---

license: apache-2.0

library_name: llama.cpp

pipeline_tag: text-generation

base_model:

- meta-models/Muse-Glimmer-30B

inference: false

tags:

- gguf

- dflash2

- speculative-decoding

- draft-model

- llama.cpp

---

Muse-Glimmer-30B-DFlash2-GGUF

Blog | GitHub

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. This

repository is a mirror of

incoai/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.

<div align="center">

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

</div>

| File | Size |

| :--- | ---: |

| Muse-Glimmer-30B-DFlash2-Q4_K_M.gguf | 1.6 GB |

| Muse-Glimmer-30B-DFlash2-Q8_0.gguf | 2.9 GB |

| Muse-Glimmer-30B-DFlash2-BF16.gguf | 5.5 GB |

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.

| Draft GGUF | Acceptance Length |

| :--- | ---: |

| BF16 | 5.45 |

| Q8_0 | 5.58 |

| Q4_K_M | 5.44 |

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}
}