LLM Introduction
Overview
This course provides a practical, end-to-end understanding of Large Language Models (LLMs), from how they are trained to how they are adapted, deployed, and evaluated in real-world scenarios.
Participants begin with the foundations of modern LLM architectures and pre-training methods, then explore the post-training techniques that shape model behavior, including supervised fine-tuning, preference tuning, LoRA and RLHF/DPO.
The course also covers safety and alignment considerations, evaluation strategies, and performance measurement.
Through guided hands-on labs, learners will build and deploy small LLMs, fine-tune models for instruction following, and evaluate outputs across downstream tasks.
Duration
1 Day
Who Should Take This Course
Audience
Developers, Data/DevOps/AIMLOps Engineers, Technical Managers and Analysts.
Prerequisites
Participants should have basic programming skills in Python and some experience with data analysis/science.
Why You Should Take This Course
Upon completing this course, participants will be able to:
- Understand the technical foundations of Large Language Models and how they are trained
- Build pre-training pipelines, including dataset sourcing and data cleaning
- Use various tokenization methods and Transformer-based architectures
- Perform pre-training on GPT-style models
- Use post-training techniques such as Supervised Fine-Tuning, PEFT/LoRA and DPO/RLHF to shape model behavior
- Implement safety and guardrail mechanisms, including refusal behavior, tone control, and persona/system prompt design
- Evaluate LLMs using traditional performance metrics, task-specific benchmarks, and human preference scoring frameworks
Course Outline
LLM Introduction
1. LLM Overview
- LLM technical foundations
- LLM capabilities and limitations
- Popular LLMs (GPT family, Llama family, Claude, etc.)
- Use cases and applications
- LAB: Building an LLM Endpoint
2. Pre-Training
- Data collection (crawling, Common Crawl)
- Data cleaning (RefinedWeb, Dolma, FineWeb)
- Tokenization (Byte Pair Encoding (BPE), SentencePiece and Unigram language models)
- Architecture (neural networks, Transformers, GPT family, Llama family)
- Text generation (greedy and beam search, top-k, top-p)
- LAB: Pretraining a Tiny GPT LLM
3. Post-Training
- SFT (Supervised Fine-Tuning) and instruction following
- PEFT (Preference-Enhanced Fine-Tuning) and LoRA (Low-Rank Adaptation)
- RLHF (Reinforcement Learning from Human Feedback)/DPO (Direct Preference Optimization)
- Safety & Guardrail tuning
- System prompts and persona shaping
- LAB: Fine-Tuning an LLM for Instruction Following
4. Evaluation
- Traditional metrics
- Task-specific benchmarks
- Safety and Alignment Evaluation
- Human evaluation and leaderboards
- LAB: Evaluating LLM Performance on Downstream Tasks