What data format is required for customization?

Most customization jobs require data in JSONL format, containing either task pairs (prompt/completion), text blocks, or preference pairs for reinforcement learning.

What models can I fine-tune with this skill?

You can customize Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku, and various Amazon Titan or Cohere models depending on regional availability.

What is the difference between fine-tuning and distillation?

Fine-tuning adapts a model to specific tasks using labeled data, while distillation transfers knowledge from a large 'teacher' model to a smaller, more cost-effective 'student' model.

How much accuracy improvement can I expect?

While results vary, supervised fine-tuning typically yields 20-40% gains, and 2025 reinforcement fine-tuning techniques have shown up to 66% accuracy improvements in specialized workflows.

Do I need to manage infrastructure for these jobs?

No, Amazon Bedrock is a fully managed service. This skill provides the code to trigger and monitor these managed jobs without requiring you to provision servers.

Bedrock Model Customization

Name: Bedrock Model Customization
Author: adaptationio

byadaptationio

Data Science & ML

Adapts Amazon Bedrock foundation models through fine-tuning, continued pre-training, reinforcement learning, and model distillation.

About

This skill empowers developers to customize Amazon Bedrock models, including Claude 3.5 Sonnet and Claude 3 Haiku, for specialized tasks and industry-specific domains. It provides comprehensive patterns for supervised fine-tuning, domain adaptation through continued pre-training, alignment via reinforcement fine-tuning (RLHF/RLAIF), and cost-efficient knowledge distillation from larger models to smaller ones. By leveraging this skill, users can improve model accuracy on proprietary data, adapt responses to specific brand voices, and optimize inference performance for production workflows while utilizing automated job monitoring and deployment workflows.

Key Features

0 GitHub stars
Supervised fine-tuning for task-specific optimization and output formatting
Automated Boto3 implementation for job creation, monitoring, and model deployment
Model distillation to transfer capabilities from teacher models to faster student models
Reinforcement fine-tuning (RLHF/RLAIF) for brand alignment and hallucination reduction
Continued pre-training to build deep domain knowledge from unlabeled text

Use Cases

Optimizing Claude for specialized medical, legal, or technical document classification
Adapting AI models to follow strict corporate brand voices and industry terminology
Distilling Claude 3.5 Sonnet capabilities into Claude 3 Haiku for lower latency and reduced costs

About

Key Features

0 GitHub stars
Supervised fine-tuning for task-specific optimization and output formatting
Automated Boto3 implementation for job creation, monitoring, and model deployment
Model distillation to transfer capabilities from teacher models to faster student models
Reinforcement fine-tuning (RLHF/RLAIF) for brand alignment and hallucination reduction
Continued pre-training to build deep domain knowledge from unlabeled text

Use Cases

Optimizing Claude for specialized medical, legal, or technical document classification
Adapting AI models to follow strict corporate brand voices and industry terminology
Distilling Claude 3.5 Sonnet capabilities into Claude 3 Haiku for lower latency and reduced costs