Welcome to the foundational level of LLM mastery! This level introduces you to the core concepts of Natural Language Processing and Large Language Models. You'll learn how text is processed, how transformers work, and how to use pre-trained models effectively.
- Understand text tokenization and vocabulary building
- Master transformer architecture basics
- Learn attention mechanisms and positional encoding
- Use pre-trained models for various NLP tasks
- Develop prompt engineering skills
- Build your first LLM-powered application
Duration: 2-3 hours
Concepts: Characters vs tokens, regex splitting, vocabulary creation, encoding/decoding
What You'll Learn:
- How LLMs process raw text
- Creating custom tokenizers
- Building vocabulary from text corpus
- Encoding text to IDs and decoding back
Key Code:
# Tokenization example
preprocessed = re.split(r'([,.:;?_!"()\']|--|\\s)', raw_text)
vocab = {token: integer for integer, token in enumerate(all_words)}Duration: 2-3 hours
Concepts: HuggingFace pipelines, pre-trained models, zero-shot classification, sentiment analysis
What You'll Learn:
- Using HuggingFace transformers library
- Different pipeline types (classification, generation, NER, Q&A)
- Model selection and loading
- Practical applications of pre-trained models
Key Code:
from transformers import pipeline
classifier = pipeline("sentiment-analysis")
result = classifier("I love this course!")Duration: 3-4 hours
Concepts: Word2Vec, GloVe, embedding spaces, semantic similarity
What You'll Learn:
- Vector representations of words
- Training Word2Vec models
- Using pre-trained embeddings
- Measuring semantic similarity
- Visualizing embedding spaces
Applications:
- Finding similar words
- Analogy tasks (king - man + woman = queen)
- Document similarity
Duration: 3-4 hours
Concepts: Self-attention, query-key-value, multi-head attention, attention scores
What You'll Learn:
- Why attention is crucial for transformers
- Computing attention scores
- Multi-head attention benefits
- Visualizing attention patterns
Key Formula:
Attention(Q, K, V) = softmax(QK^T / √d_k)V
Duration: 2 hours
Concepts: Absolute vs relative position, sinusoidal encoding, learned positions
What You'll Learn:
- Why transformers need positional information
- Sinusoidal positional encoding
- Learned positional embeddings
- Impact on model performance
Duration: 4 hours
Concepts: Encoder-decoder, layer normalization, residual connections, feed-forward networks
What You'll Learn:
- Complete transformer architecture
- Encoder vs decoder differences
- Layer normalization and residual connections
- Building blocks of transformers
Architecture Components:
- Input Embedding + Positional Encoding
- Multi-Head Attention
- Feed-Forward Network
- Layer Normalization
- Residual Connections
Duration: 3 hours
Concepts: MLM (Masked Language Modeling), CLM (Causal Language Modeling), training objectives
What You'll Learn:
- Different pre-training strategies
- BERT-style masked language modeling
- GPT-style causal language modeling
- Next sentence prediction
Duration: 3 hours
Concepts: BPE, WordPiece, SentencePiece, subword tokenization
What You'll Learn:
- Byte Pair Encoding (BPE) algorithm
- WordPiece tokenization (BERT)
- SentencePiece (language-agnostic)
- Handling unknown words and rare tokens
Comparison:
| Tokenizer | Used By | Strengths |
|---|---|---|
| BPE | GPT-2, GPT-3 | Efficient, handles rare words |
| WordPiece | BERT | Good for morphology |
| SentencePiece | T5, XLNet | Language-agnostic |
Duration: 2 hours
Concepts: HuggingFace Hub, model selection, inference optimization
What You'll Learn:
- Browsing HuggingFace model hub
- Loading models and tokenizers
- Running inference efficiently
- Model cards and documentation
Duration: 3 hours
Concepts: Greedy search, beam search, sampling, temperature, top-k, top-p
What You'll Learn:
- Different decoding strategies
- Temperature and randomness control
- Top-k and nucleus (top-p) sampling
- Controlling generation quality
Generation Parameters:
output = model.generate(
input_ids,
max_length=50,
temperature=0.7,
top_k=50,
top_p=0.9,
num_beams=5
)Duration: 3 hours
Concepts: Prompt design, instruction following, context setting
What You'll Learn:
- Crafting effective prompts
- Zero-shot prompting techniques
- Role-playing and persona setting
- Prompt templates
Best Practices:
- Be specific and clear
- Provide examples when needed
- Set the context properly
- Iterate and refine
Duration: 2 hours
Concepts: Classification without training, label understanding
What You'll Learn:
- Zero-shot classification
- How models understand labels
- Practical applications
- Limitations and strengths
Duration: 3 hours
Concepts: In-context learning, example selection, prompt formatting
What You'll Learn:
- Providing examples in prompts
- Example selection strategies
- Few-shot vs fine-tuning
- Optimal number of examples
Duration: 3 hours
Concepts: Perplexity, BLEU, ROUGE, F1, accuracy
What You'll Learn:
- Perplexity for language models
- BLEU for translation
- ROUGE for summarization
- Classification metrics
- Choosing appropriate metrics
Metrics Summary:
- Perplexity: Lower is better (measures uncertainty)
- BLEU: 0-1, higher is better (n-gram overlap)
- ROUGE: 0-1, higher is better (recall-oriented)
- F1: Harmonic mean of precision and recall
Duration: 4-6 hours
Project: Build a conversational chatbot using pre-trained models
Requirements:
- Use a pre-trained conversational model
- Implement conversation history
- Add personality through prompts
- Handle multi-turn conversations
- Create a simple UI (optional)
Skills Applied:
- Model loading and inference
- Prompt engineering
- Text generation
- Context management
pip install transformers torch numpy pandas matplotlib seaborn
pip install datasets tokenizers sentencepiece
pip install jupyter ipywidgetsimport transformers
import torch
print(f"Transformers version: {transformers.__version__}")
print(f"PyTorch version: {torch.__version__}")
print(f"CUDA available: {torch.cuda.is_available()}")Track your progress through the level:
- L1: Text Processing & Tokenization
- L2: Transformer Pipelines
- L3: Word Embeddings
- L4: Attention Mechanisms
- L5: Positional Encoding
- L6: Transformer Architecture
- L7: Pre-training Concepts
- L8: Tokenizers Deep Dive
- L9: Model Loading & Inference
- L10: Text Generation Basics
- L11: Prompt Engineering 101
- L12: Zero-shot Learning
- L13: Few-shot Learning
- L14: Model Evaluation Metrics
- L15: Basic Project
After completing this level, you should be able to:
- Explain how transformers process text
- Build custom tokenizers
- Use pre-trained models for various tasks
- Understand attention mechanisms
- Generate text with different strategies
- Engineer effective prompts
- Evaluate model performance
- Build a simple LLM application
- "Build a Large Language Model (From Scratch)" - Sebastian Raschka
- "Natural Language Processing with Transformers" - Lewis Tunstall et al.
- "Attention Is All You Need" (Vaswani et al., 2017)
- "BERT: Pre-training of Deep Bidirectional Transformers" (Devlin et al., 2018)
- "Language Models are Few-Shot Learners" (Brown et al., 2020)
- HuggingFace Course: https://huggingface.co/course
- Jay Alammar's Blog: https://jalammar.github.io/
- Andrej Karpathy's YouTube: Neural Networks Zero to Hero
Once you complete Level 1, proceed to: Level 2: Intermediate - Fine-tuning, advanced architectures, and specialized models
Good luck with your learning journey!