Muse-Glimmer-30B-DFlash2-GGUF
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.
| 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}
}