Deep Seek R1 is an intriguing, cost-effective AI model with open weights, showcasing potential through coding applications and reasoning features.
The Deep Seek R1 model is a recently released neural network with open weights, making it accessible for various applications. Despite its large size, which makes local execution by individuals less feasible, the model has generated intrigue among developers and researchers alike. The video dives into the functionalities and features of Deep Seek R1, including its reasoning tokens—a highlight for the presenter who enjoys experimenting with coding tasks and using the model's outputs for practical applications, such as developing an app that parses URLs from PDFs. Additionally, the video discusses the price advantage of the model compared to its predecessor, and while initial tests show potential, the presenter emphasizes the need for extended evaluation to better understand the model's true capabilities over time. Overall, the engage-through-coding approach to exploring this model showcases its practicality for developers and enthusiasts in AI and machine learning.
Content rate: B
This content is informative and provides a thorough overview of the Deep Seek R1 model, its potential applications, and performance insights, although some parts are speculative regarding its effectiveness.
AI model coding open-source technology
Claims:
Claim: Deep Seek R1 has a total of 671 billion parameters.
Evidence: The presenter explicitly mentions the model's total parameters during the video.
Counter evidence: There has been limited independent verification of the model's architecture outside the claims made by the creators.
Claim rating: 8 / 10
Claim: Deep Seek R1 is significantly cheaper than previous versions, specifically $2.9 per million tokens compared to $60.
Evidence: The presenter compares the pricing of Deep Seek R1 with other models and states the cost difference.
Counter evidence: Pricing structures can vary based on token usage and subscription models, affecting direct comparisons.
Claim rating: 7 / 10
Claim: Using reasoning tokens from Deep Seek R1 provides insightful and meaningful output.
Evidence: Throughout the video, the presenter experiments with reasoning tokens and shares moments of insightful outputs.
Counter evidence: Some outputs lacked the expected connections, indicating that while useful, the reasoning aspect may not consistently yield the intended insights.
Claim rating: 6 / 10
Model version: 0.25 ,chatGPT:gpt-4o-mini-2024-07-18