Easy AI Tutorial Series: Understanding the LLaMA Model Architecture

Forum topic · 小凯 · 2026-03-27

Summary

This zhichai.net forum post introduces an interactive tutorial from the Easy AI series explaining Meta's LLaMA open-source large language model. It visualizes the LLaMA architecture through five modules: an architecture view covering the tokenizer (128K vocabulary in LLaMA-3), embedding layer, decoder-only stack with multi-head self-attention and GQA, MLP feed-forward networks, and output layer; a data flow animation showing how input text is tokenized, embedded, processed through attention and feed-forward layers, and turned into next-token probabilities; an evolution timeline comparing LLaMA-1 (Feb 2023, 1T tokens, RMSNorm, SwiGLU, rotary position embeddings), LLaMA-2 (Jul 2023, 2T tokens, 4096 context, GQA, commercial license), and LLaMA-3 (Apr 2024, 15T+ tokens, 8K context, 128K tokenizer); plus parameter comparison charts spanning 7B to 400B parameters. The post serves as a learning hub for understanding decoder-only LLM design and training-scale trends.

Easy AI Tutorial Series: Understanding the LLaMA Model

This post from zhichai.net's Easy AI tutorial series presents an interactive learning platform for Meta's open-source LLaMA large language model. The platform breaks the model down into visualizable modules.

Architecture View

The architecture is a Decoder-Only Transformer composed of:

Tokenizer: Converts input text into token sequences using an efficient subword encoding algorithm. LLaMA-3 uses a 128K-vocabulary tokenizer, supporting multilingual text and improving encoding efficiency.
Embedding layer: Maps token IDs into a high-dimensional vector space, including positional encoding information for semantic representation and context understanding.
Decoder: The core component, built from stacked Decoder Blocks with multi-head self-attention (masked attention ensures causality) and feed-forward layers.
Self-Attention: Computes correlations between positions in the sequence via Query, Key, and Value vectors with softmax weighting. LLaMA-2/3 introduce Grouped Query Attention (GQA) for long-sequence modeling.
MLP feed-forward network: Two fully connected layers with activation functions for nonlinear feature transformation, with residual connections.
Output layer: A linear layer mapping to vocabulary size, producing a probability distribution over the next token and supporting multiple decoding strategies.

Data Flow Animation

An animation demonstrates the processing pipeline using the example sentence "今天天气很好" ("The weather is nice today"):

1. Text input (raw string) 2. Tokenization into a token sequence 3. Embedding into high-dimensional vectors 4. Self-attention computing inter-word correlations 5. Feed-forward network for nonlinear transformation 6. Output generation: probability distribution over the next word

Evolution Timeline

| Version | Date | Key changes | |---|---|---| | LLaMA-1 | Feb 2023 | First open large LLM, pretrained on 1T tokens; Decoder-Only Transformer, RMSNorm, SwiGLU activation, rotary position embeddings | | LLaMA-2 | Jul 2023 | Pretraining corpus expanded to 2T tokens; context doubled to 4,096; GQA introduced; improved data, stronger safety alignment, commercial-friendly license | | LLaMA-3 | Apr 2024 | 8K long-context support; 128K-vocabulary tokenizer; over 15T training tokens; enhanced multilingual and instruction-following abilities |

Parameter Comparison

The platform includes charts comparing parameter counts (7B, 8B, 13B, 30B, 65B, 70B, 400B), training data growth (in trillions of tokens), and context length improvements across versions. Highlights:

Parameter growth: From 7B up to 400B parameters — a significant capability boost.
Training data surge: From 1T to 15T+ tokens, providing a solid foundation for performance gains.
Context extension: Longer contexts enable handling of longer documents and complex conversations.

Homepage Modules

The learning platform offers: architecture visualization (interactive Decoder-Only exploration), data flow animation, evolution timeline, and parameter comparison charts.