Finetuning Technique
AWS LabsOfficialPublished by AWS Labs
- Skill
- Workflow
- Data and AI
- Apache-2.0
- Low risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill assists users in choosing the most appropriate fine-tuning technique—SFT, DPO, RLVR, or RLAIF—based on their specific use case requirements. It ensures technical feasibility by validating the selected technique against the capabilities of a previously chosen base model, preventing incompatible configurations.
What it does
- Recommends fine-tuning techniques based on task requirements and data characteristics.
- Validates chosen techniques against the specific recipes supported by the selected base model.
- Provides clear distinctions between SFT, DPO, RLVR, and RLAIF methodologies.
- Offers guidance on when to pivot to a different base model if the desired technique is unsupported.
- Summarizes the final configuration of the base model and chosen fine-tuning approach.
When to use it
- When you have decided to fine-tune a model but are unsure which technique best fits your task.
- When you need to verify if a specific fine-tuning method is compatible with your chosen base model.
- When you need to switch your fine-tuning approach and require validation for the new method.
Advantages
- Ensures alignment between the chosen fine-tuning method and the model's architectural capabilities.
- Reduces trial-and-error by providing a structured selection guide for different AI training paradigms.
- Prevents deployment failures by checking recipe availability before starting the training process.
Usage examples
- “I have a base model selected and need to choose a fine-tuning technique for a code generation task.”
- The agent will recommend RLVR due to the programmatic verifiability of code and then validate if the selected model supports RLVR recipes.
- “Can I use DPO for my summarization task with this model?”
- The agent will check the model's available recipes to confirm if DPO is supported and provide guidance on whether DPO is the right fit compared to RLAIF.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/awslabs/finetuning-technique/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip finetuning-technique.zip -d .agents/skills
Documentation by AWS Labs
Finetuning Technique
Guides the user through selecting a fine-tuning technique based on their use case and validates compatibility with the selected model.
When to Use
User has decided to finetune and needs to choose a technique
User wants to change their finetuning technique
Technique needs to be validated against a selected model
Prerequisites
A base model has been selected (via model-selection skill). The model name and hub must be known.
A
use_case_spec.mdfile exists. If not, activate the use-case-specification skill to generate it first.
Workflow
Step 1: Determine Finetuning Technique
Consult references/finetune_technique_selection_guide.md to recommend the best-fit technique based on the use case and the user's needs (SFT, DPO, RLVR, RLAIF).
Present the recommendation and reasoning to the user. Ask if they'd like to go with the recommendation or prefer a different technique.
Step 2: Validate Technique Availability
Once the user confirms a technique, retrieve the finetuning techniques available for the selected model by running:
python finetuning-technique/scripts/get_recipes.py <model-name> <hub-name>This returns only the techniques the model actually supports, filtered to SFT, DPO, RLVR, and RLAIF. Only these four techniques are supported — ignore any other techniques even if the model's recipes include them.
If the chosen technique is available for the model, proceed to Step 3.
If the chosen technique is not available for the model, explain that the selected model does not support it on SageMaker and offer to go back to model-selection to pick a different model that supports the chosen technique.
Step 3: Confirm Selections
Present a summary to the user:
Here's what we've selected:
- Base model: [model name]
- Fine-tuning technique: [SFT/DPO/RLVR/RLAIF]References
references/finetune_technique_selection_guide.md— Technique guidance (SFT/DPO/RLVR/RLAIF)
Files
- ATTRIBUTION.mdAdded by AutoGineer924 B
- LICENSELicence file added by AutoGineer9.9 KiB
- NOTICELicence file added by AutoGineer89 B
- SKILL.md2.4 KiB
- references/finetune_technique_selection_guide.md2.3 KiB
- scripts/get_recipes.pyScript748 B
LICENSE
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
Security
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Attribution and licence
- Published by
- AWS Labs
- Publisher
- AWS Labs (Official)
- Source
- awslabs/agent-plugins · plugins/sagemaker-ai/skills/finetuning-technique · commit fbefb37
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- NOTICE (added by AutoGineer from NOTICE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by AWS Labs or AWS Labs.
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The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download finetuning-technique.zip