InternVL 1.5 is an innovative upgrade to the open-source multimodal suite aimed at bridging the gap between proprietary models and open-source alternatives in terms of multimodal understanding. This model incorporates three pivotal improvements:

• Strong Vision Encoder: Continuous learning strategy bolsters the large-scale vision foundation model – InternViT-6B, enhancing visual understanding which is transferable across various LLMs.
• Dynamic High-Resolution: Allows image division into tiles with sizes up to 448x448 pixels, facilitating up to 4K resolution support depending on image aspect ratio and resolution.
• High-Quality Bilingual Dataset: Offers a rich bilingual dataset covering common scenes and document images with annotations in English and Chinese for enhanced OCR and Chinese-related tasks performance.

Here’s why this model is crucial:

• Benchmarks and Comparative Studies: InternVL 1.5 is validated against both open-source and proprietary models, recording state-of-the-art results in 8 out of 18 benchmarks, demonstrating its robust capability in practical applications.

• Open-source Availability: The release of this model on Github not only promotes transparency but also encourages community contributions to further enhancements.

Further Research and Applications

• Cross-Model Compatibility: Future research could explore the integration of InternVL with different modalities, further enhancing its applicability across various domains.
• Enhanced Multilingual Support: Expansion of language support could significantly multiply its usability in global scenarios, benefiting a wider array of users.