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dl-transformer-finetune
Build transformer fine-tuning run plans with task settings, hyperparameters, and model-card outputs. Use for repeatable Hugging Face or PyTorch finetuning workflows.
Installation
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install 0x-professor/dl-transformer-finetuneExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
Instructions
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.pyfor deterministic plan output. - Read
references/finetune-guide.mdfor hyperparameter baseline guidance.
Guardrails
- Keep run plans reproducible with explicit seeds and output directories.
- Include evaluation and rollback criteria.
Informations sur la source
- Source
- github
- Repository
- openclaw/skills
- Path
- skills/0x-professor/dl-transformer-finetune
- Version
- —
- Commit
- 45e7379c5ada58d3430d4bf810b53b7060456e84
- Source de classification
- rules
- Statut
- active
- Visibilité
- public
Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.
Contenu extrait
SKILL.md
# DL Transformer Finetune ## Overview Generate reproducible fine-tuning run plans for transformer models and downstream tasks. ## Workflow 1. Define base model, task type, and dataset. 2. Set training hyperparameters and evaluation cadence. 3. Produce run plan plus model card skeleton. 4. 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.
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