TencentBAC/FastMTP
FastMTP: Accelerating LLM Inference with Enhanced Multi-Token Prediction
<p align="left"> <strong>Technical report (coming soon)</strong> · <a href="https://github.com/Tencent-BAC/FastMTP"><strong>Github</strong></a> · <a href="https://huggingface.co/TencentBAC/FastMTP"><strong>HuggingFace</strong></a> · <a href="https://modelscope.cn/models/TencentBAC/FastMTP"><strong>ModelScope</strong></a> </p>
Overview
FastMTP is a simple yet effective method that enhances Multi-Token Prediction (MTP) for speculative decoding during inference. Our approach fine-tunes a single MTP head with shared weights across multiple causal draft steps, enabling it to capture longer-range dependencies and achieve higher acceptance rates in speculative decoding. By incorporating language-aware vocabulary compression, we further reduce computational overhead during draft generation. Experimental results across diverse benchmarks demonstrate that FastMTP achieves an average of 2.03× speedup over vanilla next token prediction while maintaining lossless output quality. With low training cost and seamless integration into existing inference frameworks, FastMTP offers a practical and rapidly deployable solution for accelerating LLM inference.
<!-- {width=50%} --> <img src="./assets/mtp-overview.png" width="75%">
Speedup comparison of different methods across subtasks, evaluated on a single A10 GPU:
<img src="./assets/radar_chart.png" width="55%">
What's Included
This repository contains the model checkpoints for FastMTP, and the processed compressed vocabulary.
Links
- Technical report (coming soon)
- Training & inference code: GitHub Repository