multimodal

InternVL2_5-8B-MPO-AWQ is a multi-modal large-scale language model launched by OpenGVLab. It is based on the InternVL2.5 series and uses Mixed Preference Optimization (MPO) technology. The model demonstrates excellent performance in visual and language understanding and generation, especially in multi-modal tasks. It achieves in-depth understanding and interaction of images and text by combining the visual part InternViT and the language part InternLM or Qwen, using randomly initialized MLP projectors for incremental pre-training. The importance of this technology lies in its ability to process multiple data types including single images, multiple images, and video data, providing new solutions in the field of multi-modal artificial intelligence.

InternVL2.5-MPO is an advanced multi-modal large-scale language model series built on InternVL2.5 and hybrid preference optimization. The model integrates the newly incrementally pretrained InternViT with various pretrained large language models, including InternLM 2.5 and Qwen 2.5, using randomly initialized MLP projectors. InternVL2.5-MPO retains the same model architecture as InternVL 2.5 and its predecessor in the new version, following the "ViT-MLP-LLM" paradigm. The model supports multiple image and video data, and further improves model performance through Mixed Preference Optimization (MPO), making it perform better in multi-modal tasks.

InternVL2_5-2B-MPO is a family of multi-modal large-scale language models that demonstrates excellent overall performance. The series is built on InternVL2.5 and hybrid preference optimization. It integrates the newly incrementally pretrained InternViT with various pretrained large language models, including InternLM 2.5 and Qwen 2.5, using randomly initialized MLP projectors. The model performs well in multi-modal tasks and is able to handle a variety of data types including images and text, making it suitable for scenarios that require understanding and generating multi-modal content.

InternVL2_5-4B is an advanced multi-modal large language model (MLLM) that maintains the core model architecture based on InternVL 2.0 and has significant enhancements in training and testing strategies and data quality. The model performs well in processing image, text-to-text tasks, especially in multi-modal reasoning, mathematical problem solving, OCR, diagrams, and document understanding. As an open source model, it provides researchers and developers with powerful tools to explore and build vision- and language-based intelligent applications.

InternVL2_5-8B is a multi-modal large language model (MLLM) developed by OpenGVLab. It has significant training and testing strategy enhancements and data quality improvements based on InternVL 2.0. The model adopts the 'ViT-MLP-LLM' architecture, which integrates the new incremental pre-trained InternViT with multiple pre-trained language models, such as InternLM 2.5 and Qwen 2.5, using a randomly initialized MLP projector. InternVL 2.5 series models demonstrate excellent performance on multi-modal tasks, including image and video understanding, multi-language understanding, etc.