AI is happening. The future is updating.MiniMax-VL-01
We are delighted to introduce our **MiniMax-VL-01** model. It adopts the "ViT-MLP-LLM" framework, which is a commonly used technique in the field of multimodal large language models. The model is initialized and trained with three key parts: a 303-million-parameter Vision Transformer (ViT) for visual encoding, a randomly initialized two-layer MLP projector for image adaptation, and the MiniMax-Text-01 as the base LLM. MiniMax-VL-01 has a notable dynamic resolution feature. Input images are resized per a pre-set grid, with resolutions from 336Γ336 to 2016Γ2016, keeping a 336Γ336 thumbnail. The resized images are split into non-overlapping patches of the same size. These patches and the thumbnail are encoded separately and then combined for a full image representation. The training data for MiniMax-VL-01 consists of caption, description, and instruction data. The Vision Transformer (ViT) is trained on 694 million image-caption pairs from scratch. Across four distinct stages of the training pipeline, a total of 512 billion tokens are processed, leveraging this vast amount of data to endow the model with strong capabilities. Finally, MiniMax-VL-01 has reached top-level performance on multimodal leaderboards, demonstrating its edge and dependability in complex multimodal tasks. For production deployment, we recommend using vLLM to serve MiniMax-VL-01. vLLM provides excellent performance for serving large language models with the following features: π₯ Outstanding service throughput performance β‘ Efficient and intelligent memory management π¦ Powerful batch request processing capability βοΈ Deeply optimized underlying performance For detailed deployment instructions, please refer to our vLLM Deployment Guide. MiniMax-VL-01 supports Function Calling capability, enabling the model to intelligently identify when external functions need to be called and output parameters in structured JSON format. With Function Calling, you can:
We are delighted to introduce our **MiniMax-VL-01** model. It adopts the "ViT-MLP-LLM" framework, which is a commonly used technique in the field of multimodal large language models. The model is initialized and trained with three key parts: a 303-million-parameter Vision Transformer (ViT) for visual encoding, a randomly initialized two-layer MLP projector for image adaptation, and the MiniMax-Text-01 as the base LLM. MiniMax-VL-01 has a notable dynamic resolution feature. Input images are resized per a pre-set grid, with resolutions from 336Γ336 to 2016Γ2016, keeping a 336Γ336 thumbnail. The resized images are split into non-overlapping patches of the same size. These patches and the thumbnail are encoded separately and then combined for a full image representation. The training data for MiniMax-VL-01 consists of caption, description, and instruction data. The Vision Transformer (ViT) is trained on 694 million image-caption pairs from scratch. Across four distinct stages of the training pipeline, a total of 512 billion tokens are processed, leveraging this vast amount of data to endow the model with strong capabilities. Finally, MiniMax-VL-01 has reached top-level performance on multimodal leaderboards, demonstrating its edge and dependability in complex multimodal tasks. For production deployment, we recommend using vLLM to serve MiniMax-VL-01. vLLM provides excellent performance for serving large language models with the following features: π₯ Outstanding service throughput performance β‘ Efficient and intelligent memory management π¦ Powerful batch request processing capability βοΈ Deeply optimized underlying performance For detailed deployment instructions, please refer to our vLLM Deployment Guide. MiniMax-VL-01 supports Function Calling capability, enabling the model to intelligently identify when external functions need to be called and output parameters in structured JSON format. With Function Calling, you can: