Udemy - Build a Mini llama.cpp in Pure C - LLM Inference Engine

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size1.1 GB
  • Uploaded Byfreecoursewb
  • Downloads23
  • Last checkedJul. 25th '26
  • Date uploadedJul. 25th '26
  • Seeders 7
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Infohash : C6BF4403FE822F2DA53F8FACCA4172FE88892E1E

Build a Mini llama.cpp in Pure C: LLM Inference Engine

https://WebToolTip.com

Published 7/2026
Created by James Jiang
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 11 Lectures ( 2h 36m ) | Size: 1.1 GB

LLM Inference Engine & Interactive Chat

What you'll learn
⚡ Build a complete Mini llama.cpp inference engine from scratch in pure C
⚡ Understand and parse the GGUF model format used by all local LLMs
⚡ Master mmap zero-copy model loading for fast LLM startup
⚡ Implement RMSNorm, SwiGLU, and RoPE rotary positional encoding
⚡ Code causal multi-head self-attention for LLM inference
⚡ Learn how KV Cache works and why it speeds up generation by 10–100x
⚡ Implement INT4 quantization and dequantization for model compression
⚡ Build custom tensor and matrix multiplication systems from zero
⚡ Write autoregressive token generation & greedy sampling logic
⚡ Develop a fully interactive LLM terminal chat
⚡ Gain real low-level C system programming skills for AI deployment
⚡ Read and understand official llama.cpp & GGML source code

Requirements
❗ Basic C programming knowledge (structs, pointers, file IO, functions)
❗ Basic understanding of Makefile compilation
❗ Fundamental knowledge of Transformer / LLM concepts
❗ A Mac, Linux, or Windows (WSL2) computer
❗ No prior llama.cpp or inference engine experience required

Files:

[ WebToolTip.com ] Udemy - Build a Mini llama.cpp in Pure C - LLM Inference Engine
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Project Overview & Environment Setup
    • 1. Project Overview & Environment Setup.mp4 (100.8 MB)
    10 - Final Project — Interactive LLM Chat Demo
    • 10. Final Project — Interactive LLM Chat Demo.mp4 (80.2 MB)
    11 - Additional content — Advanced Optimization Roadmap
    • 11. Advanced Optimization Roadmap.mp4 (86.3 MB)
    2 - Core Foundation – Common Header & Tensor Implementation
    • 2. Core C Project & Custom Tensor System.mp4 (134.2 MB)
    3 - KV Cache Implementation
    • 3. KV Cache Implementation.mp4 (65.6 MB)
    4 - INT4 Quantization – Model Compression for Local LLM Deployment
    • 4. INT4 Quantization – Model Compression for Local LLM Deployment.mp4 (68.3 MB)
    5 - GGUF File Parser – Load Real TinyLlama Model Weights
    • 5. GGUF File Parser – Load Real TinyLlama Model Weights.mp4 (121.7 MB)
    6 - LLaMA Core Layers — RMSNorm & SwiGLU Feed Forward Network
    • 6. LLaMA Core Layers — RMSNorm & SwiGLU Feed Forward Network.mp4 (132.4 MB)
    7 - RoPE Rotary Positional Encoding
    • 7. RoPE Rotary Positional Encoding.mp4 (87.8 MB)
    8 - Causal Multi-Head Attention with KV Cache Integration
    • 8. Causal Multi-Head Attention.mp4 (99.4 MB)
    9 - Tokenizer & Autoregressive Generation
    • 9. Tokenizer & Autoregressive Generation.mp4 (125.9 MB)
    • Bonus Resources.txt (0.1 KB)

Code:

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