A specialized skill for generating reproducible transformer fine-tuning run plans and model card skeletons for Hugging Face or PyTorch workflows.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install dl-transformer-finetune
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install dl-transformer-finetune using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is DL Transformer Finetune?
The DL Transformer Finetune skill provides a structured approach to managing machine learning model training cycles. It simplifies the complex process of defining task settings, hyperparameters, and output requirements into a cohesive run plan. By leveraging this tool within the Openclaw Skills ecosystem, developers can ensure their transformer fine-tuning projects are consistent, documented, and ready for deployment.
This skill bridges the gap between raw model training and professional MLOps practices. It focuses on producing configuration-ready artifacts that can be directly integrated into training pipelines, reducing manual errors and improving experiment tracking for both researchers and engineers.
DL Transformer Finetune Use Cases
- Streamlining the setup of Hugging Face or PyTorch training jobs.
- Generating standard model card documentation for new fine-tuned models.
- Creating reproducible experiment plans with fixed seeds and output paths.
- Standardizing hyperparameter selection across different transformer-based tasks.
How DL Transformer Finetune Works
- The user specifies the base model, target task type (e.g., classification, NER), and the dataset to be used.
- The skill assists in defining critical training hyperparameters and setting an evaluation cadence.
- A comprehensive run plan and model card skeleton are automatically generated.
- Technical artifacts are exported, making them ready for immediate use in training pipelines.
DL Transformer Finetune Setup
To get started with this skill, ensure your environment is prepared for Python-based ML workflows. Use the following steps to initialize the plan generator:
# Navigate to the skill directory
cd skills/dl-transformer-finetune
# Run the plan builder script
python scripts/build_finetune_plan.py
You should also review the references/finetune-guide.md file for baseline guidance on hyperparameter tuning available in Openclaw Skills.
DL Transformer Finetune Data Schema & Taxonomy
The skill organizes output into structured directories to maintain reproducibility and technical clarity:
| Artifact | Description | Format |
|---|---|---|
| Run Plan | Detailed JSON/YAML configuration for training | .json / .yaml |
| Model Card | Documentation skeleton for Hugging Face Hub | .md |
| Metadata | Explicit seeds, task types, and versioning info | .json |
| Guides | Hyperparameter baseline references | .md |
DL Transformer Finetune Advanced Features
- Automated model card skeleton generation for rapid documentation.
- Integration with deterministic script execution via the bundled build script.
- Explicit guardrails for evaluation and rollback criteria to ensure model quality.
- Support for custom output directories to maintain organized training logs within Openclaw Skills.
SKILL.md
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