Lecture Notes on Deep Seek's R1 Language Model

Introduction

• Announcement: In January 2025, Deep Seek released R1, a competitive language model.
• Significance: Requires less compute compared to other leading models and publicly released model weights and code.
• Background: Published monthly reports in 2024, detailing innovations leading to R1.

Key Innovations

Multi-Head Latent Attention

• Introduction: Announced in June 2024, essential for R1's efficiency.
• Technique: Aims to optimize the Transformer architecture by reducing the key value cache size by 57x.
• Outcome: R1 generates text 6x faster than traditional Transformers.

Technical Deep Dive

Transformer Architecture

• Mechanism: Uses attention to handle interactions between tokens.
• Example: GPT-2's attention heads and layers detailed.
• Deep Seek R1: 128 attention heads per layer and 61 layers.

Attention Pattern Calculation

• Process:
  • Input Matrix X processed into matrices Q (queries) and K (keys).
  • Dot products computed to find similar keys and queries.
  • Normalization through softmax.

Key Value Caching

• Concept: Stores keys and values to efficiently compute new tokens.
• Challenge: Increased memory usage with large models.
• Deep Seek's Solution: Clever handling of KV caching resulting in significant efficiency improvements.

Memory and Computation

Multi-Head Latent Attention

• Reduction: Reduces KV cache size by 57x while improving performance.
• Mechanism:
  • Compresses keys and values into a latent space shared across attention heads.
  • Uses learned weights to project back to original dimensions.

Implementation

• Efficiency: Absorbs extra computation into fixed training weights; no additional compute during inference.
• Performance: Linear scaling of compute with input size.

Comparison and Impact

• Cache Requirements:
  • Traditional attention: 4 MB per token.
  • Grouped query attention: 500 KB per token.
  • Multi-Head Latent Attention: 70 KB per token.
• Advantage: R1 is faster and more efficient than previous models.

Broader Implications

• Transformer Significance: Critical for AI development.
• Deep Seek's Contribution: Improved Transformer performance.
• Future Prospects: Anticipation for further breakthroughs in neural networks and intelligent systems.

Additional Resources

• Graphics and Posters: Visual aids available for deeper understanding and educational purposes.
• Welch Labs Store: Offers additional materials, such as a detailed poster and a book on imaginary numbers.

Conclusion

• Summary: Deep Seek has made a substantial contribution to the efficiency and capability of Transformer models.
• Invitation for Engagement: Explore materials for a deeper understanding and support further content creation.