📚 Prerequisites Guide

Everything you need to know before building your first LLM

🚀 Quick Start Checklist

✅ Required Knowledge

  • • Basic Python programming
  • • High school mathematics
  • • Basic command line usage

⚡ Estimated Time

  • • Setup: 10-15 minutes
  • • Tutorial: 2-3 hours
  • • Training: 20-30 minutes

💻 System Requirements

🖥️ Minimum

  • • 8GB RAM
  • • 2GB free disk space
  • • Python 3.10+
  • • Internet connection

⚡ Recommended

  • • 16GB+ RAM
  • • GPU (NVIDIA/Apple Silicon)
  • • SSD storage
  • • Stable internet

🚀 Optimal

  • • 32GB+ RAM
  • • Modern GPU (RTX 5080+/H200/AMD RX 8900-AI/Apple M4 Pro+)
  • • Fast NVMe SSD
  • • Good cooling

🔧 Platform Setup

Step 1: Install Python

# Using Homebrew (recommended)
brew install python

# Verify installation
python3 --version

Alternative: Download from python.org

Step 2: Install Git

# Install Git
brew install git

# Configure Git (optional)
git config --global user.name "Your Name"
git config --global user.email "your.email@example.com"

Step 3: Setup Project Environment

# Create project directory
mkdir llm-tutorial && cd llm-tutorial

# Create virtual environment
python3 -m venv venv

# Activate virtual environment
source venv/bin/activate

# Download requirements
curl -O https://raw.githubusercontent.com/geekyabhijit/Build_LLM/main/requirements.txt

# Install dependencies
pip install -r requirements.txt

🍎 Apple Silicon Optimization

Your Mac will automatically use MPS (Metal Performance Shaders) for GPU acceleration. No additional setup needed!

👨‍💻 Development Environment

🎯 Recommended: VS Code

  • • Download from code.visualstudio.com
  • • Install Python extension
  • • Install Jupyter extension
  • • Built-in terminal

💡 Pro tip: VS Code will automatically detect your virtual environment

🔄 Alternatives

  • • PyCharm: Full IDE experience
  • • Jupyter Lab: Notebook-focused
  • • Vim/Neovim: Terminal-based
  • • Sublime Text: Lightweight

🔧 Environment Managers (2025)

While Conda is still fine, many developers have moved to lighter alternatives for reproducible environments:

Micromamba

# Install micromamba
curl -Ls https://micro.mamba.pm/api/micromamba/$(uname -s)/latest | tar -xvj bin/micromamba

# Create environment
micromamba create -n llm-tutorial python=3.12

# Activate environment
micromamba activate llm-tutorial

Pixi

# Install pixi
curl -fsSL https://pixi.sh/install.sh | bash

# Create project
pixi init llm-tutorial
pixi add torch torchvision torchaudio

# Run commands in environment
pixi run python script.py

Both are faster and lighter than Conda, with better dependency resolution.

🧠 Knowledge Prerequisites

✅ Essential (You Must Know)

Python Basics

  • • Variables and data types
  • • Functions and classes
  • • Lists and dictionaries
  • • For loops and if statements

Math Concepts

  • • Basic algebra
  • • Matrix multiplication (concept)
  • • What is a derivative (intuition)
  • • Probability basics

⚡ Helpful (Nice to Have)

Programming

  • • NumPy basics
  • • Basic command line
  • • Git basics
  • • Virtual environments

AI/ML

  • • What neural networks do
  • • Gradient descent (concept)
  • • Training vs inference
  • • Overfitting concept

See our Cutting-Edge LLM Learning Resources for crash courses on GenAI & LLMs

🚀 Advanced (We'll Teach You)

Don't worry if you don't know these - the tutorial covers everything:

  • • Transformer architecture
  • • Self-attention mechanism
  • • Tokenization
  • • Embedding layers
  • • PyTorch fundamentals
  • • Backpropagation
  • • Model training loops
  • • GPU acceleration

📖 Quick Learning Resources

Need to brush up on something?

🐍 Python Programming (30-60 mins)

📚 Interactive Tutorials:

📖 References:

🧮 Mathematics (45-90 mins)

🎥 Visual Learning:

📚 Interactive Practice:

🤖 Deep Learning Fundamentals (1-2 hours)

🎯 Essential Watching:

🚀 Practical Courses:

🔥 PyTorch Essentials (30-45 mins)

📖 Official Resources:

🎥 Video Tutorials:

🔧 Development Tools (20-30 mins)

📝 Editor Setup:

⚙️ Environment Management:

🌟 Cutting-Edge LLM Learning Resources (2025)

Stay current with the latest in LLM research and applications

🚀 Crash-course on GenAI & LLMs

📚 Google Cloud Micro-courses:

Fast, hands-on, and updated this month.

🎓 Full Semester Depth

📚 University Courses:

🔧 Prompt Engineering & LLMOps

📚 Updated Courses:

Keeps your deployment & prompting skills current.

⚡ Performance Enhancements (2025)

Optional enhancements for faster training and inference

🔥 Flash-Attention 3

For faster training on Ampere+ GPUs:

pip install flash-attn --no-build-isolation

Add to your requirements.txt for faster attention mechanisms.

🚀 vLLM

For blazing-fast inference:

pip install vllm

Now officially under the PyTorch Foundation for optimized inference.

🐳 Containerization

Pre-baked Docker image with all optimizations:

# Use the llm-stack Docker image
# PyTorch 2.7 + CUDA 12.6 + Flash-Attn 3 pre-baked
docker run --gpus all -it llm-stack/pytorch:2.7-cuda12.6-flash3

Simplified setup with all performance enhancements pre-installed.

✅ Final Readiness Check

Ready to start? Check these boxes:

🔧 Common Setup Issues

❌ "python: command not found"

Try these solutions:

  • Use python3 instead of python
  • Check if Python is in your PATH environment variable
  • Reinstall Python and check "Add to PATH" option
  • Restart your terminal/command prompt
❌ "pip install" fails with permissions error

Solutions:

  • Make sure your virtual environment is activated
  • On Linux/Mac: Don't use sudo with pip
  • Try: pip install --user package_name
  • Update pip: python -m pip install --upgrade pip
❌ PyTorch installation issues

Platform-specific solutions:

  • Windows: Install Microsoft Visual C++ Build Tools
  • Mac M1/M2: Use the conda-forge channel
  • Linux: Check CUDA version compatibility
  • All: Try CPU-only version first: pip install torch --index-url https://download.pytorch.org/whl/cpu
❌ Virtual environment not working

Common fixes:

  • Make sure you're in the right directory
  • Check activation command for your platform
  • Verify with: which python (should show venv path)
  • Try recreating: rm -rf venv && python -m venv venv

🆘 Need Help?

💬 Community