What is a finetune run plan?

A reproducible blueprint detailing model, dataset, task, hyperparameters, evaluation cadence, and artifact outputs for transformer fine-tuning.

How do I ensure reproducibility?

Use explicit seeds and organized output directories; rely on the build_finetune_plan.py script to generate deterministic plans and versioned artifacts.

What outputs are produced?

A run plan, a model-card skeleton, and configuration-ready artifacts suitable for training pipelines.

dl-transformer-finetune

npx machina-cli add skill 0x-Professor/Agent-Skills-Hub/dl-transformer-finetune --openclaw

Files (1)

SKILL.md

880 B

DL Transformer Finetune

Overview

Generate reproducible fine-tuning run plans for transformer models and downstream tasks.

Workflow

Define base model, task type, and dataset.
Set training hyperparameters and evaluation cadence.
Produce run plan plus model card skeleton.
Export configuration-ready artifacts for training pipelines.

Use Bundled Resources

Run scripts/build_finetune_plan.py for deterministic plan output.
Read references/finetune-guide.md for hyperparameter baseline guidance.

Guardrails

Keep run plans reproducible with explicit seeds and output directories.
Include evaluation and rollback criteria.

Source

git clone https://github.com/0x-Professor/Agent-Skills-Hub/blob/main/skills/dl-transformer-finetune/SKILL.mdView on GitHub

Overview

DL Transformer Finetune helps you generate reproducible fine-tuning run plans for transformer models across downstream tasks. It defines the base model, task type, and dataset, sets hyperparameters and evaluation cadence, and outputs a model-card skeleton plus configuration-ready artifacts for Hugging Face or PyTorch workflows.

How This Skill Works

You specify the base model, task type, and dataset to frame the plan. Then you configure training hyperparameters and how often to evaluate, and use scripts/build_finetune_plan.py to produce a deterministic run plan and model-card skeleton. The resulting artifacts are export-ready for training pipelines and include explicit seeds and output directories to guarantee reproducibility.

When to Use It

Setting up a Hugging Face or PyTorch finetuning job with fixed seeds for auditability
Reproducing a prior finetune run to verify results or compare tweaks
Benchmarking multiple datasets or tasks using the same hyperparameter baseline
Generating a model-card skeleton for releasing a finetuned transformer to HF Hub
Exporting configuration-ready artifacts for CI/CD pipelines and automation

Quick Start

Step 1: Define base model, task type, and dataset
Step 2: Configure hyperparameters and evaluation cadence; set explicit seeds
Step 3: Run scripts/build_finetune_plan.py to generate the deterministic plan and export artifacts

Best Practices

Pin seeds and use explicit output directories to guarantee repeatability
Version-control the base model, dataset, and hyperparameters used in the plan
Run the build_finetune_plan.py script to produce a deterministic plan output
Include explicit evaluation cadence and rollback criteria in the plan
Organize outputs with clear, task-specific naming and metadata

Example Use Cases

Finetune BERT-base-uncased on SST-2 with a deterministic plan and seed settings
DistilBERT on MRPC using fixed hyperparameters and a defined evaluation schedule
RoBERTa-large finetuning on a custom sentiment dataset with a PyTorch trainer and plan
Generate a Hugging Face model-card skeleton for a new task release with reproducible config
Export CI-ready finetune artifacts that can be consumed by automated training pipelines

Frequently Asked Questions

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