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YeonwooSung/llm-architecture-from-scratchDocumentazione

YeonwooSung/llm-architecture-from-scratch open-source repository on GitHub

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Installazione rapida
git clone https://github.com/YeonwooSung/llm-architecture-from-scratch.git

LLM architecture from scratch

Trendshift

Implementing LLM architectures from scratch for deep understanding of modern LLM architectures.

Implemented

Model Model Size Modality Normalization Positional Encoding Attention Mixture of Experts
GPT-2 XL 1.5B Text LayerNorm Learned Absolute Multi-Head Self-Attention (causal) ❌
Llama3-8B 8B Text RMSNorm RoPE Grouped-Query Attention (causal) ❌
Llama 3.2-3B 3B Text RMSNorm RoPE Grouped-Query Attention (causal) ❌
OlMo 2 7B 7B Text RMSNorm RoPE Grouped-Query Attention (causal) ❌
Phi-4 14B Text RMSNorm RoPE Grouped-Query Attention (causal) ❌
DeepSeek v3 671B Text RMSNorm RoPE Multi-head Latent Attention ✅
Gemma 3 27B Text + Image RMSNorm RoPE Sliding window + GQA ❌
xLSTM 7B Text RMSNorm RoPE mLSTM ❌
Mistral 3.1 Small 24B Text + Image RMSNorm RoPE Sliding window + GQA ❌
Llama 4 Maverick 400B Text RMSNorm RoPE + NoPE GQA ✅
Qwen3 32B Text RMSNorm RoPE Grouped-Query Attention (causal) ❌
Qwen3 (235B-A22B) 235B A22B Text RMSNorm RoPE Grouped-Query Attention (causal) ✅
SmolLM3 3B Text RMSNorm RoPE + NoPE Grouped-Query Attention (causal) ❌
Kimi K2 1T Text RMSNorm RoPE Multi-head Latent Attention ✅
GPT OSS 120B Text RMSNorm YaRN RoPE Grouped-Query Attention (causal) ✅
GLM 4.5 355B Text RMSNorm RoPE Grouped-Query Attention (causal) ✅
Grok 2.5 270B Text RMSNorm RoPE Grouped-Query Attention (causal) ✅
MiniMax M2 (230B) 230B Text RMSNorm RoPE Grouped-Query Attention (causal) ✅
Qwen3 Next 80B-A3B Text RMSNorm RoPE Grouped-Query Attention (causal) ✅
Ouro Thinking 2.6B Text RMSNorm RoPE Grouped-Query Attention (causal) ❌

In progress

  • OLMo3 (32B)
  • DeepSeek v3.2 (671B)
  • Mistral Large 3
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Data snapshot: 2026-11-09