Code Llama 4 from Scratch
This lecture teaches how to code Llama 4 from scratch.
Contents
- Introduction to the course
- Llama 4 Overview and Ranking
- Course Prerequisites
- Course Approach for Beginners
- Why Code Llama from Scratch?
- Understanding LLMs and Text Generation
- How LLMs Predict the Next Word
- Probability Distribution of Next Words
- The Role of Data in Prediction
- Probability Distribution and Word Prediction
- Sampling Techniques
- Greedy Sampling
- Random Sampling
- Top K Sampling
- Temperature Sampling for Controlling Randomness
- What are Tokens?
- Tokenization Example: "Hello world"
- How LLMs Learn Semantic Meaning
- Token Relationships and Context
- The Concept of Embeddings
- Tokenization Challenges
- Large Vocabulary Size
- Handling Misspellings and New Words
- Introducing Subword Tokens
- Byte Pair Encoding (BPE) Overview
- Understanding Vector Embeddings
- Visualizing Embeddings
- The Embedding Layer
- Token Indexing and Swapping Embeddings
- Coding Your Own Tokenizer
- Implementing Byte Pair Encoding
- Initializing Vocabulary and Pre-tokenization
- Splitting Text into Words
- Calculating Pair Frequencies
- Merging Frequent Pairs
- Updating Vocabulary and Tokenization Rules
- Implementing the Merges
- Encoding Text with the Tokenizer
- Decoding Tokens Back to Text
- Self-Attention Mechanism
- Query, Key, and Value Vectors
- Calculating Attention Scores
- Applying Softmax
- Weighted Sum of Values
- Self-Attention Matrix Operations
- Multi-Head Attention
- Implementing Self-Attention
- Masked Self-Attention
- Rotary Positional Embeddings (RoPE)
- Understanding Positional Information
- How RoPE Works
- Implementing RoPE
- Feed-Forward Networks (FFN)
- Linear Layers and Activations
- Implementing FFN

Fact Check: "Code Llama 4 from Scratch" Course Outline

Verdict: Mostly Accurate, but the title is misleading.

The topics listed are genuine components of modern decoder-only LLMs (including the Llama family), but there is one major issue.

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✅ Accurate Topics

These are all foundational concepts you'd learn when building a transformer-based LLM:

• ✅ Understanding LLMs and text generation

• ✅ Next-token prediction

• ✅ Probability distributions

• ✅ Greedy, Random, Top-K, Temperature sampling

• ✅ Tokens and tokenization

• ✅ Embeddings

• ✅ Subword tokenization

• ✅ Byte Pair Encoding (BPE)

• ✅ Coding a tokenizer

• ✅ Self-attention

• ✅ Query, Key, Value vectors

• ✅ Softmax attention

• ✅ Multi-head attention

• ✅ Masked self-attention

• ✅ Feed-forward networks (FFN)

• ✅ Linear layers

These are standard topics covered in transformer implementations.

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⚠️ Partially Accurate

Rotary Positional Embeddings (RoPE)

• ✅ Llama models use RoPE instead of learned positional embeddings.

• ✅ Learning how to implement RoPE is appropriate when recreating a Llama-style architecture.

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❌ Misleading Part

"Code Llama 4 from Scratch"

This title is misleading because:

• Llama 4's complete architecture has not been publicly released in enough detail to reproduce it exactly.

• Llama 4 includes additional architectural innovations beyond a basic transformer, such as:

  • Mixture-of-Experts (MoE) routing

  • Very long-context optimizations

  • Large-scale distributed training techniques

  • Proprietary training data and recipes

A course covering only tokenization, attention, RoPE, and FFNs is not implementing the full Llama 4 model.

What you're actually building is closer to:

• a Llama-style transformer,

• or a mini GPT/Llama implementation,

• rather than a faithful reproduction of Llama 4.

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Missing Topics (if the goal is a true Llama-style implementation)

A more complete implementation would also cover:

• ❌ RMSNorm

• ❌ SwiGLU activation (instead of a basic FFN)

• ❌ KV Cache for efficient inference

• ❌ Grouped Query Attention (GQA)

• ❌ Causal masking implementation details

• ❌ Optimizer and training loop

• ❌ Cross-entropy loss

• ❌ Mixed precision training

• ❌ Checkpoint saving/loading

• ❌ Distributed training

• ❌ Mixture-of-Experts (for Llama 4 specifically)

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Final Verdict

Claim	Verdict
Covers LLM fundamentals	✅ Correct
Covers transformer implementation	✅ Correct
Covers tokenizer implementation	✅ Correct
Covers RoPE	✅ Correct
Actually codes the full Llama 4 architecture	❌ Misleading
Better described as "Build a Llama-style Transformer from Scratch"	✅ Yes

Overall accuracy: 8.5/10. The curriculum is technically sound for learning LLM fundamentals, but the title "Code Llama 4 from Scratch" overstates what such a course can realistically implement unless it also covers Llama 4–specific architectural details and training methods.