What is MiniMax-VL-01, and what are its capabilities?

MiniMax-VL-01 is a vision-language model built on MiniMax-Text-01, utilizing a ViT-MLP-LLM framework. It excels in multimodal tasks thanks to its dynamic resolution mechanism for image processing.

What are the key features of MiniMax-Text-01?

MiniMax-Text-01 boasts 456 billion parameters, a hybrid attention architecture (Lightning, Softmax, MoE), and a training context length of 1 million tokens, enabling high performance on text-based tasks.

MiniMax-01 provides access to large language and vision-language models, including MiniMax-Text-01 and MiniMax-VL-01, designed for various natural language and multimodal tasks.

Where can I find the license information for the MiniMax-01 models and code?

The model license agreement can be found at [Model License Link], and the code license (MIT) is available at [Code License Link] on the GitHub repository.

MiniMax-01

Name: MiniMax-01
Author: MiniMax-AI

byMiniMax-AI

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3.4k

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API Development

Developer Tools

Data Science & ML

Provides access to large language and vision-language models based on linear attention for various natural language and multimodal tasks.

MiniMax-01

byMiniMax-AI

•

3.4k

•

API Development

Developer Tools

Data Science & ML

Provides access to large language and vision-language models based on linear attention for various natural language and multimodal tasks.

MiniMax-01 is the official repository for MiniMax-Text-01, a large language model with 456 billion parameters, and MiniMax-VL-01, a vision-language model leveraging a ViT-MLP-LLM framework. MiniMax-Text-01 achieves long-context capabilities through a hybrid architecture integrating Lightning Attention, Softmax Attention, and Mixture-of-Experts (MoE), enabling it to handle up to 4 million tokens during inference. MiniMax-VL-01 extends this with enhanced visual capabilities and dynamic resolution mechanisms, achieving top-level performance on multimodal leaderboards.

Key Features

01Vision-language model with dynamic resolution mechanism

022,495 GitHub stars

03Hybrid attention architecture (Lightning Attention, Softmax Attention, MoE)

04Training context length of 1 million tokens, inference up to 4 million tokens

05Large language model with 456 billion parameters

06ViT-MLP-LLM framework for multimodal capabilities

Use Cases

01Academic research and benchmarking of large language models

02Development of applications requiring long-context understanding

03Multimodal task solving using vision and language

MiniMax-01

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MiniMax-01

Key Features

Use Cases

Neon

Firecrawl

Figma Context

Diagram Maker & Visualizer

GH Issues Auto-Fixer

Discord Integration

Key Features

Use Cases