MiniMax-M1是开源大规模混合注意力推理模型，采用混合专家模型（MoE）架构。
模型架构
-
混合专家模型（MoE）：MiniMax-M1采用了混合专家架构，结合了闪电注意力机制。这种设计使得模型在处理复杂任务时具有更高的效率和灵活性。
-
参数量：该模型的总参数量达到4560亿，其中每个token激活参数为45.9亿。
上下文处理能力
- 超长上下文支持：MiniMax-M1原生支持高达100万token的上下文长度，这一特性使其在处理长文本输入时表现优异，达到DeepSeek R1的8倍。
计算效率
- 高效推理：在生成10万token文本时，MiniMax-M1的浮点运算量仅为DeepSeek R1的25%，显著提升了推理时的计算效率。
训练与优化
-
强化学习训练：模型通过大规模强化学习（RL）进行训练，涵盖了多种复杂问题，包括传统数学推理和真实世界的软件工程环境。
-
CISPO算法：MiniMax-M1引入了一种名为CISPO的创新算法，通过裁剪重要性采样权重而非token更新来优化训练效率。
版本与适用性
-
多版本支持：MiniMax-M1提供了40K和80K两种思维预算版本，适应不同的应用需求。
开源特性
- 开放性：作为一款开源模型，MiniMax-M1允许开发者根据自身需求进行定制化调整，促进了技术创新和知识共享。
应用场景
-
长文本处理：MiniMax-M1支持高达100万token的上下文长度，特别适合需要处理长输入的任务，如文档分析、法律文本解读等。
-
复杂推理任务：该模型在数学推理、逻辑推理和软件工程等领域表现出色，能够处理复杂的推理问题。
-
工具调用：MiniMax-M1支持结构化功能调用，能够识别并输出外部函数的调用参数，适用于需要与其他软件或API集成的场景。
-
聊天机器人与API：模型提供在线搜索的聊天机器人及API，支持视频生成、图像生成和语音合成等功能，适合开发智能助手和多媒体应用。
-
教育与研究：在教育领域，MiniMax-M1可以帮助学生进行复杂作业的分析和总结，提供深入的研究支持。
-
创意写作：该模型能够为作家和创意工作者提供灵感和编辑建议，帮助他们在写作过程中进行多层次的分析。
-
数据提取与总结：MiniMax-M1具备精确的信息提取能力，适用于会议纪要、摘要提取等场景，能够快速生成关键信息和总结。
MiniMax-M1 is an open-source large-scale hybrid attention reasoning model based on a Mixture of Experts (MoE) architecture.
Model Architecture
-
Mixture of Experts (MoE): MiniMax-M1 adopts a Mixture of Experts architecture combined with a Flash Attention mechanism. This design enables higher efficiency and flexibility in handling complex tasks.
-
Parameter Count: The model has a total of 456 billion parameters, with 4.59 billion active parameters per token.
Context Handling Capability
-
Ultra-Long Context Support: MiniMax-M1 natively supports up to 1 million tokens in context length, making it excellent at processing long text inputs—eight times that of DeepSeek R1.
Computational Efficiency
-
Efficient Inference: When generating 100,000 tokens of text, MiniMax-M1 requires only 25% of the floating-point operations compared to DeepSeek R1, significantly improving inference efficiency.
Training and Optimization
-
Reinforcement Learning Training: The model is trained using large-scale reinforcement learning (RL), covering a wide range of complex problems, including traditional mathematical reasoning and real-world software engineering environments.
-
CISPO Algorithm: MiniMax-M1 introduces an innovative algorithm called CISPO, which optimizes training efficiency by pruning importance sampling weights instead of token updates.
Versions and Applicability
-
Multiple Versions: MiniMax-M1 offers 40K and 80K thinking budget versions to suit different application needs.
Open-Source Features
-
Openness: As an open-source model, MiniMax-M1 allows developers to customize it according to their needs, promoting technological innovation and knowledge sharing.
Application Scenarios
-
Long Text Processing: With support for up to 1 million tokens, MiniMax-M1 is ideal for tasks that require handling long inputs, such as document analysis and legal text interpretation.
-
Complex Reasoning Tasks: The model excels in mathematical reasoning, logical reasoning, and software engineering, capable of handling intricate reasoning problems.
-
Tool Use: MiniMax-M1 supports structured function calling, capable of recognizing and outputting external function call parameters, making it suitable for scenarios requiring integration with other software or APIs.
-
Chatbots and APIs: The model provides chatbots with online search and APIs that support video generation, image creation, and speech synthesis—ideal for developing intelligent assistants and multimedia applications.
-
Education and Research: In the education sector, MiniMax-M1 can assist students with complex assignment analysis and summaries, offering in-depth research support.
-
Creative Writing: The model can offer inspiration and editorial suggestions for writers and creatives, aiding multi-layered analysis during the writing process.
-
Data Extraction and Summarization: MiniMax-M1 has accurate information extraction capabilities, making it suitable for meeting minutes and summary generation tasks, quickly producing key insights and overviews.
