Add model card and metadata (#1)

- Add model card and metadata (558b862f8634ceeae4a35dabb7c362ac3bfe7a56)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

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+---
+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}
+}
+```