Internvl2 5 8B : A multimodal large language model supporting interaction understanding between images and text.

Internvl2 5 8B

AI Model Multimodal #Multimodal #Large Language Model #Image-Text-Text #Transformers #TensorBoard #Safetensors #Multilingual Standard Picks Open Source

Overview :

InternVL2_5-8B is a multimodal large language model (MLLM) developed by OpenGVLab, significantly enhanced with training and testing strategies as well as data quality improvements based on InternVL 2.0. This model employs the 'ViT-MLP-LLM' architecture, integrating the newly pre-trained InternViT with various pre-trained language models, such as InternLM 2.5 and Qwen 2.5, utilizing a randomly initialized MLP projector. The InternVL 2.5 series models demonstrate outstanding performance on multimodal tasks, including image and video understanding and multilingual comprehension.

Target Users :

The target audience includes researchers, developers, and enterprises, particularly professionals who require image and text interaction understanding and multimodal data analysis. InternVL2_5-8B, with its robust multimodal processing capabilities and efficient training strategies, is well-suited for users seeking innovative applications in image recognition, natural language processing, and machine learning.

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Use Cases

- Using InternVL2_5-8B for image description and image question answering.

- Utilizing the model for multilingual image annotation and classification.

- Applying the model to understand and analyze video content.

Features

- Dynamic high-resolution multimodal data processing: capable of handling single images, multiple images, and video datasets.

- Unified model training pipeline: includes three stages of MLP warm-up, ViT incremental learning, and full-model instruction tuning.

- Progressive scaling strategy: involves training on a smaller LLM first before transferring the visual encoder to a larger LLM without retraining.

- Training enhancement techniques: including random JPEG compression and loss re-weighting methods to improve the model's robustness against noisy images.

- Data organization and filtering: controlled by parameters to organize training data and design efficient data filtering pipelines to remove low-quality samples.

- Multimodal capability evaluation: assessed on various aspects of multimodal reasoning, mathematics, OCR, chart and document understanding.

- Language capability evaluation: maintained pure language performance by collecting more high-quality open-source data while filtering out low-quality data.

How to Use

1. Install necessary libraries such as torch and transformers.

2. Load the model and tokenizer from Hugging Face.

3. Prepare input data, including images and text.

4. Preprocess the images by resizing and converting them into the format required by the model.

5. Use the model for inference to obtain interaction understanding results for images and text.

6. Analyze and apply the model's output, such as automatic image annotation or question-answering systems.