Summary
The video introduces the large language model Deep Seek R1 and its impressive performance on reasoning tasks including math, coding, and scientific reasoning. It delves into how the model evaluates itself, incorporates self-guidance, and utilizes model distillation for optimization. Additionally, it explores prompt engineering techniques for enhancing responses, explains the model's use of reinforcement learning akin to how robots learn, and details model distillation to improve accessibility and task performance.
Introduction to Deep Seek R1
Introducing the new large language model Deep Seek R1 and its benchmark performance in reasoning problems like math, coding, and scientific reasoning.
Chain of Thought Technique
Exploring how the model self-evaluates its performance, guides itself, and uses model distillation in Deep Seek R1.
Prompt Engineering Technique
Discussing prompt engineering technique used to explain reasoning and guide the model in providing better responses.
Reinforcement Learning in Deep Seek R1
Explaining how Deep Seek uses reinforcement learning to train the model, similar to how robots learn to walk and adapt over time.
Model Distillation
Detailing the model distillation technique to train a smaller model from a larger one, improving accessibility and performance on tasks like answering questions and generating examples.
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