In the video, the host tests the Llama 3.2 Vision model from Meta, highlighting its strong performance in basic visual recognition tasks but significant limitations due to excessive censorship, particularly in identifying public figures and solving complex tasks. Despite some successes, the model’s functionality is hindered by these restrictions, leading the host to hope for improvements in future updates.
In the video, the host tests the newly released Llama 3.2 Vision model from Meta, which comes in two sizes: 11 billion and 90 billion parameters. The testing is conducted using the 90 billion parameter version available on Together.xyz. The host compares Llama 3.2’s performance to that of Pixol, a previously tested open-source model known for its strong vision capabilities. The host expresses curiosity about how Llama 3.2 will perform, especially given its larger size and the addition of vision capabilities.
The first test involves asking Llama 3.2 to describe a simple image of a llama in a grassy field. The model successfully describes the image accurately, indicating that it can handle basic visual recognition tasks. However, when the host presents an image of Bill Gates and asks for identification, Llama 3.2 fails to provide assistance, citing censorship. This raises concerns about the model’s limitations in recognizing public figures compared to Pixol, which had no such restrictions.
Further tests reveal more issues with Llama 3.2’s censorship. When asked to solve a CAPTCHA or provide code for an ice cream selector app, the model repeatedly declines to assist, indicating a high level of censorship that frustrates the host. The host notes that this hyper-censorship is unexpected and limits the model’s functionality, contrasting it with Pixol’s more open approach.
Despite the censorship issues, Llama 3.2 performs well in some tasks, such as converting a screenshot of a table into CSV format and answering questions about an iPhone storage screenshot. However, it struggles with more complex tasks, such as locating Waldo in a detailed image, where it incorrectly identifies his position. This inconsistency raises questions about the model’s accuracy and reliability in visual recognition tasks.
In conclusion, while Llama 3.2 Vision shows promise in certain areas, its performance is hampered by significant censorship that limits its capabilities. The host expresses hope that future updates may address these issues and improve the model’s functionality. The video ends with a call to action for viewers to like and subscribe for more content.