Understanding Model Fine-Tuning Techniques

Sep 4, 2024

Lecture Notes on Model Fine-Tuning

Introduction to Model Fine-Tuning

  • Presenter: Shah
  • Part of a series on using large language models (LLMs) in practice.
  • Previous topic: Prompt engineering.
  • Fine-tuning is introduced as an advanced technique for specific applications.

What is Model Fine-Tuning?

  • Definition: Adjusting internal model parameters (weights/biases) of a pre-trained model.
  • Analogy:
    • Base Model (e.g., GPT-3): Like a raw diamond—valuable but rough.
    • Fine-Tuned Model (e.g., GPT-3.5 Turbo): Polished and practical for specific applications.

Differences Between Base and Fine-Tuned Models

  • Base models do word prediction based on vast text corpuses.
  • Example of base model response:
    • Prompt: "Tell me how to fine-tune a model"
    • Response: Lists generic questions.
  • Example of fine-tuned model response:
    • Prompt: Same as above
    • Response: Provides specific steps to fine-tune a model.

Benefits of Fine-Tuning

  • Fine-tuned models can outperform larger base models.
  • Example: OpenAI's Instruct GPT with fewer parameters performed better than GPT-3.

Methods of Fine-Tuning

1. Self-Supervised Learning

  • Train model on a curated corpus for specific applications.
  • Example: Fine-tuning GPT-3 with personal writing style data.

2. Supervised Learning

  • Use input-output pairs for training.
  • Example: Question-answer pairs for better answering.
  • Requires prompt templates for effective training.

3. Reinforcement Learning

  • OpenAI's method for Instruct GPT:
    1. Supervised fine-tuning.
    2. Train a reward model using human rankings of responses.
    3. Apply reinforcement learning (e.g., Proximal Policy Optimization).

Focus on Supervised Learning Approach

Steps to Fine-Tune a Model:

  1. Choose a Fine-Tuning Task: e.g., text summarization, classification.
  2. Prepare Training Dataset: Create input-output pairs.
  3. Choose Base Model: Select from existing models.
  4. Fine-Tune the Model: Use supervised learning techniques.
  5. Evaluate Model Performance: Assess the quality of the fine-tuned model.

Updating Model Parameters

Options for Fine-Tuning Parameters:

  1. Retrain All Parameters: High computational cost, not efficient.
  2. Transfer Learning: Freeze most parameters, fine-tune only the last layers.
  3. Parameter Efficient Fine-Tuning: Introduce additional trainable parameters while keeping most weights frozen (e.g., Low Rank Adaptation - LoRa).

Low Rank Adaptation (LoRa)

  • Efficiently adds trainable parameters to stabilize and improve fine-tuning.
  • More details provided about how LoRa works mathematically.

Example of Using LoRa for Fine-Tuning

  1. Choice of Base Model: DistilBERT for sentiment analysis.
  2. Label Mapping: Define positive/negative labels.
  3. Load Dataset: Use IMDB reviews dataset for training.
  4. Pre-process Data: Create tokenizer and tokenize datasets.
  5. Define Evaluation Metrics: Monitor model performance (accuracy).
  6. Training the Model: Set hyperparameters, use Trainer from HuggingFace.
  7. Evaluate Performance: Compare accuracy of the fine-tuned model.

Conclusion

  • Fine-tuning can significantly improve performance with fewer training examples.
  • Importance of addressing overfitting during the training process.
  • Encouragement to explore fine-tuning practices further.

Additional Resources

  • References to blogs and papers for further reading on model fine-tuning methods and applications.

Full transcript