Multi-Token Prediction

A multi-token prediction model designed to boost the efficiency and performance of language models

CommonProductProgrammingLanguage ModelMulti-Token Prediction

Multi-token prediction is a technology developed by Facebook based on large language model research. It aims to improve model efficiency and performance by predicting multiple future tokens. This technique enables the model to generate multiple tokens in a single forward pass, thereby accelerating generation speed and potentially enhancing model accuracy. The model is freely available for non-commercial research use, but usage is subject to Meta's privacy policies and applicable laws and regulations.

Supports training and inference of language models for non-commercial research purposes
Provides a code-based 7B parameter model trained on 200B and 1T tokens
Compatible with Llama-formatted Pytorch state dictionaries
facilitating model deployment and usage
Supports additional prediction heads to bypass standard autoregressive inference
Provides a Tokenizer for handling model input
Adheres to the LLaMA Acceptable Use Policy
ensuring responsible use of the material

This model is targeted towards researchers and developers who leverage advanced language models to advance natural language processing research and application development. It is particularly suitable for researchers and developers seeking to enhance model generation speed and efficiency while maintaining or improving generation quality.

Researchers utilized this model to enhance prediction speed in natural language understanding tasks.
Developers leveraged the model for code generation
accelerating the development process.
Educational institutions employed this technology to demonstrate advanced language model applications to students in their teachings.

1. Install the necessary dependency libraries
including torch
fairscale
fire
and sentencepiece.

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