Build LLM from Scratch
Learn how to create, train, and tweak large language models (LLMs) by building one from the ground up! Master the complete process from initial design and creation, to pretraining on a general corpus, and fine-tuning for specific tasks.
What You'll Learn
Core Concepts
- • Plan and code all the parts of an LLM
- • Prepare datasets suitable for LLM training
- • Fine-tune LLMs for text classification
- • Implement attention mechanisms and transformers
- • Build tokenizers and embedding layers
Advanced Topics
- • Pretraining strategies and techniques
- • Model optimization and scaling
- • Instruction tuning and RLHF
- • Evaluation metrics and benchmarking
- • Deployment and inference optimization
Course Structure
Weeks 1-4: Foundation
Understanding transformer architecture, attention mechanisms, and basic language modeling
Weeks 5-8: Implementation
Building your first LLM from scratch, tokenization, and embedding strategies
Weeks 9-12: Training
Pretraining on large datasets, optimization techniques, and distributed training
Weeks 13-16: Advanced
Fine-tuning, instruction following, safety alignment, and deployment strategies
Prerequisites
Strong Python Programming
Proficiency in Python, object-oriented programming, and experience with data structures
PyTorch Experience
Familiarity with PyTorch for deep learning, neural network training, and GPU programming
Machine Learning Background
Understanding of basic ML concepts, neural networks, and deep learning fundamentals
Ready to Build Your Own LLM?
Based on "Build a Large Language Model (From Scratch)" by Sebastian Raschka
Course launches soon!
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