Llama 3.2 VISION Tested - Shockingly Censored! 🤬

1. Llama 3.2 Vision performs well on basic image descriptions.

🥈85 01:04

The model successfully described simple images, demonstrating its capability in basic visual recognition tasks.

• It accurately described a llama in a grassy field, showcasing its ability to process straightforward images.
• The speed of response was noted as a positive aspect during testing.
• This indicates that while it has censorship issues, it retains some functional strengths.

2. Llama 3.2 Vision shows significant censorship in its responses.

🥇92 01:44

The model fails to identify people in images and solve simple captchas, indicating a high level of censorship compared to previous models.

• When asked to identify a celebrity, Llama 3.2 refused to assist, unlike its predecessor Pixol.
• Attempts to solve a captcha were met with similar refusals, showcasing its limitations.
• The model's responses suggest a focus on preventing inappropriate content access.

3. Llama 3.2 Vision's performance is inconsistent.

🥉78 05:18

While it succeeded in some tasks, its failures in others highlight inconsistency in performance.

• The model accurately converted a table to CSV but failed to provide correct storage information from an iPhone screenshot.
• This inconsistency may affect user trust and application in real-world scenarios.
• Further testing may be needed to fully understand its capabilities and limitations.

4. Complex tasks reveal Llama 3.2's limitations.

🥈80 06:46

The model struggled with more complex tasks, such as identifying Waldo in a detailed image.

• It incorrectly located Waldo, indicating potential issues with detailed image analysis.
• This suggests that while it can handle basic tasks, it may falter under more challenging scenarios.
• The performance raises questions about its overall reliability for intricate visual tasks.