Mini-InternVL-Chat-4B-V1-5

Model overview

Mini-InternVL-Chat-4B-V1-5 is a multimodal large language model (MLLM) developed by OpenGVLab. It is part of the Mini-InternVL-Chat series, which aims to create smaller yet high-performing multimodal models. The model uses the InternViT-300M-448px vision model and either the InternLM2-Chat-1.8B or Phi-3-mini-128k-instruct language model, resulting in a 4.2B parameter model. This smaller model maintains excellent performance while reducing the computational requirements compared to the larger InternVL 1.5 model.

Model inputs and outputs

Inputs

• Images: The model accepts dynamic resolution images, up to a maximum of 40 tiles of 448 x 448 pixels (4K resolution).

Outputs

• Multimodal responses: The model generates text responses based on the input image and any additional context provided.

Capabilities

Mini-InternVL-Chat-4B-V1-5 is capable of understanding and generating multimodal responses, combining visual and linguistic information. It can be used for a variety of tasks, such as image captioning, visual question answering, and multimodal dialog.

What can I use it for?

The Mini-InternVL-Chat-4B-V1-5 model can be used in a wide range of applications that require multimodal understanding and generation, such as:

• Interactive chatbots that can understand and respond to images
• Assistants that can provide detailed captions and explanations for images
• Visual question answering systems that can answer questions about the content of an image

Things to try

With Mini-InternVL-Chat-4B-V1-5, you can experiment with various multimodal tasks, such as:

• Generating creative image captions that go beyond simple descriptions
• Engaging in open-ended conversations about images, exploring the model's reasoning and understanding
• Combining the model's visual and language understanding to tackle complex multimodal tasks, such as visual reasoning or multimodal story generation.