finetuning
Generates code that fine-tunes a base model using SageMaker serverless training jobs. Use when the user says "start training", "fine-tune my model", "I'm ready to train", or when the plan reaches the finetuning step. Supports SFT, DPO, RLVR, and RLAIF trainers, including RLVR Lambda reward function and RLAIF custom prompt creation.
Profile →npx skills add awslabs/agent-plugins --skill finetuning --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Prerequisites Before starting this workflow, verify: 1. A `use_case_spec.md` file exists - If missing: Activate the `use-case-specification` skill first, then resume - DON'T EVER offer to create a use case spec without activating the use-case-specification skill. 2. A fine-tuning technique (SFT, DPO, RLVR, RLAIF, or CPT/RFT (for Nova)) and base model have already been selected - If missing: Activate the `model-selection` and/or `finetuning-technique` skills to collect what's missing, then resume - Don't make recommendations on the spot. You MUST activate the appropriate skill. 3. A base model name available on SageMakerHub has been identified - If missing: Activate the `model-selection` skill to get it - **Important:** Only use the model name that `model-selection` retrieves, as it may differ from other commonly used names for the same model 4. The SDK environment has been verified (SDK version, region, execution role) - If not done: Activate the `sdk-getting-started` skill first, then resume 5. A training dataset uploaded to a bucket in the environment's default region. - If not met: Help the user upload the dataset to the correct S3 --- # Critical Rules ## Code Generation Rules
- Code Generation Rules
- User Communication Rules
- 1. Code Generation Setup
- 1.1 Directory Setup
- 1.2 Select Code Template
- 1.3 Generate Code
- 1.4 Auto-Generate Configuration Values
- 2.1 Check Reward Function Status
- 2.2 Generate Reward Function From Template
- 2.3 Set CUSTOMREWARDFUNCTION value
- 3. RLAIF (for RLAIF only, skip this section if technique is not RLAIF)
- 4. EULA review and acceptance
- 5. Post-Generation
- 6. Continuous Customization
What does the finetuning skill do?
Generates code that fine-tunes a base model using SageMaker serverless training jobs. Use when the user says "start training", "fine-tune my model", "I'm ready to train", or when the plan reaches the finetuning step. Supports SFT, DPO, RLVR, and RLAIF trainers, including RLVR Lambda reward function and RLAIF custom prompt creation.
How do I install it?
Run `npx skills add awslabs/agent-plugins --skill finetuning --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From awslabs/agent-plugins, a repository with 850 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.