---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
---

# dMoE-16B: dLLMs with Learnable Block Experts

[dMoE](https://fscdc.github.io/dMoE/) is a block-level Mixture-of-Experts (MoE) framework designed for Diffusion Large Language Models (dLLMs). By aggregating token-level expert distributions within each block into a unified block-level distribution, dMoE substantially reduces the number of uniquely activated experts during inference, mitigating memory-bound bottlenecks without sacrificing performance.

- **Paper:** [dMoE: dLLMs with Learnable Block Experts](https://huggingface.co/papers/2605.30876)
- **Project Page:** [https://fscdc.github.io/dMoE/](https://fscdc.github.io/dMoE/)
- **Repository:** [https://github.com/fscdc/dMoE](https://github.com/fscdc/dMoE)

## Highlights

- **Learnable Block Experts**: Introduces block-level MoE routing into dLLMs, drastically compressing the number of activated unique experts across diffusion steps.
- **Reduced MoE Bandwidth**: Significantly reduces memory bandwidth consumed by expert weight loading during the block diffusion process.
- **Improved Efficiency-Accuracy Trade-off**: Achieves 1.14x to 1.66x end-to-end latency speedup while maintaining competitive performance on benchmarks.
- **Plug-and-play on LLaDA-2.0**: Built directly on top of LLaDA-2.0-mini without architectural changes.

## Sample Usage

The model can be used with the Transformers library. Note that it requires `trust_remote_code=True` to load the custom architecture.

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

MODEL_NAME = "FSCCS/dMoE-16B"
device = "cuda:0"

model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME, trust_remote_code=True, torch_dtype=torch.bfloat16
).to(device).eval()

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)

prompt = "A robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?" + "
Let's think step by step
"

messages = [[{"role": "user", "content": prompt}]]
input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)

inputs = tokenizer(input_text, return_tensors="pt", padding_side="left")
input_ids = inputs["input_ids"].to(device)

with torch.no_grad():
    out, unique_experts_count = model.generate(
        input_ids,
        steps=32,
        gen_length=2048,
        block_length=32,
        temperature=0.0,
        eos_early_stop=True,
    )

generated = out[:, input_ids.shape[1]:]
result = tokenizer.batch_decode(generated, skip_special_tokens=True)

print("Output:", result[0])
print("Unique experts count:", unique_experts_count)
```

## Citation

```bibtex
@article{feng2026dmoe,
  title={dMoE: dLLMs with Learnable Block Experts},
  author={Feng, Sicheng and Chen, Zigeng and Fang, Gongfan and Ma, Xinyin and Wang, Xinchao},
  journal={arXiv preprint arXiv:2605.30876},
  year={2026}
}
```