Instructions to use z-lab/Muse-Glimmer-30B-DFlash2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use z-lab/Muse-Glimmer-30B-DFlash2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="z-lab/Muse-Glimmer-30B-DFlash2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("z-lab/Muse-Glimmer-30B-DFlash2")
model = AutoModel.from_pretrained("z-lab/Muse-Glimmer-30B-DFlash2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use z-lab/Muse-Glimmer-30B-DFlash2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "z-lab/Muse-Glimmer-30B-DFlash2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "z-lab/Muse-Glimmer-30B-DFlash2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/z-lab/Muse-Glimmer-30B-DFlash2

SGLang

How to use z-lab/Muse-Glimmer-30B-DFlash2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "z-lab/Muse-Glimmer-30B-DFlash2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "z-lab/Muse-Glimmer-30B-DFlash2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "z-lab/Muse-Glimmer-30B-DFlash2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "z-lab/Muse-Glimmer-30B-DFlash2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use z-lab/Muse-Glimmer-30B-DFlash2 with Docker Model Runner:
```
docker model run hf.co/z-lab/Muse-Glimmer-30B-DFlash2
```

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. This repository is a mirror of incoai/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.

DFlash 2: parallel block drafting with a candidate path selector

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.

Task	Official DFlash	DSpark	DFlash 2
GSM8K	5.43	5.45	6.57
MATH-500	5.39	5.01	6.56
HumanEval	4.11	4.33	5.66
MBPP	3.74	4.02	5.30
MT-Bench	3.52	3.59	4.42

Throughput

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

Concurrency 1

Task	Autoregressive	Official DFlash	DSpark	DFlash 2
GSM8K	63.9	247.8 (3.88×)	236.5 (3.70×)	293.7 (4.59×)
MATH-500	64.0	246.3 (3.85×)	218.4 (3.41×)	295.5 (4.62×)
HumanEval	65.1	210.5 (3.23×)	201.4 (3.09×)	266.2 (4.09×)
MBPP	63.9	196.8 (3.08×)	192.7 (3.02×)	264.8 (4.14×)
MT-Bench	64.0	164.6 (2.57×)	159.7 (2.49×)	197.4 (3.08×)

Concurrency 8

Task	Autoregressive	Official DFlash	DSpark	DFlash 2
GSM8K	476.6	1,574.4 (3.30×)	1,456.1 (3.06×)	1,816.6 (3.81×)
MATH-500	466.0	1,582.9 (3.40×)	1,386.0 (2.97×)	1,859.3 (3.99×)
HumanEval	499.9	1,419.8 (2.84×)	1,315.4 (2.63×)	1,784.9 (3.57×)
MBPP	491.6	1,278.9 (2.60×)	1,266.6 (2.58×)	1,719.7 (3.50×)
MT-Bench	470.0	1,078.4 (2.29×)	1,052.6 (2.24×)	1,288.9 (2.74×)

Concurrency 32

Task	Autoregressive	Official DFlash	DSpark	DFlash 2
GSM8K	1,705.6	2,330.3 (1.37×)	2,301.7 (1.35×)	2,818.3 (1.65×)
MATH-500	1,710.2	2,427.3 (1.42×)	2,185.0 (1.28×)	2,869.6 (1.68×)
HumanEval	1,798.1	2,170.0 (1.21×)	2,068.9 (1.15×)	2,780.2 (1.55×)
MBPP	1,717.5	1,964.6 (1.14×)	2,006.5 (1.17×)	2,685.4 (1.56×)
MT-Bench	1,721.8	1,668.0 (0.97×)	1,627.2 (0.95×)	1,975.5 (1.15×)

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}
}