LLAMA vs Transformers: Exploring the Key Architectural Differences (RMS Norm, GQA, ROPE, KV Cache)

In this video, we explore the architectural differences between LLaMA and the standard transformer model. We dive deep into the major changes introduced by LLaMA, such as Pre-Normalization, SwiGLU activation function, Rotary Position Embedding (RoPE), Grouped Query Attention, and the use of KV Cache for improved performance.

You’ll learn:

The impact of Pre-Normalization for improved gradient flow and stability during training.
How the SwiGLU activation function outperforms traditional ReLU.
The benefits of RoPE for handling longer sequences.
Why Grouped Query Attention is more efficient than Multi-Head Attention.
How KV Cache optimizes memory usage and boosts inference speeds.
Join me as we break down these changes in detail and see how they significantly enhance the LLaMA model’s performance compared to vanilla transformers. Whether you’re familiar with transformers or looking to expand your understanding, this video offers valuable insights into the latest advancements in AI model architecture.

#llama #transformer #coding #tutorial #machinelearning #genai #kvcache
#rope #embedding #encoding ##encoder #decoder #generativeai #advancedai #beginners #deeplearning #chatgpt #llm #ai #research #airesearch

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