Awesome-Multi-Token-Prediction — security grade SAFE, quality 58/100

Security audit verdict: SAFE · quality 58/100

No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

by Xiaohao-Liu · LLM Plugin · ★ 191

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

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About Awesome-Multi-Token-Prediction

Awesome Multi-Token Prediction (MTP!) A curated list of papers, tools, and resources on Multi-Token Prediction (MTP) and related techniques in Large Language Models (LLMs), Speech-Language Models (SLMs), and more. Multi-Token Prediction (MTP) is an emerging paradigm that enhances the efficiency and capability of language and multimodal models by allowing them to predict multiple tokens simultaneously. This repository collects recent research and implementations in this exciting direction. 🔬 Recent Papers (2026)

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Quick Facts

Stars191
Forks10
CategoryLLM Plugin
Quality Score58.1779373034814/100
Last Updated2026-08-09
Created2025-06-20
Est. Tokens~17k

Compatible Skills

These tools work well together with Awesome-Multi-Token-Prediction for enhanced workflows:

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Frequently Asked Questions

What is Awesome-Multi-Token-Prediction?

Awesome-Multi-Token-Prediction is A curated list of papers, tools, and resources on Multi-Token Prediction (MTP) and related techniques in Large Language Models (LLMs), Speech-Language Models (SLMs), and more.. It is categorized as a LLM Plugin with 191 GitHub stars.

How do I install or use Awesome-Multi-Token-Prediction?

You can find installation instructions and usage details in the Awesome-Multi-Token-Prediction GitHub repository at github.com/Xiaohao-Liu/Awesome-Multi-Token-Prediction. The project has 191 stars and 10 forks, indicating an active community.

What are the best alternatives to Awesome-Multi-Token-Prediction?

The top alternatives to Awesome-Multi-Token-Prediction on Agent Skills Hub include LLM-VM, Awesome-LLMs-ICLR-24, hyper-mcp. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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