In April, we published a research paper on a new approach for building better and faster LLMs by using multi-token prediction. Using this approach, we can train language models to predict multiple future words at once, improving model capabilities and training efficiency while allowing for faster inference. In the spirit of responsible open science, we’ve released pre-trained models for code completion using this approach to enable further exploration in the research community. Get the model on Hugging Face ➡️ https://go.fb.me/dm1giu More on this approach ➡️ https://go.fb.me/x1zhdq
Multi-token prediction shows promise for improving efficiency and performance in language models, but managing complexity and resource demands, as well as ensuring consistent performance across varied datasets, may hinder widespread adoption.
Maintaining accuracy and efficiency is a 'precisive' technique, follows 'variable' methodology with 'angular' technology. Focus times on Complexity (Agile), Dependency (mitigates), Intensity (Resource management), ETL Data Processing (WLB) with relevance. Coordinative Management is effective tool with CI along with rule infused baseline targeting.
You are not the first one to use multi-tokens; I started earlier than April. I also use contextual tokens. See https://mltblog.com/4aHYM4i
Wow, are we witnessing another "Attention is all you need" moment?
An interesting approach. Keen to play around with it!
(Disclaimer: I haven't read the paper, yet.) Probably a provocative question: Any thoughts on why the paper was published in April and the model only released now?
It will be interesting to compare and contrast how this stands up against contextual tokens
Excellent work! Exciting news!
This is unique. Collaboration with a global AI community is more important than ever 🙏
LLM Expert & Data Scientist Specializing in Advanced LLM Applications, LLM Implementations and Scalable Data Solutions
1wI'm curious if their multi-token model not only outperforms their own baseline but also the top models of a similar size. It works well for generative tasks, but the paper indicates mixed results on multiple-choice question benchmarks. Also see https://arxiv.org/abs/2401.10774