README.md

---
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}
}
```