Traing and Evaluation Explained සිංහලෙන් - Part 7 | How to Fine-Tune Large Language Models (LLMs)

පාඩම 07 - මේ පාඩමෙන් අපි බලමු කොහොමද traing and validation methods setup කරන්නෙ කියලා.

📚Colab Notebook: https://colab.research.google.com/drive/1Bbf5aW9SmUtsS5D7ywBAmCdMkC73H5lK?usp=sharing
📊Dataset: https://github.com/HGSChandeepa/ML-Projects/blob/main/finetune-bert%20for%20classification/spamdata_v2.csv

In this video series, you’ll learn how to fine-tune an LLM from scratch. We’ll explain all the essential theoretical concepts and coding techniques you need to master before diving into fine-tuning.

Follow along with our Jupyter Notebook walkthrough to learn how to preprocess data, freeze BERT parameters, handle class imbalance, and train a high-performing spam classifier. This video is perfect for machine learning enthusiasts, NLP beginners, and data scientists looking to master fine-tuning in 2025.

🔑 What You’ll Learn:

- What it means to fine-tune large language models and the different methods used for fine-tuning
- Choosing between an encoder, decoder, or encoder-decoder model — and why it matters
- Preparing the fine-tuning dataset and performing preprocessing
- Understanding Transformers and loading base pre-trained models
- Tokenization and embeddings explained
- The self-attention mechanism in detail
- Creating DataLoaders for LLM training
- Building the LLM architecture (including fully connected layers, activation functions, dropout layers, and softmax)
- Using the Adam optimizer
- Strategies to handle class imbalance
- How Negative Log-Likelihood (NLL) is used in classification tasks
- Training and validating the model
- Final evaluation: loss calculation, classification report, and confusion matrix
- How to make inferences and predictions on new, unseen text
- Fine-tuning BERT for text classification using PyTorch
- Five fine-tuning methods: feature-based, full fine-tuning, layer-wise, adapters, and gradual unfreezing
- Handling class imbalance with weighted loss functions
- Tokenizing text using BertTokenizerFast and preparing DataLoaders

Видео Traing and Evaluation Explained සිංහලෙන් - Part 7 | How to Fine-Tune Large Language Models (LLMs) канала Adomic