InternVL2_5-4B

A multimodal large language model that integrates visual and language understanding.

CommonProductImageMultimodalLarge Language Model

InternVL2_5-4B is an advanced multimodal large language model (MLLM) that maintains the core model architecture of InternVL 2.0 while significantly enhancing training and testing strategies and data quality. The model excels in handling tasks from image and text to text, particularly in multimodal reasoning, mathematical problem solving, OCR, and chart and document comprehension. As an open-source model, it provides researchers and developers with powerful tools to explore and build intelligent applications based on visual and linguistic elements.

- Supports multimodal data: capable of processing composite data types that include both images and text.
- Dynamic high-resolution training: able to dynamically adjust image resolution for optimal performance on multimodal datasets.
- Single model training pipeline: enhances the model's visual perception and multimodal capabilities through a three-phase training process.
- Progressive scaling strategy: improves training efficiency by initially training on a smaller LLM and then transferring the visual encoder to a larger LLM.
- Training enhancement techniques: includes random JPEG compression and loss re-weighting to improve the model's robustness against noisy images.
- Data organization and filtering: optimizes the balance and distribution of training data through meticulous organization and filtering techniques.
- Multilingual support: enables understanding across multiple languages
broadening application scenarios.

\The target audience includes researchers
developers
and enterprises
particularly teams that need to build or enhance intelligent applications combining visual and linguistic elements. The multimodal capabilities provided by InternVL2_5-4B make it an ideal choice for developing applications such as image recognition
automated tagging

In the education sector
InternVL2_5-4B can be used to develop teaching aids that help students better understand complex concepts through image and text understanding.
In e-commerce
the model can enhance user experience by utilizing image search and recommendation systems
interpreting product images and descriptions.

1. Install necessary libraries such as torch and transformers.
2. Load the InternVL2_5-4B model using AutoModel.from_pretrained.
3. Prepare input data
including images and text
ensuring they meet the model's input requirements.

Visit

InternVL2_5-4B Visit Over Time

Monthly Visits

20899836

Bounce Rate

46.04%

Page per Visit

5.2

Visit Duration

00:04:57

InternVL2_5-4B Visit Trend

InternVL2_5-4B Visit Geography

InternVL2_5-4B Traffic Sources

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InternVL2_5-4B

InternVL2_5-4B Visit Over Time

InternVL2_5-4B Visit Trend

InternVL2_5-4B Visit Geography

InternVL2_5-4B Traffic Sources

InternVL2_5-4B Alternatives

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InternVL 2.5 — Open-source multimodal large language model series

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