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 / README.md

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	---
	license: apache-2.0
	library_name: transformers
	base_model: Zigeng/DMax-Math-16B
	base_model_relation: finetune
	datasets:
	- Zigeng/DMax-LLaDA-2.0-Mini-Math-Trajectories
	tags:
	- diffusion
	- dllm
	- mmd
	- math
	- custom_code
	---

	# DMax-Math-MMD

	[![License: Apache-2.0](https://img.shields.io/badge/License-Apache--2.0-blue.svg)](https://www.apache.org/licenses/LICENSE-2.0) [![Arxiv](https://img.shields.io/badge/arXiv-Paper-b31b1b.svg)](https://arxiv.org/abs/2610.06648) [![GitHub: Code](https://img.shields.io/badge/GitHub-Code-181717.svg?logo=github)](https://github.com/yandex-research/dlm-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](https://huggingface.co/Zigeng/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](https://github.com/yandex-research/dlm-mmd#installation),
	run from the project repository root:

	```bash
	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](https://huggingface.co/Zigeng/DMax-Math-16B)
	and [LLaDA2.0-mini](https://huggingface.co/inclusionAI/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](https://github.com/czg1225/DMax),
	[LLaDA2.0-mini](https://huggingface.co/inclusionAI/LLaDA2.0-mini), and
	[dInfer](https://github.com/inclusionAI/dInfer) for releasing their models, data, and code.

	## Citation

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

	```bibtex
	@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}
	}
	```