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Qwen3.6-35B-A3B-DFlash-GGUF

Name: Qwen3.6-35B-A3B-DFlash-GGUF
Author: Anbeeld

by Anbeeld · source Hugging Face · updated 2026-08-29

apache-2.0225 MB~1 GB RAMsource aliveunlabeled

GGUF quantizations of z-lab DFlash draft model for Qwen 3.6 35B A3B.

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Source and license

Source: https://huggingface.co/Anbeeld/Qwen3.6-35B-A3B-DFlash-GGUF
License: apache-2.0
Requirements: about 1 GB of RAM, 225 MB on disk (estimate: suggested file size × 1.15 + 0.5 GB; a real measurement comes with lsh models). Runs with llama.cpp.
Tags: transformers gguf qwen3 feature-extraction safetensors dflash speculative-decoding speculative-decoding-draft block-diffusion draft-model diffusion-language-model efficiency qwen qwen3.6 sglang text-generation

Numbers

4,444 downloads on Hugging Face
13 likes
license apache-2.0
0.2 GB for qwen36-35b-a3b-dflash-Q4_K_M.gguf
327,838 npm downloads a week for node-llama-cpp
latest node-llama-cpp@3.22.1

Numbers as of 2026-10-02 20:59 UTC, from the source API.

Summary

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Files

file	quant	size
`qwen36-35b-a3b-dflash-Q2_K.gguf`	Q2_K	146 MB
`qwen36-35b-a3b-dflash-Q3_K_M.gguf`	Q3_K_M	187 MB
`qwen36-35b-a3b-dflash-Q4_K_M.gguf`	Q4_K_M	225 MB
`qwen36-35b-a3b-dflash-Q5_K_M.gguf`	Q5_K_M	267 MB
`qwen36-35b-a3b-dflash-Q6_K.gguf`	Q6_K	312 MB
`qwen36-35b-a3b-dflash-Q8_0.gguf`	Q8_0	402 MB
`qwen36-35b-a3b-dflash-bf16.gguf`	BF16	747 MB

From the source README

Qwen 3.6 35B A3B DFlash GGUF

GGUF quantizations of z-lab DFlash draft model for Qwen 3.6 35B A3B.

Use with BeeLlama.cpp, a llama.cpp fork with advanced quantization features.

Qwen3.6-35B-A3B-DFlash

Paper | Github | Blog

This DFlash draft model is a joint retrain from Z-Lab and Modal, trained with 40k sequence length and sliding-window attention for improved long-context performance. It is mirrored across the following Hugging Face repositories:

`z-lab/Qwen3.6-35B-A3B-DFlash`
`modal-labs/Qwen3.6-35B-A3B-DFlash`

This repository contains a DFlash draft model for `Qwen/Qwen3.6-35B-A3B`. It is not a standalone language model. It is intended to be paired with the target model in a speculative decoding server.

DFlash uses a lightweight block diffusion draft model to propose multiple tokens in parallel. The target model verifies those proposals, improving serving throughput while preserving the target model's output distribution.

Quick Start

Installation

SGLang

Card id model:hf:Anbeeld/Qwen3.6-35B-A3B-DFlash-GGUF · collected 2026-10-02 20:59 UTC · JSON