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name: model-customization description: "Use SageMaker Python SDK v3 foundation-model customization, evaluation, AI Registry assets, recipe overrides, data mixing, notifications, and Agentic RFT workflows." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

SageMaker Python SDK v3 Model Customization Sub-skill

Use this sub-skill for foundation model customization with the specialized SFTTrainer, DPOTrainer, RLVRTrainer, RLAIFTrainer, CPTTrainer, and MultiTurnRLTrainer APIs, plus evaluation jobs, AI Registry datasets and evaluators, Nova data mixing, recipe overrides, notifications, and job monitoring.

Trigger phrases

Read this sub-skill when the user asks to:

• fine-tune, customize, or align a foundation model with SageMaker v3
• use SFTTrainer, DPOTrainer, RLVRTrainer, RLAIFTrainer, CPTTrainer, or MultiTurnRLTrainer
• run Nova data mixing, recipe overrides, or get_resolved_recipe()
• validate a training or evaluation config with dry_run=True
• create or use AI Registry DataSet or Evaluator assets
• launch benchmark, custom-scorer, LLM-as-judge, InspectAI, or multi-turn RL evaluations
• configure SNS/EventBridge notifications for training jobs
• inspect show_metrics(), stream_logs(), or AgentRFTJob artifacts

When not to read this sub-skill

• Generic training jobs, local training, distributed training, HPO, or JumpStart training: use ../training/SKILL.md.
• Deploying a customized model to an endpoint, local container, or Bedrock serving path: use ../serving/SKILL.md.
• Session/default-bucket/image URI/resource-chain questions: use ../core-resources/SKILL.md.
• Pipeline orchestration, registry workflows outside evaluation assets, feature store, or governance steps: use ../mlops/SKILL.md.

Short workflow

1. Identify the task family: SFT, DPO, RLVR, RLAIF, CPT, or MultiTurnRL.
2. Pick the compute path:
   • serverless when compute=None
   • TrainingJobCompute/Compute for serverful SMTJ
   • HyperPodCompute for cluster-backed jobs
   • CPT is HyperPod-only
   • MultiTurnRL uses agent_env and AgentRFTJob, not TrainingJobCompute
3. Use the right asset type:
   • DataSet for registered training/evaluation data
   • Evaluator for reward functions, reward prompts, or judge assets
   • DataMixingConfig for Nova data mixing
4. Resolve recipe and validation intent early with get_resolved_recipe() and dry_run=True before launching a chargeable job.
5. Keep accept_eula=True and explicit role / region / S3 outputs ready for gated models and cloud execution.
6. After submission, inspect logs and metrics with stream_logs() and show_metrics(); for MTRL inspect the returned AgentRFTJob.
7. When the task becomes deployment, hand it to ../serving/SKILL.md.

Reference map

• references/foundation-model-customization.md: specialized trainer APIs, compute choices, recipes, data mixing, dry-run, logs, and model-specific operating patterns.
• references/evaluation-and-ai-registry.md: evaluator classes, evaluation execution objects, AI Registry DataSet and Evaluator assets, and multi-turn RL artifacts.
• references/recipes-data-mixing-notifications.md: recipe precedence, get_resolved_recipe(), DataMixingConfig, notifications, metrics, and log streaming after restart.
• references/troubleshooting.md: region, credentials, role, EULA, model access, data validation, recipe, notification, and evaluation failure recovery.

Quick API map

• Training: SFTTrainer, DPOTrainer, RLVRTrainer, RLAIFTrainer, CPTTrainer, MultiTurnRLTrainer, TrainingType, CustomizationTechnique.
• Data and assets: DataMixingConfig, DataSet, Evaluator.
• Evaluation: BenchMarkEvaluator, CustomScorerEvaluator, LLMAsJudgeEvaluator, InspectAIEvaluator, MultiTurnRLEvaluator, EvaluationPipelineExecution.
• Monitoring: show_metrics(), stream_logs(), plot_training_metrics(), AgentRFTJob.get_training_metrics().

Guardrails

• Use v3 imports only.
• Do not hardcode account IDs, region names, role ARNs, bucket names, subnets, or credentials in public guidance.
• Keep deployment guidance out of this sub-skill; route customized-model serving to the sibling serving sub-skill.
• If you only need general ModelTrainer flow, leave this sub-skill and use the training sub-skill instead.