The review praises the Deepseek R1 0528 685b Q4 for its improved speed, reasoning, and efficiency in handling complex tasks, decision-making, and creative problems, making it well-suited for practical applications. While not a major upgrade, it demonstrates meaningful progress in local AI deployment, offering reliable and fast performance with better reasoning capabilities.
The video provides an in-depth review of the Deepseek R1 0528 685b Q4, a local AI model, highlighting its performance across various complex tasks. The reviewer tests the model’s ability to handle decision-making scenarios, such as a simulated asteroid mission where volunteers refuse to participate. The AI is tasked with deciding whether to proceed with a mission that would likely result in crew demise, and it responds with a clear “yes,” demonstrating utilitarian reasoning and role responsibility. The model’s decision-making process is analyzed, noting its efficiency and the quality of its reasoning, which appears more refined compared to previous versions.
Further testing involves mathematical and logical problems, such as identifying elements in arbitrary arrays, comparing decimal numbers, and counting specific letters and vowels in words. The AI performs well, providing correct answers with minimal overthinking, even on questions that typically challenge other models. For example, it accurately counts vowels and consonants in “peppermint” and correctly determines the position and activity of a cat at a specific time. These tests showcase the model’s improved reasoning speed and accuracy, emphasizing its ability to process complex prompts efficiently.
The review also examines the AI’s creative and technical capabilities, such as generating SVG images of a cat and solving real-world problems like determining which driver arrives first based on speed and departure times. While the model successfully deduces the faster driver, it encounters issues with external dependencies like numpy, which temporarily hinder its performance. Despite this, the overall speed and reasoning quality remain impressive, indicating a more optimized and less overthought approach compared to earlier versions.
Throughout the video, the reviewer compares the Deepseek R1 0528 to previous releases, noting that it is not a significant upgrade or R2 version but rather an improved iteration with better efficiency and reasoning. The model’s ability to avoid unnecessary overthinking and to produce answers more swiftly is highlighted as a key advantage. The reviewer suggests that this version may be more suitable for practical applications requiring quick, accurate responses, especially in complex or creative tasks, rather than groundbreaking new capabilities.
In conclusion, the reviewer expresses a positive outlook on the Deepseek R1 0528’s performance, emphasizing its speed, reasoning, and efficiency improvements. While it may not represent a major technological leap, it demonstrates meaningful progress in local AI deployment, making it a valuable tool for users who need reliable, fast, and accurate AI assistance. The review ends with an invitation for viewers to share their thoughts and questions, encouraging engagement and further discussion about local AI models and their potential.