Muse-Glimmer-30B-DFlash2-GGUF

Muse Glimmer 30B DFlash2 GGUF

GGUF quantizations of Inco AI DFlash2 draft model for Muse Glimmer 30B.

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

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Muse-Glimmer-30B-DFlash2

Blog | GitHub

This repository contains the DFlash 2 draft model for meta-models/Muse-Glimmer-30B. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify. It is finetuned from meta-models/Muse-Glimmer-30B-assistant, the official DFlash drafter Meta ships with the model. The checkpoint is also mirrored at z-lab/Muse-Glimmer-30B-DFlash2.

DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts a whole block of tokens in a single pass and keeps the top candidates at every position. A lightweight selector then traces one coherent path through them. Two-tap dynamic convolutions in the backbone keep the draft from decaying toward the end of the block. Decoding is lossless: greedy output matches the target model exactly, and sampling preserves its distribution.

<div align="center"> <img src="assets/dflash2-figure.png" alt="DFlash 2: parallel block drafting with a candidate path selector" width="100%"> </div>

Quick Start

Serve with SGLang:

pip install "sglang[all] @ git+https://github.com/sgl-project/sglang.git#subdirectory=python"

python -m sglang.launch_server \
  --model-path meta-models/Muse-Glimmer-30B \
  --speculative-algorithm DFLASH \
  --speculative-draft-model-path incoai/Muse-Glimmer-30B-DFlash2 \
  --speculative-num-draft-tokens 16

Or with vLLM:

pip install -U "vllm @ git+https://github.com/vllm-project/vllm.git@refs/pull/52816/head"

vllm serve meta-models/Muse-Glimmer-30B \
  --speculative-config '{
    "method": "dflash",
    "model": "incoai/Muse-Glimmer-30B-DFlash2",
    "num_speculative_tokens": 15
  }'

See the blog post for other engines and more details.

Evaluation

• Runtime: SGLang on one NVIDIA H200, with FlashAttention 3 for target and draft attention
• Speculation block size: 16 (15 draft tokens per verification step)
• Sampling: Muse's officially recommended parameters (temperature 1.0, top-p 0.95, top-k 64), with high reasoning strength
• Maximum new tokens: 4096
• Prompts: benchmark formatting from z-lab/dflash

We compare autoregressive decoding, the official DFlash drafter (meta-models/Muse-Glimmer-30B-assistant), a community DSpark drafter (DaoCloud/Muse-Glimmer-30B-DSpark), and DFlash 2. All speculative methods propose fifteen draft tokens per verification step.

Acceptance Length

Acceptance length is the per-request mean of completion tokens divided by verification steps. Higher is better.

Throughput

Throughput is total output tokens divided by end-to-end wall time. Each cell shows output tok/s (speedup vs. autoregressive).

Concurrency 1

Concurrency 8

Concurrency 32

Citation

If you find DFlash 2 useful, please cite:

@misc{inco2026dflash2,
  title  = {{DFlash 2: Keep Drafting Parallel}},
  author = {{Inco AI}},
  year   = {2026},
  month  = {August},
  url    = {https://inco.ai/blog/dflash2/}
}

Please also cite the original DFlash paper:

@inproceedings{chen2026dflash,
  title     = {{DFlash: Block Diffusion for Flash Speculative Decoding}},
  author    = {Chen, Jian and Liang, Yesheng and Liu, Zhijian},
  booktitle = {International Conference on Machine Learning (ICML)},
  year      = {2026}
}