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.
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
- GitHub Repository: https://github.com/Moe-Zbeeb/TAPS
Citation
@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}
}