The video highlights the qw q 32b AI model's efficiency and reasoning capabilities using an elevator logic task, demonstrating its comparative strengths against Gemini 2.0.
In this video, the presenter introduces the new AI model qw q 32b, which utilizes 24 GB of VRAM compared to its predecessor, the deep seek all1 model, which demands an impressive 1.5 terabytes. This unique AI model is praised for its exceptional stability and impressive capabilities in reasoning tasks. The presenter demonstrates the model's performance by executing a series of tests using an elevator logic example. The elevator logic involves navigating floors with specific button presses, highlighting qw q 32b's capacity to carry out complex reasoning. Each button press interacts differently depending on functions associated with prime numbers and trap floors, showcasing the model’s decision-making process and self-correction ability through logical argumentation. In a comparative analysis with Gemini 2.0, the presenter illustrates the speed improvements when utilizing appropriate tools like Python for programming tasks.
Content rate: B
The content provides substantial insights into the capabilities and performance of qw q 32b and its comparative analysis with Gemini 2.0. However, while grounded in demonstrated results, some claims would benefit from more robust data to fully substantiate them.
AI model reasoning performance technology
Claims:
Claim: The qw q 32b model uses significantly less VRAM than the deep seek all1 model, which requires 1.5 TB.
Evidence: The video specifies that qw q 32b uses 24 GB of VRAM, a stark contrast to the 1.5 TB required by the deep seek all1 model.
Counter evidence: No opposing evidence was provided that contests the VRAM specifications for the qw q 32b and deep seek all1 models.
Claim rating: 9 / 10
Claim: qw q 32b demonstrated impressive stability and reasoning capabilities during the elevator logic test.
Evidence: The presenter remarked on qw q 32b's logical reasoning during the elevator task, noting its detailed arguments and eventual self-correction, leading to valid button press solutions.
Counter evidence: While no direct counter-evidence was submitted against the model's performance, the satisfaction of the results could vary based on subjective interpretations of reasoning displays.
Claim rating: 8 / 10
Claim: Using Python as a tool drastically improves the speed of executing complex reasoning tasks compared to pure verbal logic.
Evidence: The video shows that Gemini 2.0, when utilizing Python, solved the same elevator logic task in 9.9 seconds, significantly faster than the verbal execution which took longer to calculate.
Counter evidence: The effectiveness of verbal reasoning may still hold merit for understanding, but it lacks the computational speed seen with programming.
Claim rating: 8 / 10
Model version: 0.25 ,chatGPT:gpt-4o-mini-2024-07-18