Instructions to use yresearch/DMax-Math-MMD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yresearch/DMax-Math-MMD with Transformers:

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

pipe = pipeline("text-generation", model="yresearch/DMax-Math-MMD", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("yresearch/DMax-Math-MMD", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yresearch/DMax-Math-MMD with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yresearch/DMax-Math-MMD"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yresearch/DMax-Math-MMD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yresearch/DMax-Math-MMD

SGLang

How to use yresearch/DMax-Math-MMD 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 "yresearch/DMax-Math-MMD" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yresearch/DMax-Math-MMD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "yresearch/DMax-Math-MMD" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yresearch/DMax-Math-MMD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yresearch/DMax-Math-MMD with Docker Model Runner:
```
docker model run hf.co/yresearch/DMax-Math-MMD
```

DMax-Math-MMD

Representation-Space MMD for Diffusion Language Models

DMax-Math-MMD is a 16B diffusion language model for mathematical reasoning, obtained by MMD post-training of DMax-Math-16B. It builds on LLaDA2.0-mini and uses DMax's hybrid masked–uniform block diffusion.

The post-training objective minimizes Maximum Mean Discrepancy (MMD) between model samples and reference responses, measured in the representation space of a frozen diffusion language model. The project reports increased tokens per forward pass while maintaining or improving accuracy on the benchmarks below.

Reference results

Results reported in the project README, with decoding threshold 0.85. Each entry is accuracy (%) / tokens per forward pass (TPF). Baseline results are attributed to the original DMax paper in the project README.

Method	GSM8K	MATH500	Minerva-Algebra	ASDIV
DMax-Math	92.1 / 5.48	75.4 / 5.94	91.5 / 7.03	92.5 / 5.62
DMax-Math-MMD	92.1 / 6.15	76.0 / 6.84	92.1 / 8.19	92.9 / 6.20

Results can vary with hardware, tensor parallelism, and library versions. TPF measures decoding parallelism; wall-clock speed also depends on the runtime.

Evaluation and inference

Use DMax's dInfer evaluation pipeline. After following the evaluation environment setup in the project README, run from the project repository root:

conda activate dinfer
DOMAIN=math MODEL_PATH=yresearch/DMax-Math-MMD bash scripts/eval.sh

The project's math evaluation uses threshold 0.85 and evaluates GSM8K, MATH500, Minerva-Algebra, and ASDIV.

The checkpoint includes nine Safetensors shards with BF16 parameters and FP32 router bias buffers, plus its tokenizer, chat template, and custom model code. Direct Transformers loading requires trust_remote_code=True. Pass dtype=torch.bfloat16 for BF16 loading; the current configuration declares FP32.

Generation requires the DMax/dInfer diffusion decoder. The included custom backbone does not implement the standard Transformers .generate() interface.

License and acknowledgements

The model follows the Apache-2.0 license of the DMax-Math base checkpoint and LLaDA2.0-mini. The included model implementation retains its Apache-2.0 notices. The separate MMD training repository is MIT-licensed, with Apache-2.0 third-party components.

We thank the authors of DMax, LLaDA2.0-mini, and dInfer for releasing their models, data, and code.

Citation

If you find this work useful in your research, please consider citing:

@article{drobyshevskiy2026mmd,
  title  = {Representation-Space MMD for Diffusion Language Models},
  author = {Drobyshevskiy, Ilya and Sudakov, Ilia and Semenov, Maksim and Kuznedelev, Denis and
            Ignatov, Maksim and Temirchev, Pavel and Balagansky, Nikita and
            Meshchaninov, Viacheslav and Gushchin, Nikita and Baranchuk, Dmitry},
  journal = {arXiv preprint arXiv:2610.06648},
  year    = {2026}
}