Update DFlash model card

README.md

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+---
+license: other
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- dflash
+- speculative-decoding
+- block-diffusion
+- draft-model
+- efficiency
+- minimax
+- minimax_m2
+- diffusion-language-model
+---
+
+# MiniMax-M2.5-DFlash
+
+[**Paper**](https://arxiv.org/abs/2602.06036) | [**GitHub**](https://github.com/z-lab/dflash) | [**Blog**](https://z-lab.ai/projects/dflash/)
+
+**DFlash** is a speculative decoding method that uses a lightweight **block diffusion** model to draft multiple tokens in parallel. This is the drafter model, which must be paired with [MiniMaxAI/MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5).
+
+<div align="center">
+  <img src="https://huggingface.co/z-lab/gemma-4-31B-it-DFlash/resolve/main/assets/dflash_system.png" alt="DFlash Architecture" width="85%">
+</div>
+
+## Quick Start
+
+### Installation
+
+vLLM:
+
+Check out [vLLM issue #46105](https://github.com/vllm-project/vllm/issues/46105).
+
+SGLang:
+
+```bash
+uv pip install "git+https://github.com/sgl-project/sglang.git#subdirectory=python"
+```
+
+### Launch Server
+
+vLLM:
+
+Check out [vLLM issue #46105](https://github.com/vllm-project/vllm/issues/46105).
+
+SGLang:
+
+```bash
+python -m sglang.launch_server \
+  --model-path MiniMaxAI/MiniMax-M2.5 \
+  --tp-size 4 \
+  --speculative-algorithm DFLASH \
+  --speculative-draft-model-path z-lab/MiniMax-M2.5-DFlash \
+  --attention-backend trtllm_mha \
+  --speculative-draft-attention-backend fa4 \
+  --mem-fraction-static 0.8 \
+  --trust-remote-code \
+  --host 0.0.0.0 \
+  --port 30000
+```
+
+### Usage
+
+For SGLang, use port `30000`.
+
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
+
+response = client.chat.completions.create(
+    model="MiniMaxAI/MiniMax-M2.5",
+    messages=[{"role": "user", "content": "Write a quicksort in Python."}],
+    max_tokens=4096,
+    temperature=0.0,
+    extra_body={"chat_template_kwargs": {"enable_thinking": True}},
+)
+print(response.choices[0].message.content)
+```
+
+## Benchmark Results
+
+**Setup:** 4 NVIDIA B200 GPUs per server/run, SGLang, tensor parallel size 4, target attention backend `trtllm_mha`, draft attention backend `fa4`, thinking enabled, max output length 4096, greedy decoding. Concurrency 1 uses 128 prompts; concurrency 32 uses 1024 prompts.
+
+### Throughput
+
+_Generated tokens/sec_
+
+**Block Size = 8**
+
+| Task | Concurrency | **DFlash** |
+|---|---:|---:|
+| Math500 | 1 | **355.17** |
+|  | 32 | **4619.18** |
+| GSM8K | 1 | **347.84** |
+|  | 32 | **4161.22** |
+| HumanEval | 1 | **331.03** |
+|  | 32 | **4329.96** |
+| MT-Bench | 1 | **385.45** |
+|  | 32 | **4658.84** |
+
+### Acceptance Length
+
+| Task | c1 | c32 |
+|---|---:|---:|
+| Math500 | 4.503 | 4.516 |
+| GSM8K | 4.342 | 4.338 |
+| HumanEval | 3.923 | 3.979 |
+| MT-Bench | 4.382 | 4.184 |
+
+## Acknowledgements
+
+Special thanks to [David Wang](https://davidwa.ng/) for his outstanding engineering support on this project. We are also grateful to [Modal](https://modal.com/), [InnoMatrix](https://innomatrix.ai), and [Yotta Labs](https://www.yottalabs.ai/) for providing the compute resources used to train this draft model.
+
+## Citation
+
+If you find DFlash useful, please cite our work. To share feedback on DFlash or request new model support, please fill out this form: [DFlash Feedback](https://forms.gle/4YNwfqb4nJdqn6hq9).
+
+```bibtex
+@article{chen2026dflash,
+  title   = {{DFlash: Block Diffusion for Flash Speculative Decoding}},
+  author  = {Chen, Jian and Liang, Yesheng and Liu, Zhijian},
+  journal = {arXiv preprint arXiv:2602.06036},
+  year    = {2026}
+}
+```