InternVL2 2B

Table of Contents

Model Overview

The InternVL2-2B model is a powerful multimodal large language model that can process and understand both text and images. It’s part of the InternVL series, which includes models of various sizes, from 1 billion to 108 billion parameters.

Capabilities

The InternVL2-2B model is capable of handling a wide range of tasks, including:

• Document and chart comprehension: Understand and analyze documents and charts to answer questions and provide insights.
• Infographics QA: Answer questions based on infographics and visual data.
• Scene text understanding and OCR tasks: Recognize and understand text within images and videos.
• Scientific and mathematical problem solving: Solve complex scientific and mathematical problems.
• Cultural understanding and integrated multimodal capabilities: Understand and respond to cultural nuances and integrate multiple forms of input, such as text, images, and videos.

Strengths

The InternVL2-2B model has several strengths that make it a top-performing model in its class:

• Large context window: Trained with an 8k context window, allowing it to understand and respond to longer and more complex inputs.
• Multimodal capabilities: Can handle multiple forms of input, including text, images, and videos.
• Competitive performance: Outperforms many open-source models and is competitive with proprietary commercial models.

Unique Features

The InternVL2-2B model has several unique features that set it apart from other models:

• Instruction-tuned models: Optimized for multimodal tasks and fine-tuned on a wide range of instructions.
• Variety of model sizes: Available in several sizes, ranging from 1 billion to 108 billion parameters.
• Support for multiple GPUs: Can be run on multiple GPUs for faster inference and training.

Performance

The InternVL2-2B model showcases remarkable performance across various tasks, with a strong emphasis on multimodal capabilities.

• Speed: Handles large inputs with ease, processing long texts, multiple images, and videos efficiently.
• Accuracy: Achieves competitive performance on par with proprietary commercial models in various capabilities, including document and chart comprehension, infographics QA, scene text understanding and OCR tasks, scientific and mathematical problem-solving, and cultural understanding and integrated multimodal capabilities.

Limitations

While the InternVL2-2B model is a powerful tool, it’s not perfect. It may:

• Produce biased or discriminatory responses: Due to its size and probabilistic generation paradigm.
• Require careful evaluation and testing: To ensure it is used responsibly and safely.

Getting Started

To get started with the InternVL2-2B model, you can:

• Use the online demo: Experience the model’s capabilities firsthand.
• Run the model on your own hardware: Using the provided code and instructions.
• Evaluate the model: Using the provided evaluation guide and tools.

Format

The InternVL2-2B model is a multimodal large language model that accepts input in the form of text, images, and videos. It uses a transformer architecture and is designed for multimodal tasks.

• Architecture: Consists of three main components: InternViT-300M-448px, MLP projector, and internlm2-chat-1_8b.
• Supported Data Formats: Supports tokenized text sequences, 448x448 images in RGB format, and 16-frame videos with each frame resized to 448x448.
• Special Requirements: Input images and videos must be pre-processed using the dynamic_preprocess function to split them into smaller patches. Input text must be tokenized using the AutoTokenizer from the transformers library.