📦 Module 6: Specialization — Supervised Fine-Tuning (SFT) for Q&A

[malibayram]

📦 Module 6: Specialization — Supervised Fine-Tuning (SFT) for Q&A

This module covers supervised fine-tuning to adapt the pretrained model for specific tasks like Question-Answering.

Tasks to Complete:

• Lesson 6.1 — Why Fine-Tuning? Pretraining Isn't Enough

  • Explain limitations of pretraining for specific tasks
  • Show examples of pretrained vs fine-tuned model outputs
  • Introduce the concept of task-specific adaptation

• Lesson 6.2 — Intro to Supervised Fine-Tuning

  • Explain SFT methodology and process
  • Compare different fine-tuning approaches
  • Discuss learning rate considerations for fine-tuning
  • Cover catastrophic forgetting and mitigation strategies

• Lesson 6.3 — Q&A Dataset (e.g., GammaCorpus / Turkish QA)

  • Explore and analyze Q&A datasets
  • Prepare GammaCorpus or Turkish QA dataset
  • Format data for question-answering task
  • Create data preprocessing pipeline for Q&A

• Lesson 6.4 — Adapting the Training Loop for SFT

  • Modify training loop for supervised fine-tuning
  • Implement task-specific loss calculation
  • Adjust hyperparameters for fine-tuning
  • Add evaluation metrics for Q&A performance

• Lesson 6.5 — Running Fine-Tuning for QA Task

  • Execute fine-tuning on Q&A dataset
  • Monitor fine-tuning progress and metrics
  • Save fine-tuned model checkpoints
  • Validate model performance on test set

• Lesson 6.6 — Inference with Fine-Tuned QA Model

  • Implement Q&A inference pipeline
  • Create question-answering interface
  • Test model on various question types
  • Compare performance with pretrained model

Deliverables:

• 6 video lectures (~25 minutes each)
• Q&A dataset preparation notebook
• Supervised fine-tuning implementation
• Fine-tuning training pipeline
• Q&A inference and evaluation notebook
• Fine-tuned model checkpoints
• Module quiz

Key Implementation Files:

• qa_dataset.py - Q&A dataset preparation
• fine_tuning.py - SFT training loop
• qa_inference.py - Question-answering interface
• evaluation_metrics.py - Q&A performance metrics
• Fine-tuning configuration files

Fine-Tuning Components:

• Task-specific data formatting
• Modified training loop for SFT
• Evaluation metrics (BLEU, ROUGE, exact match)
• Hyperparameter optimization for fine-tuning
• Model comparison and validation

Datasets:

• GammaCorpus Turkish Q&A dataset
• Alternative English Q&A datasets
• Custom Q&A data preparation tools

Resources:

• Supervised fine-tuning best practices
• Q&A evaluation metrics
• Turkish NLP resources and datasets