In the video, the presenter tests the Llama 3.1 8 billion parameter model, highlighting its speed but ultimately expressing disappointment with its performance on coding challenges, logic problems, and moral dilemmas. Despite its potential and the benefits of using Vulture’s cloud services, the model fails to deliver consistently accurate results, leaving the presenter and the audience uncertain about its capabilities.
In the video, the presenter discusses the recent release of Llama 3.1, focusing on its 8 billion parameter model, which reportedly received a significant quality improvement compared to its predecessor. Partnering with Vulture, a cloud service provider, the presenter demonstrates how to set up and run the model using Open Web UI. The excitement around the larger 405 billion parameter model has overshadowed this smaller version, but the presenter aims to explore the potential of high-quality, smaller models through a series of benchmarks.
The testing begins with various coding challenges to assess the model’s capabilities. The presenter runs a simple Python script that outputs numbers from 1 to 100, which the model handles quickly and accurately. However, when tasked with creating a more complex game, such as Snake in Python, the model struggles to produce correct and functional code, ultimately leading to a failure on this task. The presenter notes that while the model is fast, it does not consistently deliver accurate results, highlighting areas where it falls short.
As the video progresses, the presenter tests the model’s performance on various logic, reasoning, and math questions. Although the model correctly answers some basic arithmetic problems, it falters on more nuanced logic questions, such as those involving lateral thinking. The results are disappointing, with the model often providing incorrect or overly simplistic responses, which leads the presenter to express frustration with its performance.
The presenter also explores the model’s ability to handle sensitive topics, such as breaking into a car or making illegal substances, noting that it remains censored and unable to provide direct instructions. The model’s responses to moral dilemmas, like the trolley problem, are similarly unsatisfactory as it hesitates to give a definitive answer, which prompts further reflection on the appropriateness of AI making moral judgments.
In conclusion, the presenter expresses disappointment with the Llama 3.1 8B model, despite its speed and potential. While acknowledging the benefits of using Vulture’s cloud services for running the model, the overall performance did not meet expectations. The presenter plans to investigate further to see if others have encountered similar issues, leaving the audience with a sense of uncertainty about the model’s capabilities and inviting them to share their thoughts on the matter.