflash,
title = {{DFlash: Block Diffusion for Flash Speculative Decoding}},
author = {Chen, Jian and Liang, Yesheng and Liu, Zhijian},
journal = {arXiv preprint arXiv:2602.06036},
year = {2026}
}