yukiwayx commited on
Commit
92ae4d5
·
verified ·
1 Parent(s): f3883ee

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +1 -1
README.md CHANGED
@@ -10,10 +10,10 @@ language:
10
  <p align="left">
11
  <strong>Technical report (coming soon)</strong> ·
12
  <a href="https://github.com/Tencent-BAC/FastMTP"><strong>Github</strong></a> ·
 
13
  <a href="https://modelscope.cn/models/TencentBAC/FastMTP"><strong>ModelScope</strong></a>
14
  </p>
15
 
16
-
17
  ## Overview
18
 
19
  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.
 
10
  <p align="left">
11
  <strong>Technical report (coming soon)</strong> ·
12
  <a href="https://github.com/Tencent-BAC/FastMTP"><strong>Github</strong></a> ·
13
+ <a href="https://huggingface.co/TencentBAC/FastMTP"><strong>HuggingFace</strong></a> ·
14
  <a href="https://modelscope.cn/models/TencentBAC/FastMTP"><strong>ModelScope</strong></a>
15
  </p>
16
 
 
17
  ## Overview
18
 
19
  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.