Add model card and metadata

README.md

@@ -1 +1,31 @@
-arxiv.org/abs/2603.27027
+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- speculative-decoding
+---
+
+# TAPS: Task-Aware Proposal Distributions for Speculative Sampling
+
+This repository contains a draft model introduced in the paper [TAPS: Task Aware Proposal Distributions for Speculative Sampling](https://huggingface.co/papers/2603.27027).
+
+## Overview
+
+TAPS (Task-Aware Proposal Distributions) investigates how the training distribution of draft models affects the efficiency of speculative decoding. In speculative decoding, a lightweight draft model proposes future tokens that a larger target model (like Meta-Llama-3-8B-Instruct) verifies in parallel. 
+
+This model is a lightweight LLaMA-style drafter (approximately 0.8B parameters with 1 layer) designed to accelerate autoregressive generation. The research demonstrates that task-specific training (e.g., on MathInstruct or ShareGPT) yields significant specialization, and that specialized drafters are best combined at inference time using strategies like confidence-based routing or merged-tree verification.
+
+## Resources
+- **Paper:** [TAPS: Task Aware Proposal Distributions for Speculative Sampling](https://huggingface.co/papers/2603.27027)
+- **GitHub Repository:** [https://github.com/Moe-Zbeeb/TAPS](https://github.com/Moe-Zbeeb/TAPS)
+
+## Citation
+```bibtex
+@article{zbib2026taps,
+  title={TAPS: Task Aware Proposal Distributions for Speculative Sampling},
+  author={Zbib, Mohamad and Bazzi, Mohamad and Mohanna, Ammar and Ghanem, Bernard and Hammoud, Hasan Abed Al Kader},
+  year={2026},
+  note={Technical report}
+}
+```