Training a Large Language Model (LLM) involves multiple stages, from basic pretraining on large datasets to advanced fine-tuning for specific applications. This document outlines the essential steps in training LLMs at different levels: Basic, Intermediate, and Advanced.
Sources: Web scraping, books, Wikipedia, research papers.
Cleaning: Removing duplicates, correcting syntax errors, tokenization.
Formatting: Converting text into a structured format suitable for training.
Converts raw text into numerical representations (tokens).
Types: Word-level (e.g., Word2Vec) Subword-level (e.g., Byte Pair Encoding, SentencePiece) Character-level
Choosing the Right Transformer-Based Model BERT (Bidirectional Encoder Representations from Transformers) GPT (Generative Pretrained Transformer) T5 (Text-To-Text Transfer Transformer) LLaMA, Falcon, Mistral
Masked Language Modeling (MLM): Predict missing words (used in BERT).
Causal Language Modeling (CLM): Predict the next word (used in GPT models).
Sequence-to-Sequence (Seq2Seq): Translate or generate text given input (used in T5, BART).
Batch size, learning rate, number of epochs, optimizer selection (Adam, SGD).
This level introduces domain adaptation, fine-tuning, and efficiency improvements.
Using pretrained models and fine-tuning on specific datasets.
Examples: Medical AI: Training on clinical reports (e.g., MIMIC dataset). Legal AI: Training on case law. Finance AI: Training on financial documents.
Techniques for improving training efficiency: Layer-wise Learning Rate Scaling. Adaptive optimizers (AdamW, LAMB). Mixed Precision Training (FP16, BF16 for faster computation).
Data Augmentation: Synonym replacement, paraphrasing, back-translation.
Curriculum Learning: Gradually increasing training difficulty.
Data Filtering: Removing harmful or biased text using content moderation tools.
Training models using human preferences.
Steps: Train a reward model based on human-labeled comparisons. Use the reward model to fine-tune the LLM. Improve response quality using iterative feedback.
Quantization: Reducing model precision (e.g., INT8, FP16) for deployment.
Pruning: Removing less important weights to reduce model size.
Distillation: Training a smaller model (student) to mimic a larger model (teacher).
This level focuses on cutting-edge training techniques for robust, efficient, and highly capable models.
Training on multiple GPUs/TPUs
Techniques: Data Parallelism: Splitting data across multiple devices. Model Parallelism: Splitting model layers across GPUs. Pipeline Parallelism: Processing different layers in a sequence.
Using structured prompts to guide LLM responses.
Using structured prompts to guide LLM responses.
Training models incrementally without forgetting past knowledge.
Techniques: Knowledge Retention using LoRA (Low-Rank Adaptation) Episodic memory storage for chatbot applications.
Enhancing model resilience against attacks and misleading prompts.
Techniques: Adversarial data augmentation. Model fine-tuning with adversarial examples.
Training models to understand and generate text, images, and speech.
Example models: CLIP (image-text alignment) Whisper (speech recognition & synthesis) Flamingo (vision-language models)
| Level | Key Techniques | Examples/Models |
|---|---|---|
| Basic | Data Collection, Tokenization, Pretraining | BERT, GPT, T5 |
| Basic | MLM, CLM, Seq2Seq Objectives | Masked token prediction |
| Intermediate | Transfer Learning, Domain Adaptation | Fine-tuned GPT for Medical AI |
| Intermediate | RLHF (Reinforcement Learning from Human Feedback) | ChatGPT, Claude |
| Intermediate | Model Compression (Quantization, Pruning, Distillation) | TinyBERT, DistilBERT |
| Advanced | Distributed Training (Data/Model Parallelism) | GPT-4, PaLM, LLaMA |
| Advanced | Continual Learning, Memory Augmentation | LoRA, Retrieval-Augmented Generation (RAG) |
| Advanced | Adversarial Training | Robust models against prompt attacks |
| Advanced | Multimodal Training | CLIP, Whisper, Flamingo |