Customizing models for enhanced results: Fine-tuning in Amazon Bedrock (AIM357)

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Fine-Tuning and Model Customization

Introduction

• Fine-tuning and model customization are hot topics in the field of foundation models and small language models.
• This session will cover the basics of fine-tuning and two cool applications: with Hugging Face models and with Meta models.

What is Fine-Tuning?

• Fine-tuning is the process of taking a pre-trained model and customizing it with your own data.
• The pre-trained model is typically obtained by training on a large corpus of unlabeled data, which provides a baseline level of capabilities.
• The fine-tuning process involves using labeled, task-specific examples (prompt-completion pairs) to further train the base model and make it specific to your use case.

The Fine-Tuning Lifecycle

1. Use Case Definition: Identify the specific task you want to solve, e.g., document summarization or text-to-SQL conversion.
2. Data Preparation: Clean, enrich, and de-duplicate the data to ensure high quality. High-quality data is a key differentiator for successful fine-tuning.
3. Model Customization: Use frameworks and services like Amazon SageMaker and Amazon Bedrock to fine-tune the base model using the prepared data.
4. Monitoring: Monitor the fine-tuning process and adjust hyperparameters as needed.
5. Evaluation: Evaluate the fine-tuned model on a blind test set to assess its performance on the target task.

When to Fine-Tune?

• Fine-tuning is one of several techniques, along with prompt engineering and retrieval-augmented generation (RAG), that can be used to improve model performance.
• Fine-tuning is generally more effective for:
  • Adjusting the model's tone or personality
  • Teaching the model a completely new skill (e.g., text-to-SQL)
  • Incorporating new knowledge that the base model doesn't have
• Fine-tuning may be less effective for generalizing the model to multiple similar tasks.

Key Considerations for Fine-Tuning

• Effectiveness when the base model is already familiar with the new concepts
• Promising few-shot results with the base model
• Complexity of the prompt engineering required to achieve desired outcomes

Amazon Bedrock Features for Fine-Tuning

Bedrock Fine-Tuning

• Provides a simple interface to fine-tune base models using a JSON-formatted dataset.
• Allows controlling key hyperparameters like learning rate, epoch count, and batch size.
• Supports early stopping based on validation metrics.

Continued Pre-Training

• Rare but possible to pre-train models from scratch on large, high-quality datasets.
• Requires significant time and resources, e.g., $20 million to train a 1 trillion parameter model.

Custom Model Import

• Allows importing custom fine-tuned models from external sources (e.g., SageMaker, Hugging Face) to use with Bedrock's inference APIs.
• Enables flexibility and cost-efficiency by leveraging your own fine-tuned models.

Bedrock Model Distillation

• Generates prompt-completion pairs using a larger "teacher" model to fine-tune a smaller "student" model.
• Can leverage production logs as the dataset for distillation.
• Allows efficiently training smaller models without requiring a large initial dataset.

Customizing Anthropic's Chatgpt-3 (Hykoo)

Hykoo Fine-Tuning Requirements

• Data must be in JSON Lines format, following the Message API structure.
• Each line represents a training record with a system prompt (optional but recommended) and alternating user and assistant messages.

Hykoo Fine-Tuning Parameters

• Required parameters: epoch count, batch size, learning rate
• Optional but recommended: early stopping threshold and patience

Hykoo Fine-Tuning Performance

• Example fine-tuning on the TED-QA dataset achieved 91.2% accuracy, outperforming the base Hykoo and advanced ChatGPT-3.5 models.
• Fine-tuning also reduced the average output token length by 35%, improving efficiency and reducing costs.

Customizing Meta's LLaMA Models

LLaMA Fine-Tuning Use Cases

• Customer service chatbots
• Content generation
• Compliance and regulatory analysis
• Financial data analysis

Key Considerations for LLaMA Fine-Tuning

• Importance of a well-curated, diverse dataset
• Distinction between domain-specific and custom datasets
• Recommended starting points for hyperparameters (learning rate, batch size)

LLaMA Fine-Tuning Demo

• Demonstrated fine-tuning an 8B LLaMA model on the AQuA dataset for solving algebraic word problems.
• Showed performance improvement of the fine-tuned model compared to the base 8B model.
• Highlighted the advantages of fine-tuning, including increased accuracy and reduced token usage.