Add model card and metadata
Browse filesHi! I'm Niels, part of the community science team at Hugging Face.
I've opened this PR to improve the model card for this repository. Specifically, I have:
- Added metadata to the YAML section (`pipeline_tag`, `library_name`, and `license`).
- Included a descriptive summary of the model and its role in speculative decoding as presented in the TAPS paper.
- Added links to the paper and the official GitHub repository.
- Added the BibTeX citation for the paper.
Feel free to merge this if it looks good to you!
README.md
CHANGED
|
@@ -1 +1,30 @@
|
|
| 1 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
library_name: transformers
|
| 4 |
+
pipeline_tag: text-generation
|
| 5 |
+
---
|
| 6 |
+
|
| 7 |
+
# TAPS: Task-Aware Proposal Distributions for Speculative Sampling
|
| 8 |
+
|
| 9 |
+
This repository contains a draft model checkpoint introduced in the paper [TAPS: Task Aware Proposal Distributions for Speculative Sampling](https://huggingface.co/papers/2603.27027).
|
| 10 |
+
|
| 11 |
+
## Introduction
|
| 12 |
+
TAPS studies how the training distribution of draft models shapes the quality and efficiency of speculative decoding. By utilizing task-specific training (e.g., on MathInstruct or ShareGPT), draft models can achieve significant acceleration on matched downstream workloads. This repository provides specialized drafters (such as HASS or EAGLE-2 variants) designed for use with larger verifier models like Meta-Llama-3-8B-Instruct.
|
| 13 |
+
|
| 14 |
+
## Resources
|
| 15 |
+
- **Paper:** [TAPS: Task Aware Proposal Distributions for Speculative Sampling](https://arxiv.org/abs/2603.27027)
|
| 16 |
+
- **GitHub Repository:** [Moe-Zbeeb/TAPS](https://github.com/Moe-Zbeeb/TAPS)
|
| 17 |
+
|
| 18 |
+
## Model Details
|
| 19 |
+
This checkpoint is a lightweight, single-layer Llama-style drafter (~0.8B parameters). It is intended to be used in a speculative decoding pipeline to propose future tokens, which are then verified in parallel by a larger target model.
|
| 20 |
+
|
| 21 |
+
## Citation
|
| 22 |
+
If you find this work useful, please cite:
|
| 23 |
+
```bibtex
|
| 24 |
+
@article{zbib2026taps,
|
| 25 |
+
title={TAPS: Task Aware Proposal Distributions for Speculative Sampling},
|
| 26 |
+
author={Zbib, Mohamad and Bazzi, Mohamad and Mohanna, Ammar and Ghanem, Bernard and Hammoud, Hasan Abed Al Kader},
|
| 27 |
+
year={2026},
|
| 28 |
+
note={Technical report}
|
| 29 |
+
}
|
| 30 |
+
```
|