The video introduces Pixol 12B, an open-source multimodal vision model by Mistral AI, highlighting its impressive performance in vision tasks and its ability to process both images and text with a 12 billion parameter architecture. While it excels in visual recognition and description, the model shows limitations in logic and reasoning tasks, suggesting a potential future for specialized AI models tailored to specific functions.
In the video, the host introduces Pixol 12B, a new open-source multimodal vision model developed by Mistral AI. The model is notable for its ability to process both images and text, boasting a 12 billion parameter architecture. The host explains that Pixol is designed to excel in multimodal tasks, instruction following, and has shown strong performance in text-only benchmarks. The model is hosted on Vulture, a cloud service that provides easy access to powerful GPUs, which the host praises for its simplicity and efficiency.
The video begins with a brief overview of the model’s capabilities and the initial announcement, which lacked detailed information. After downloading and testing Pixol, the host highlights its strengths, particularly in vision-related tasks. The model supports variable image sizes and can handle a long context window of up to 128,000 tokens, making it versatile for various applications. The host also compares Pixol’s performance against other models in a benchmark chart, indicating that it outperforms many competitors.
The host conducts several tests to evaluate Pixol’s performance, starting with a coding challenge to write a Tetris game in Python. While the model struggles with this task, it performs well in simpler logic questions, demonstrating its limitations in reasoning. The focus then shifts to vision tasks, where Pixol excels. For instance, when asked to describe an image of a llama, the model provides a detailed and accurate description almost instantaneously.
Further tests include identifying a celebrity in a photo, solving a CAPTCHA, and analyzing a screenshot of the host’s iPhone storage. Pixol successfully identifies Bill Gates and accurately answers questions about the storage usage on the phone. However, it falters when asked to determine which app is not downloaded, showcasing some limitations in understanding visual cues. Overall, the model impresses with its speed and accuracy in vision-related tasks, outperforming other models in similar tests.
In conclusion, the host expresses enthusiasm for Pixol’s capabilities, particularly in vision tasks, while acknowledging its shortcomings in logic and reasoning. They suggest that the future of AI models may involve using specialized models for different tasks, such as Pixol for vision and other models for logic or complex queries. The video wraps up with a reminder of Vulture’s sponsorship and an invitation for viewers to try out the service, along with a call to like and subscribe for more content.