How does model distillation help with costs?

Distillation transfers knowledge from a large 'teacher' model to a smaller 'student' model, allowing you to maintain high quality while reducing inference costs by 50-70%.

Do I need labeled data for all customization types?

No. Supervised fine-tuning requires labeled input-output pairs, but continued pre-training works with unlabeled raw text, and distillation only requires a prompt dataset.

What is the benefit of reinforcement fine-tuning?

Reinforcement fine-tuning uses human or AI feedback to align models with specific values or brand voices, resulting in up to 66% accuracy gains in specialized workflows.

Which models can I customize with this skill?

The skill supports customization for the Claude 3 family (Sonnet, Haiku, Opus), Amazon Titan Text and Embeddings, Cohere Command, and AI21 Jurassic-2.

Amazon Bedrock Model Customization

Name: Amazon Bedrock Model Customization
Author: adaptationio

byadaptationio

데이터 과학 및 ML

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

소개

This skill provides a comprehensive framework for adapting Amazon Bedrock foundation models, including the Claude 3 family, to meet specific domain-specific requirements. It streamlines the entire customization lifecycle—from preparing JSONL training datasets and managing S3 bucket uploads to launching training jobs and monitoring performance. Whether you are seeking to adapt Claude to proprietary technical data, align responses with reinforcement fine-tuning for 2025-standard accuracy gains, or reduce costs by distilling large models into smaller ones, this skill provides the necessary patterns and automation to implement production-grade AI customization.

주요 기능

Continued pre-training for deep domain knowledge adaptation
0 GitHub stars
Reinforcement fine-tuning (RLHF) for brand alignment and safety
Automated S3 data integration and training job monitoring
Supervised fine-tuning for task-specific accuracy improvements
Knowledge distillation to reduce costs and latency using teacher-student models

사용 사례

Adapting Claude 3 models to specialized medical, legal, or financial datasets
Aligning model output styles to specific corporate brand voices and values
Distilling the intelligence of Claude 3.5 Sonnet into Haiku for cheaper inference

소개

주요 기능

Continued pre-training for deep domain knowledge adaptation
0 GitHub stars
Reinforcement fine-tuning (RLHF) for brand alignment and safety
Automated S3 data integration and training job monitoring
Supervised fine-tuning for task-specific accuracy improvements
Knowledge distillation to reduce costs and latency using teacher-student models

사용 사례

Adapting Claude 3 models to specialized medical, legal, or financial datasets
Aligning model output styles to specific corporate brand voices and values
Distilling the intelligence of Claude 3.5 Sonnet into Haiku for cheaper inference