Muse-Glimmer-30B-DFlash2-GGUF

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This repository contains GGUF conversions of incoai/Muse-Glimmer-30B-DFlash2, 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. The checkpoints are also mirrored at z-lab/Muse-Glimmer-30B-DFlash2-GGUF.

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.

Quick Start

Build llama.cpp with DFlash 2 support (PR #27342):

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
git fetch origin pull/27342/head:pr-27342
git switch pr-27342

# NVIDIA CUDA
cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON
cmake --build build -j

# Apple Silicon
cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_METAL=ON
cmake --build build -j

Then serve:

./build/bin/llama-server \
  -hf meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M \
  -hfd incoai/Muse-Glimmer-30B-DFlash2-GGUF:Q4_K_M \
  --spec-type draft-dflash \
  --spec-draft-n-max 15

See the blog post for other engines and more details.

Evaluation

• Target: meta-models/Muse-Glimmer-30B-GGUF, Q4_K_M
• Sampling: Muse's officially recommended parameters (temperature 1.0, top-p 0.95, top-k 64), with high reasoning strength
• Maximum new tokens: 2048
• Prompts: the first eight GSM8K test examples

Acceptance Length

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

Full evaluations of the base checkpoint are on the main model card.

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}
}