InternVL2_5-1B-MPO
InternVL2_5-1B-MPO is a powerful multi-modal model for image and video understanding, excelling in tasks like image description and visual question answering.
English
English
What is InternVL2_5-1B-MPO?
InternVL2_5-1B-MPO is a sophisticated multimodal large language model (MLLM) built on InternVL2.5 and enhanced with Mixed Preference Optimization (MPO). This model integrates new incremental pre-training from InternViT with various pre-trained large language models like InternLM 2.5 and Qwen 2.5, using random initialization MLP projectors.
Key Features:
Supports Multimodal Data: Handles multiple images and video data.
Advanced Architecture: Uses 'ViT-MLP-LLM' paradigm, effectively combining visual and language information.
Enhanced Performance: Combines InternViT with different pre-trained LLMs.
Dynamic Resolution Handling: Can process image blocks up to 448x448 pixels.
Efficiency Improvements: Pixel reorganization reduces the number of visual tokens, enhancing efficiency.
Optimized Model Response: MPO optimizes the model by integrating preference loss, quality loss, and generation loss.
Ideal Users:
Target users include researchers, developers, and enterprises that need to process and understand large volumes of visual and language data. The advanced multimodal capabilities make it perfect for applications in image recognition, natural language processing, and machine learning.
Usage Examples:
Generate detailed descriptions of image sets.
Extract key information from video frames to create video summaries.
Answer specific questions based on visual content in visual question answering tasks.
Tutorial:
1. Install necessary libraries such as torch and transformers.
2. Load the model from Hugging Face using model = AutoModel.frompretrained('OpenGVLab/InternVL25-1B-MPO').
3. Prepare input data; if images are involved, preprocess them (resize and normalize).
4. Convert text to a format the model can understand using a tokenizer.
5. Input the processed images and text into the model for inference.
6. Post-process the output to get the final results.
7. For multi-image or video data, combine multiple image blocks or frames and provide additional context when inputting data.
