Add model card and metadata

README.md

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+---
+library_name: transformers
+pipeline_tag: text-generation
+base_model: openPangu/openPangu-Embedded-7B
+tags:
+- diffusion
+- parallel-generation
+---
+
+# FLUID-7B
+
+FLUID (Flexible Unidirectional Inference Diffusion) is a framework designed to efficiently adapt pre-trained Autoregressive (AR) backbones into parallel diffusion models. By enforcing **Strictly Causal Alignment** and introducing **Elastic Horizons**, FLUID achieves state-of-the-art performance with significantly less training data compared to standard diffusion models.
+
+- **Paper:** [From AR to Diffusion: Efficiently Adapting Large Language Models with Strictly Causal and Elastic Horizons](https://huggingface.co/papers/2605.27387)
+- **GitHub Repository:** [Oli-lab-nun/FLUID](https://github.com/Oli-lab-nun/FLUID)
+
+## Key Features
+
+*   **Strictly Causal Alignment**: Unlike bidirectional diffusion, FLUID uses a lower-triangular attention mask to maintain the inductive biases of AR priors. This enables seamless initialization from GPT-style checkpoints like openPangu-Embedded-7B.
+*   **Elastic Horizon Modeling**: An entropy-driven mechanism that dynamically modulates denoising strides based on local information density. It "sprints" through predictable text and "downshifts" for complex reasoning.
+*   **Training Efficiency**: Achieves superior results on reasoning benchmarks using only 2.7B tokens of adaptation data, outperforming models trained on trillions of tokens.
+
+## Performance
+
+FLUID-7B matches or exceeds top-tier AR and Diffusion baselines across standard benchmarks:
+
+| Model | Type | Tokens | MMLU | GSM8K | MATH500 | HumanEval |
+| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
+| LLaMA-3-8B | AR | 15T | 68.4 | 78.3 | 27.4 | 59.8 |
+| Qwen-2.5-7B | AR | 18T | 76.6 | 91.6 | 84.8 | 79.2 |
+| LLaDA-8B | Diff | 2.0T | 65.5 | 36.2 | 34.2 | 47.6 |
+| **FLUID-7B (Ours)** | **Diff** | **2.7B** | **67.8** | **91.9** | **61.8** | **60.4** |
+
+## Acknowledgements
+
+FLUID-7B is adapted from the **openPangu-Embedded-7B** base model. We gratefully acknowledge the developers of openPangu for releasing their model and related resources to the community.
+
+## Citation
+
+```bibtex
+@inproceedings{fluid2026,
+  title={From AR to Diffusion: Efficiently Adapting Large Language Models with Strictly Causal and Elastic Horizons},
+  author={Anonymous},
+  booktitle={Submission to ACL 2026},
+  year={2026}
+}
+```