Meta Llama 3 vision multimodal models – how to use them and what they can do

Meta has introduced multimodal capabilities to its Llama 3 model, allowing it to process both images and text. This advancement enables the model to analyze visual content and respond to queries about it, such as generating keywords or extracting information from images. However, initial tests reveal significant limitations in the model's reasoning and understanding abilities. For instance, when tasked with analyzing charts, Llama 3 often produced incorrect conclusions and figures, indicating a lack of comprehension. Despite these shortcomings, the model performed well in tasks like image recognition and sentiment analysis, accurately identifying objects and evaluating emotional states based on facial expressions. The Llama 3 model is available in two sizes, 11 billion and 90 billion parameters, with the latter potentially offering improved performance. While Meta's foray into multimodal AI is noteworthy, the model's current capabilities suggest it still requires further development to achieve a higher level of reasoning akin to human understanding.

- Meta's Llama 3 model now supports multimodal inputs, combining text and images.

- Initial tests show significant limitations in the model's ability to analyze and interpret visual data accurately.

- The model excels in image recognition and sentiment analysis tasks.

- Llama 3 is available in two variants, with the larger model expected to perform better.

- The development highlights the ongoing challenges in achieving advanced reasoning in AI models.