Chitrarth (Chitra: Image; Artha: Meaning) is a multilingual VLM that integrates a state-of-the-art multilingual Large Language Model (LLM) with a vision module. This model is trained primarily on multilingual image-text data and is designed to work across 10 prominent Indian languages, including Hindi, Bengali, Telugu, Tamil, Marathi, Gujarati, Kannada, Malayalam, Odia, and Assamese, as well as English
Performance against SOTA VLMs on different academic multimodal tasks. Our model consistently outperforms IDEFICS 2 (7B) and PALO 7B on different benchmarks while remaining competitive on TextVQA and Vizwiz.
We introduce BharatBench, a comprehensive evaluation benchmark suite designed for 10 under-resourced Indic languages across 3 tasks. The performance of Chitrarth on the BharatBench Evaluation framework sets a strong baseline for future research in this domain. Our model is unique in its ability to handle all included languages.
Below are the performance results of Chitrarth on BharatBench across three evaluation tasks: POPE, LLaVA-Bench, and MMVet.
