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

# DMax-Coder-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-Coder-MMD is a 16B diffusion language model for code generation,
obtained by MMD post-training of
[DMax-Coder-16B](https://huggingface.co/Zigeng/DMax-Coder-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 **0.9**.
Each entry is **accuracy (%) / tokens per forward pass (TPF)**.
Baseline results are attributed to the original DMax paper in the project README.

| Method | HumanEval-Instruct | MBPP-Instruct |
| --- | :---: | :---: |
| DMax-Coder | 83.5 / 7.36 | 79.2 / 5.86 |
| **DMax-Coder-MMD** | **85.9** / **8.07** | **83.0** / **6.10** |

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=code MODEL_PATH=yresearch/DMax-Coder-MMD bash scripts/eval.sh
```

The project's code evaluation uses threshold **0.9** and evaluates
HumanEval-Instruct and MBPP-Instruct.

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-Coder base checkpoint](https://huggingface.co/Zigeng/DMax-Coder-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}
}
```