DeepSeek R1 Explained to your grandma

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AI Summary

Summary of Deep Seek R1 Large Language Model Video

Main Takeaways from the Paper:

1. Chain of Thought Prompting:
   • Technique where the model is asked to explain its reasoning step by step.
   • Helps identify where the model’s reasoning goes wrong for correction.
2. Reinforcement Learning:
   • The model learns on its own, similar to a baby learning to walk.
   • Optimizes its policy to maximize reward without explicit correct answers.
   • Deep Seek R1’s accuracy improves over time, potentially surpassing OpenAI’s GPT models.
3. Model Distillation:
   • Makes large language models (LLMs) more accessible by teaching a smaller model to perform like a larger one.
   • Deep Seek R1 was distilled into smaller models like Llama 3 and Quen.
   • Distilled models can outperform larger models on certain tasks with less memory and storage requirements.

Detailed Instructions and URLs:

• No specific CLI commands, website URLs, or detailed instructions were provided in the transcript.

Additional Notes:

• The video discusses the potential for Deep Seek R1 to reach high accuracy levels with extended training.
• The paper includes a graph showing the improvement of Deep Seek R1 over time with reinforcement learning.
• The video explains the technical aspects of reinforcement learning and model distillation in the context of AI training.
• The video encourages viewers to read the paper and try Deep Seek on AMA, but no URL is provided.