Summary of "Fine-tuning Large Language Models (LLMs) | w/ Example Code"

Overview: The video, presented by Shaw, is part of a series on practical use of large language models (LLMs). It focuses on fine-tuning pre-trained LLMs to improve performance on specific tasks beyond prompt engineering. Fine-tuning adjusts internal model parameters to specialize a base model like GPT-3 for particular applications, enhancing alignment and output quality.

Key Technological Concepts & Analysis:

What is Fine-tuning?
- Fine-tuning involves training one or more internal parameters (weights/biases) of a pre-trained model.
- Example: Transforming GPT-3 (a “raw diamond”) into a fine-tuned model like GPT-3.5 Turbo or InstructGPT, which are more practical for applications such as ChatGPT.
- Base models predict next words based on large corpora but may generate generic or misaligned completions.
- Fine-tuned models generate more aligned, task-specific completions.
Advantages of Fine-tuning:
- Smaller fine-tuned models can outperform larger base models (e.g., OpenAI’s 1.3B parameter InstructGPT beating GPT-3’s 175B parameters).
- Enables better performance without requiring massive computational resources.
- Allows adaptation to niche tasks or specific styles (e.g., mimicking a particular author’s writing).
Methods of Fine-tuning:
- Self-Supervised Learning: Similar to base model training but on curated domain-specific corpora.
- Supervised Learning: Uses paired input-output datasets (e.g., question-answer pairs) to teach the model specific behaviors. Requires prompt templates to convert pairs into training prompts.
- Reinforcement Learning from Human Feedback (RLHF): Combines supervised fine-tuning with a reward model trained on human rankings of outputs, followed by reinforcement learning (e.g., PPO) to further optimize outputs. This approach was used for InstructGPT.
Supervised Fine-tuning Workflow:
- Choose a fine-tuning task (e.g., sentiment analysis, text summarization).
- Prepare a labeled dataset with input-output pairs.
- Select a base model (foundation or already fine-tuned).
- Fine-tune the model using supervised learning.
- Evaluate model performance using metrics such as accuracy.
Parameter Update Strategies:
- Full fine-tuning: Update all model parameters (computationally expensive for large models).
- Transfer learning: Freeze most parameters and fine-tune only the head (last layers).
- Parameter-efficient fine-tuning: Freeze all original parameters and add a small set of trainable parameters, drastically reducing training cost.
Parameter-efficient Fine-tuning with LoRA (Low-Rank Adaptation):
- LoRA adds trainable low-rank matrices (B and A) to frozen weight matrices instead of updating all weights.
- This reduces trainable parameters from millions to thousands by decomposing parameter updates into low-rank matrices.
- Example given: From 1 million trainable parameters to about 4,000 with LoRA.
- LoRA is effective and efficient for fine-tuning large models on limited hardware.

Practical Tutorial & Example Code:

Environment: Uses Hugging Face ecosystem (Transformers, Datasets, PEFT, Evaluate libraries), PyTorch, and NumPy.
Base Model: DistilBERT uncased (67 million parameters), chosen for its smaller size suitable for local machine fine-tuning.
Task: Sentiment analysis on IMDb movie reviews (binary classification: positive/negative).
Dataset: IMDb truncated dataset (1,000 training and 1,000 validation samples).
Data Processing:
- Tokenization of text inputs using Hugging Face AutoTokenizer.
- Padding/truncation to fixed sequence length.
- Use of a data collator for dynamic padding within batches to improve efficiency.
Evaluation Metric: Accuracy, computed by comparing model predictions (via logits) to true labels.
Baseline Evaluation: Unfine-tuned DistilBERT performs near chance (~50% accuracy).
Fine-tuning with LoRA:
- Defined LoRA config parameters (task type, intrinsic rank, learning rate, dropout, target modules).
- Applied LoRA to query layers, resulting in ~1 million trainable parameters (~2% of base model).
- Set training hyperparameters: learning rate = 0.001, batch size = 4, epochs = 10.
- Used Hugging Face Trainer API for training with evaluation after each epoch.
Results:
- Training loss decreased, accuracy improved.
- Validation loss increased, indicating some overfitting.
- Fine-tuned model showed improved sentiment classification on test examples.
Additional Notes: