RedHatAI/Qwen3.6-35B-A3B-speculator.dspark · Hugging Face
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Machine Learning Model
Summary
This repository hosts the DFlash drafter model, a novel speculative decoding method that utilizes a lightweight block diffusion model to enhance inference speed. It is designed to be used in conjunction with the Qwen/Qwen3.6-27B target model to enable efficient, high-quality parallel drafting.
Highlights
- Introduces DFlash, a speculative decoding method using block diffusion for drafting.
- Serves as a specialized drafter component for the Qwen/Qwen3.6-27B model.
- Provides specific installation instructions for vLLM and SGLang to support architectural changes like causal SWA layers.
- Aims to push the limits of inference speed through efficient parallel drafting.
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Context
Audience
Machine Learning Engineers and AI Researchers
DomainMachine Learning
Formatmodel repository
Accessopen source
Source Overview
DFlash is a lightweight block diffusion drafter model that accelerates inference for Qwen3.6-27B via speculative decoding.
It drafts up to 16 parallel tokens and requires the Qwen3.6-27B target model. The model is still under training, with full vLLM and SGLang support for its causal SWA layers pending.
Topics
Related
Speculative DecodingQwenvLLMSGLangBlock DiffusionLarge Language Models
