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Minimax 01
Overview :
MiniMax-01 is a robust language model with a total of 456 billion parameters, where each token activates 45.9 billion parameters. It employs a hybrid architecture that combines lightning attention, softmax attention, and mixture of experts (MoE). Through advanced parallel strategies and innovative computation-communication overlap methods, such as Linear Attention Sequence Parallelism (LASP+), variable-length ring attention, and expert tensor parallelism (ETP), it extends the training context length to 1 million tokens and can process contexts of up to 4 million tokens during inference. MiniMax-01 has demonstrated top-tier model performance across multiple academic benchmarks.
Target Users :
The target audience includes researchers, developers, and enterprises, particularly those needing to handle long texts and complex language tasks, such as natural language processing research, text generation, and intelligent customer service. For users seeking high performance and extensive context processing capabilities, MiniMax-01 is an ideal choice.
Use Cases
Used in natural language processing research to explore new language model architectures and algorithms.
Provides more accurate and natural language understanding and responses in enterprise intelligent customer service systems.
Generates high-quality news articles, storytelling content, and more in text generation applications.
Features
Utilizes a hybrid attention mechanism, combining lightning attention and softmax attention to enhance model performance.
Employs mixture of experts (MoE) technology to boost the model's expressive capabilities and flexibility.
Achieves efficient training of large-scale parameters through advanced parallel strategies and computation-communication overlap methods.
Supports context processing of up to 4 million tokens, ideal for handling long texts and complex tasks.
Excels in multiple academic benchmark tests, showcasing strong language understanding and generation capabilities.
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