Move a large model's behavior to a low-latency small model.
Evaluate quality and training-data risk.
Choose two considerations.
Evaluate the small model's quality, safety, format, latency, and cost and govern teacher outputs used as adaptation data.
Detailed explanation
A smaller model may not preserve every capability.
A smaller model may not preserve every capability.
Generated training examples can contain bias or secrets.
Generated training examples can contain bias or secrets.
Capabilities can differ.
Capabilities can differ.
Errors and sensitive content can be inherited.
Errors and sensitive content can be inherited.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 3.1のモデル適応・評価と責任あるAIを確認する。Expected result
蒸留の利点と、能力・安全性・データ品質の再評価を説明できる。Key points
- Small model
- Distillation data
- Reevaluation
Notes
- Environment: AWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
- Command output formatting can vary slightly by distribution or tool version.
- Run the example in a temporary directory or process when possible.
Foundation review
Read the scope first
Check whether the command acts on the current shell, a new process, an existing process, or a file.
Verify the observable result
Use the supplied command and compare the output with the expected result.