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

0•

数据科学与机器学习

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

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.

主要功能

010 GitHub stars

02Supervised fine-tuning for task-specific optimization and output formatting

03Automated Boto3 implementation for job creation, monitoring, and model deployment

04Model distillation to transfer capabilities from teacher models to faster student models

05Reinforcement fine-tuning (RLHF/RLAIF) for brand alignment and hallucination reduction

06Continued pre-training to build deep domain knowledge from unlabeled text

使用场景

01Optimizing Claude for specialized medical, legal, or technical document classification

02Adapting AI models to follow strict corporate brand voices and industry terminology

03Distilling Claude 3.5 Sonnet capabilities into Claude 3 Haiku for lower latency and reduced costs

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add adaptationio/skrillz bedrock-fine-tuning

For use in Claude.ai and ChatGPT

Download Skill