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Internvl2 5 1B
InternVL2_5-1B
I
Internvl2 5 1B
AI ModelDevelopment Platform#Multimodal#Large Language Model#Image Recognition#Text Understanding#Machine LearningStandard PicksOpen Source
Overview :
InternVL 2.5 is a series of advanced multimodal large language models (MLLMs). Building on InternVL 2.0, it enhances training and testing strategies and improves data quality while maintaining its core model architecture. This model integrates the newly pre-trained InternViT with various pre-trained large language models (LLMs) such as InternLM 2.5 and Qwen 2.5, using a randomly initialized MLP projector. InternVL 2.5 supports multiple images and video data, employing a dynamic high-resolution training method to enhance its capability to handle multimodal data.
Target Users :
The target audience includes researchers, developers, and enterprises that need to process and understand extensive image and text data. InternVL2_5-1B offers a robust multimodal model applicable in various scenarios such as image recognition, text analysis, and cross-modal search.
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Use Cases
Using the InternVL2_5-1B model for joint understanding and reasoning tasks involving images and text.
In multi-image understanding tasks, leveraging the InternVL2_5-1B model to analyze and compare different image contents.
Applying the InternVL2_5-1B model for video content analysis to extract key information and events from videos.
Features
Supports dynamic high-resolution training methods for multimodal data, enhancing the model's ability to process multiple images and video data.
Utilizes a 'ViT-MLP-LLM' architecture, integrating visual encoders and language models, with cross-modal alignment via an MLP projector.
Offers a multi-stage training process, including MLP warm-up, incremental learning of the visual encoder, and full model instruction fine-tuning to optimize the model's multimodal capabilities.
Introduces a progressive expansion strategy that effectively aligns the visual encoder with large language models, reducing redundancy and enhancing training efficiency.
Applies random JPEG compression and loss reweighting techniques to improve model robustness against noisy images and balance different length responses in NTP loss.
Designs an efficient data filtering pipeline to eliminate low-quality samples, ensuring high data quality for model training.
How to Use
1. Install the necessary libraries, such as torch and transformers.
2. Load the InternVL2_5-1B model using AutoModel.from_pretrained.
3. Prepare the input data, including images and text, and preprocess the images.
4. Input the preprocessed images and text into the model to perform multimodal tasks.
5. Adjust model parameters as needed, such as the maximum number of new tokens and sampling strategies.
6. Obtain the model output and perform subsequent analysis or applications based on the output.
7. For tasks involving multiple rounds of dialogue or understanding multiple images, repeat steps 3-6, adjusting the input based on context.
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