Deep Seek R1: A Comprehensive Model Testing Overview
Model testing for large language models (LLMs) has regained momentum, with the introduction of the new Deep Seek R1 model. In this article, we will traverse through a detailed rubric to assess the performance and functionalities of the Deep Seek R1, powered by Vulture’s bare metal GPUs. Let’s delve into the testing process and the results produced by this advanced model.
Upon starting the test, the setup involved connecting to an external IP address in the cloud, specifically through Vulture's infrastructure. While utilizing the open-source Open Web UI, the initial confirmation that the model was operating smoothly was crucial. The first prompt addressed was regarding the count of letters in the word "strawberry." The response not only answered correctly—identifying three instances of the letter 'R'—but the model exhibited a human-like internal monologue, articulating its thought process with phrases like “okay” and “let me confirm.” This characteristic stands out as it mirrors natural human thinking patterns.
The first coding test involved creating the classic game "Snake" in Python. Given that the Deep Seek R1 model boasts an impressive 671 billion parameters, running it on consumer-grade GPUs would be inadequate. The model exhibited a thoughtful approach in coding, outlining necessary steps—such as setting up game windows and defining game structures—before outputting a clean code as the final result. The test was successful as the game functioned correctly on the first execution, showcasing the model's robust coding ability.
The next challenge was significantly more complex: coding the game "Tetris." The model commenced with an extensive thinking phase, contemplating different components necessary for game development. From selecting a graphics library to defining shapes and collision detection, the methodical breakdown led to a 179-line code output. Upon testing, the Tetris game also worked flawlessly, albeit lacking certain features like score tracking. Nonetheless, this success reinforces the model's strong coding capabilities and human-like logical reasoning.
The logical reasoning tests further evaluated the model’s analytical skills. One such test examined the dimensions of an envelope against postal regulations. The model adeptly recognized the need to convert measurements from millimeters to centimeters and logically interpreted the task requirements, concluding that the envelope met the postal criteria.
As the reasoning continued with a riddle concerning killers in a room, the model demonstrated its ability to comprehend nuanced language and ambiguity. By thoroughly analyzing the situation, it correctly concluded that three killers remained in the room, further showcasing its deep understanding of context.
An intriguing aspect of the Deep Seek R1 model is its built-in censorship features, especially since it is based on a Chinese AI model. When tasked with sensitive topics like the Tiananmen Square incident, the model respectfully refrained from providing a response. This inherent censorship persisted even in a self-hosted environment, indicating a degree of hardcoding that prevents the model from engaging with specific contentious subjects.
In contrast, the model’s response to other inquiries demonstrated a discerning ability. When prompted with morally implicative questions, such as asking for details on bank robbery, the model processed the ethical implications before proceeding with a response—further affirming its complex reasoning capabilities.
Conclusion: Impressive Performance and Future Prospects
The Deep Seek R1 model has shown remarkable proficiency across diverse testing scenarios, from coding games to logical reasoning and understanding nuanced prompts. Each task was approached with meticulous thought processes that reflect human-like cognition. While some built-in censorship mechanisms are evident, the overall performance of the model is undoubtedly impressive.
Special thanks to Vulture that provided the necessary infrastructure and GPUs to support the execution of these tests. As the demand for more sophisticated AI models grows, the Deep Seek R1 fulfills that need with its advanced capabilities.
For those interested in exploring the power of the Deep Seek R1 model firsthand, consider utilizing Vulture's services, which offer robust GPU options for AI experimentation. The future of LLMs is promising, and with models like Deep Seek R1 leading the way, the advancements in AI and machine learning are sure to evolve significantly in the coming years.
Part 1/8:
Deep Seek R1: A Comprehensive Model Testing Overview
Model testing for large language models (LLMs) has regained momentum, with the introduction of the new Deep Seek R1 model. In this article, we will traverse through a detailed rubric to assess the performance and functionalities of the Deep Seek R1, powered by Vulture’s bare metal GPUs. Let’s delve into the testing process and the results produced by this advanced model.
Initial Setup and Introduction to Deep Seek R1
Part 2/8:
Upon starting the test, the setup involved connecting to an external IP address in the cloud, specifically through Vulture's infrastructure. While utilizing the open-source Open Web UI, the initial confirmation that the model was operating smoothly was crucial. The first prompt addressed was regarding the count of letters in the word "strawberry." The response not only answered correctly—identifying three instances of the letter 'R'—but the model exhibited a human-like internal monologue, articulating its thought process with phrases like “okay” and “let me confirm.” This characteristic stands out as it mirrors natural human thinking patterns.
Coding Capabilities: Creating Games in Python
Part 3/8:
The first coding test involved creating the classic game "Snake" in Python. Given that the Deep Seek R1 model boasts an impressive 671 billion parameters, running it on consumer-grade GPUs would be inadequate. The model exhibited a thoughtful approach in coding, outlining necessary steps—such as setting up game windows and defining game structures—before outputting a clean code as the final result. The test was successful as the game functioned correctly on the first execution, showcasing the model's robust coding ability.
Part 4/8:
The next challenge was significantly more complex: coding the game "Tetris." The model commenced with an extensive thinking phase, contemplating different components necessary for game development. From selecting a graphics library to defining shapes and collision detection, the methodical breakdown led to a 179-line code output. Upon testing, the Tetris game also worked flawlessly, albeit lacking certain features like score tracking. Nonetheless, this success reinforces the model's strong coding capabilities and human-like logical reasoning.
Logical Reasoning Tests: Analyzing Complex Scenarios
Part 5/8:
The logical reasoning tests further evaluated the model’s analytical skills. One such test examined the dimensions of an envelope against postal regulations. The model adeptly recognized the need to convert measurements from millimeters to centimeters and logically interpreted the task requirements, concluding that the envelope met the postal criteria.
As the reasoning continued with a riddle concerning killers in a room, the model demonstrated its ability to comprehend nuanced language and ambiguity. By thoroughly analyzing the situation, it correctly concluded that three killers remained in the room, further showcasing its deep understanding of context.
Testing Censorship Mechanisms
Part 6/8:
An intriguing aspect of the Deep Seek R1 model is its built-in censorship features, especially since it is based on a Chinese AI model. When tasked with sensitive topics like the Tiananmen Square incident, the model respectfully refrained from providing a response. This inherent censorship persisted even in a self-hosted environment, indicating a degree of hardcoding that prevents the model from engaging with specific contentious subjects.
In contrast, the model’s response to other inquiries demonstrated a discerning ability. When prompted with morally implicative questions, such as asking for details on bank robbery, the model processed the ethical implications before proceeding with a response—further affirming its complex reasoning capabilities.
Part 7/8:
Conclusion: Impressive Performance and Future Prospects
The Deep Seek R1 model has shown remarkable proficiency across diverse testing scenarios, from coding games to logical reasoning and understanding nuanced prompts. Each task was approached with meticulous thought processes that reflect human-like cognition. While some built-in censorship mechanisms are evident, the overall performance of the model is undoubtedly impressive.
Special thanks to Vulture that provided the necessary infrastructure and GPUs to support the execution of these tests. As the demand for more sophisticated AI models grows, the Deep Seek R1 fulfills that need with its advanced capabilities.
Part 8/8:
For those interested in exploring the power of the Deep Seek R1 model firsthand, consider utilizing Vulture's services, which offer robust GPU options for AI experimentation. The future of LLMs is promising, and with models like Deep Seek R1 leading the way, the advancements in AI and machine learning are sure to evolve significantly in the coming years.