AI Learning Plan

DeepSeek Architecture

https://g.co/gemini/share/9c3678bc09d0 (opens in a new tab)

Read the "Attention Is All You Need" paper the finish hand-on exercise like chatbot and llm-gateway-qa first.

This is an excellent goal. Learning about DeepSeek's architecture is a fantastic way to understand the cutting-edge of language model design. Its unique approach with Mixture-of-Experts and Multi-head Latent Attention showcases the future of efficient and scalable AI.

Here is a detailed learning plan to help you go from a beginner to having a deep understanding of DeepSeek's architecture, complete with citations and learning resources for each session.

Phase 1: Foundational Knowledge (1-2 weeks)

Goal: Understand the basic building blocks of modern language models. You can't understand DeepSeek's innovations without first understanding the Transformer architecture it's built on.

Session 1: The Transformer Architecture

• Concept: The "Attention Is All You Need" paper introduced the Transformer, which is the foundational architecture for nearly all modern LLMs. Learn about its key components:
  • Self-Attention: How the model weighs the importance of different words in a sequence.
  • Multi-Head Attention: How it allows the model to pay attention to different parts of the input in parallel.
  • Positional Encodings: How the model understands the order of words since the self-attention mechanism is order-agnostic.
  • Feed-Forward Networks (FFNs): The non-attention part of the transformer block.
• Learning Resources:
  • Paper: "Attention Is All You Need" by Vaswani et al. (The original paper is a must-read, even if you just skim the high-level concepts).
  • Video: "The Illustrated Transformer" by Jay Alammar. This is the single best visual explanation of the Transformer architecture for newcomers.
  • Blog Post: "A Gentle Introduction to Transformers" by Hugging Face.

Session 2: Dense vs. Sparse Models

• Concept: Understand the fundamental difference between the dense architecture of Llama and the sparse MoE architecture of DeepSeek.
• Learning Resources:
  • Blog Post: "What is Mixture of Experts?" by IBM. This provides a great high-level overview of MoE.
  • Video/Tutorial: NVIDIA's "Applying Mixture of Experts in LLM Architectures" blog and accompanying videos. They explain the trade-offs and benefits in an accessible way.

Phase 2: DeepSeek's Key Architectural Innovations (2-3 weeks)

Goal: Dive into the specific innovations that make DeepSeek unique. This is where you'll begin to understand how it achieves its efficiency and performance.

Session 3: The DeepSeek Mixture-of-Experts (MoE) Architecture

• Concept: Explore the details of DeepSeek's specialized MoE. Learn how its "router" works, the concept of shared experts, and how this differs from other MoE models like Mixtral.
• Learning Resources:
  • Paper: "DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model" on arXiv. This is the primary source. Focus on the sections describing the DeepSeekMoE architecture.
  • Technical Analysis: "DeepSeek Technical Analysis — (1) Mixture-of-Experts" on Medium by Jinpeng Zhang. This is a fantastic breakdown of the paper's key ideas in a more digestible format.

Session 4: Multi-head Latent Attention (MLA)

• Concept: Understand how DeepSeek's novel attention mechanism works. Learn how it compresses the Key-Value (KV) cache to significantly reduce memory usage during inference, which is a major bottleneck for long context windows.
• Learning Resources:
  • Paper: The same "DeepSeek-V2" paper from Session 3. Look for the "Multi-head Latent Attention" section.
  • Blog Post: "Understanding Multi-Head Latent Attention" by Banatt. This article provides an excellent, easy-to-follow explanation of the concept of low-rank matrices and how they are used in MLA.

Phase 3: Practical Application and Hands-on Experience (3-4 weeks)

Goal: Move from theory to practice. Work with the models directly using the Hugging Face ecosystem to solidify your understanding.

Session 5: Navigating the Hugging Face Hub and Models

• Concept: Get familiar with the Hugging Face ecosystem. Learn how to find DeepSeek's models on the Hub, understand the model cards, and use the transformers library to load them.
• Learning Resources:
  • Hugging Face Documentation: "Quickstart" on the Hugging Face Transformers documentation.
  • Model Hub: Explore the official DeepSeek models: deepseek-ai/DeepSeek-V2 and deepseek-ai/deepseek-moe-16b. Pay attention to the files and their structure.

Session 6: Running Inference with DeepSeek Models

• Concept: Write your first lines of code. Use the pipeline function to perform inference (e.g., text generation, summarization) and then learn how to do it more manually with the tokenizer and model objects.
• Learning Resources:
  • Hugging Face Notebook: The official Hugging Face transformers library has excellent tutorials for inference.
  • Google Colab/Jupyter Notebooks: Create a notebook to experiment with different DeepSeek models. Pay attention to the memory usage (VRAM) and speed to see the efficiency benefits of the MoE architecture in action.

Session 7: Fine-Tuning an LLM (with a focus on MoE)

• Concept: Learn the process of fine-tuning an open-source LLM on a custom dataset. While full fine-tuning is resource-intensive, you can use Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA to practice on your own hardware.
• Learning Resources:
  • Hugging Face Tutorial: "Fine-tune a large language model with LoRA and Hugging Face." This guide walks you through the entire process step-by-step.
  • Blog Post: "Fine-Tuning LLMs: A Guide With Examples" by DataCamp. This provides a great conceptual overview before you dive into the code.

By following this plan, you will build a solid theoretical foundation and gain practical skills, giving you a deep and comprehensive understanding of the DeepSeek model architecture and its place in the world of modern AI.