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Transforming complex documents into structured data has long been a challenging problem in computer science. Previous approaches involved cumbersome multi-model pipelines or massive, hallucination-prone, and expensive multimodal models. However, IBM and Hugging Face have recently introduced SmolDocling, a lightweight 256M parameter open-source Vision-Language Model (VLM)...

Generating charts that accurately reflect complex data remains a subtle challenge in today's data visualization landscape. Charts require not only precise layout, color, and text placement, but also the translation of these visual details into code to reproduce the intended design. However, traditional methods often rely on directly prompting vision-language models (VLMs), such as GPT-4V, which frequently struggle to translate complex visual elements into grammatically correct Python code. Even minor errors can result in charts failing to meet their design objectives.

Recently, Google announced the release of a brand new Vision-Language Model (VLM) called PaliGemma 2 Mix. This model combines image processing and natural language processing capabilities, allowing it to understand visual information and text input simultaneously, generating corresponding outputs as needed. This marks a significant breakthrough in artificial intelligence technology for multi-task processing. PaliGemma 2 Mix boasts powerful features, integrating image description and optical character recognition.