Fine-Tuning Llama 3 on a Custom Dataset: Training LLM for a RAG Q&A Use Case on a Single GPU

Introduction

This tutorial outlines the process of fine-tuning the Llama 3 8B Instruct model on a custom dataset tailored for a Retrieval-Augmented Generation (RAG) Q&A application, specifically focusing on financial data. The guide will walk you through each stage, from dataset creation to model evaluation, ensuring you can replicate the process using your own data.

Chapter 1: Why Fine-Tuning

Fine-tuning a language model allows you to improve its performance for specific tasks. In this tutorial, we will use the Llama 3 8B Instruct model to enhance its capabilities for answering financial questions. A well-fine-tuned model can yield more accurate and contextually relevant responses compared to a base model.

Chapter 2: Fine-Tuning Process Overview

Steps to Follow

1. Build a Dataset: Create a dataset from custom prompts using a JSON file.
2. Evaluate Base Model: Assess the initial performance of the base model.
3. Set Up Adapter: Use a LoRA adapter to fine-tune on top of the original model.
4. Training and Monitoring: Train the model and monitor its performance throughout the process.
5. Evaluation: Evaluate the fine-tuned model against a test set.
6. Model Deployment: Push the newly trained model to Hugging Face Hub.

Chapter 3: Dataset Creation

Steps to Create a Custom Dataset

1. Download Financial Q&A Dataset: Use the dataset available on Hugging Face, which contains approximately 7,000 examples of questions, contexts, and answers.
2. Convert to DataFrame: Transform the dataset into a Pandas DataFrame for easier manipulation.
3. Format Examples: Create a function to format data into a suitable structure for training.
4. Token Count: Analyze token counts to ensure they are below a defined threshold (e.g., 512 tokens).
5. Split Dataset: Divide your data into training, validation, and test sets.

Practical Tip

• Remove any rows with excessive token counts to maintain optimal training speeds.

Chapter 4: Establishing Baseline Performance

Steps to Evaluate the Base Model

1. Set Up Pipeline: Create a prediction pipeline using the original model and tokenizer.
2. Generate Predictions: Run predictions on a sample from the test dataset.
3. Record Performance: Note the quality and verbosity of the model's responses.

Common Pitfall

• Ensure that the test data is representative of the types of questions you expect the model to answer.

Chapter 5: Training on Completions

Steps for Fine-Tuning

1. Configure the Training Loop: Adjust the training parameters to focus on the output generated by the model rather than the input.
2. Use Collator Functions: Mask tokens to optimize training efficiency.
3. Set Up LoRA: Target specific linear layers in the model for fine-tuning.
4. Train the Model: Initiate the training process, keeping track of performance metrics.

Chapter 6: Training Configuration

Key Configuration Points

• Set maximum tokens to 512 for input.
• Utilize gradient accumulation for efficient resource management.
• Monitor training and validation losses to avoid overfitting.

Example Code Snippet

from transformers import Trainer

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=validation_dataset
)
trainer.train()

Chapter 7: Loading Model and Pushing to Hugging Face Hub

Steps to Deploy the Model

1. Merge Model Components: Combine the fine-tuned model with the adapter.
2. Upload to Hugging Face: Push the model and tokenizer to the Hugging Face repository, ensuring they are split into manageable sizes.

Practical Tip

• Monitor the upload process to ensure that all components are correctly uploaded.

Chapter 8: Model Evaluation

Steps for Comparison

1. Download Fine-Tuned Model: Pull the newly trained model from Hugging Face.
2. Run Predictions: Generate predictions using the fine-tuned model.
3. Compare Results: Analyze the differences in performance between the fine-tuned and base models.

Key Metrics to Analyze

• Response accuracy
• Verbosity and relevance of answers

Conclusion

Fine-tuning the Llama 3 8B Instruct model can significantly enhance its performance on specific tasks such as financial Q&A. By carefully preparing your dataset, setting up the training environment, and evaluating the model effectively, you can achieve better results than with the base model. For ongoing improvements, consider experimenting with different training parameters and additional fine-tuning sessions.