MiniMax-Text-01

MiniMax-Text-01 Introduction

MiniMax-Text-01 is a large language model developed by MiniMaxAI, with 456 billion parameters, and each token activates 45.9 billion parameters. It adopts a hybrid architecture, combines lightning attention, softmax attention and expert hybrid technology, and utilizes advanced parallel strategies and computational communication overlapping methods (such as LASP+, variable length ring attention, expert tensor parallelism) to support millions of tokens training context length, and inference context length of up to 4 million tokens. The model performs well on multiple academic benchmarks.

target users

Natural language processing professionals, content creators, educators, and other developers, researchers, and enterprise users who need to process and generate long text content.

Usage scenarios

Develop intelligent writing assistants to quickly generate article reports, etc.

for natural language processing research such as language understanding and text generation

Build an intelligent customer service system to provide efficient and accurate customer support

Product features

Powerful language generation capabilities to generate high-quality text

Supports long context processing of 4 million tokens

Hybrid attention mechanism and expert mixing technology to improve performance and efficiency

Advanced parallel strategies and computational communication overlapping methods enable large-scale parameter training

Reach top model levels in academic benchmarks

Tutorial

1 Load the model configuration and tokenizer from the Hugging Face website

2 Set the quantization configuration, it is recommended to use int8 quantization

3 Set device mapping based on the number of devices

4 Load the word segmenter and preprocess the input text

5 Load the quantized model and move it to the specified device

6 Set the generation configuration, such as the maximum number of new tokens and the end token ID

7 Use the model to generate text and decode the ID to obtain the final text output