dl-transformer-finetune

What This Skill Does

The dl-transformer-finetune skill is a specialized agentic tool designed to standardize and automate the creation of configuration-ready fine-tuning run plans for Large Language Models (LLMs) and transformer-based architectures. By leveraging this skill, developers can bridge the gap between model selection and training execution, ensuring that every experiment is documented, reproducible, and aligned with best practices for Hugging Face and PyTorch workflows. The skill automates the generation of hyperparameter configurations, evaluation cadence schedules, and model card skeletons, reducing manual configuration errors.

Installation

To integrate this skill into your OpenClaw environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/0x-professor/dl-transformer-finetune Ensure you have the latest version of OpenClaw installed to maintain compatibility with the required dependency scripts.

Use Cases

This skill is ideal for ML engineers and data scientists needing to maintain a repository of consistent training runs. Use it when:

• Launching a new task-specific fine-tuning job for sequence classification, token classification, or causal language modeling.
• Ensuring team-wide reproducibility by forcing explicit seeds and directory structures for training artifacts.
• Generating model documentation automatically to satisfy project compliance and transparency requirements.
• Comparing performance across different hyperparameter baselines defined in the internal documentation.

Example Prompts

1. "Build a finetune plan for a roberta-base model on the glue-mrpc dataset using a learning rate of 2e-5 and batch size of 16, ensure evaluation occurs every 500 steps."
2. "Generate a training run plan for a Llama-3-8B instruct finetuning, including a model card skeleton and specific hyperparameters for memory-efficient training on a single A100."
3. "Create a reproducible run config for token classification using distilbert, setting the seed to 42 and adding a rollback trigger if the validation loss increases for 3 consecutive epochs."

Tips & Limitations

To maximize the utility of this skill, consult the references/finetune-guide.md file provided in the repo for validated hyperparameter ranges. Always utilize the bundled scripts/build_finetune_plan.py to ensure your configuration schema matches the expected deployment pipelines. Note that while this skill generates the blueprint, it does not execute the actual training job; it is intended to hand off configurations to your CI/CD or training infrastructure. Ensure that all data paths are verified before submitting the generated configuration to your compute cluster to avoid IO errors during startup.