funsloth-hfjobs_skill

Overview

This skill is a training manager that launches Unsloth fine-tuning jobs on Hugging Face Jobs with optional Weights & Biases monitoring. It packages a PEP 723 script, estimates cost, selects appropriate GPU types, and streamlines job submission, monitoring, and artifact retrieval. The tool is designed to make cloud GPU fine-tuning fast and repeatable for models from small to very large.

How this skill works

You convert your training notebook into a PEP 723-compatible Python script and include required dependencies and an optional WandB init. The skill helps pick the right GPU class, runs a cost estimate, generates a HF Jobs config, and submits the job via the Hugging Face CLI. It then provides commands for job status, live logs, WandB tracking, and downloading artifacts from the Hub when the run finishes.

When to use it

• You need to fine-tune models on Hugging Face cloud GPUs rather than local hardware.
• You want automated cost estimation before launching experiments.
• You need integrated WandB monitoring for metrics and visualizations.
• You require a repeatable PEP 723 script and job config for production training.
• You want simple commands to monitor logs and retrieve model artifacts.

Best practices

• Choose GPU based on model size: A10G for 7–14B, A100 40GB for 14–34B, A100 80GB or H100 for larger models.
• Convert notebooks to PEP 723 script format and pin critical dependencies.
• Enable WandB only if you have an API key set in the environment and need live dashboards.
• Estimate costs with the provided estimator before committing long runs.
• Add checkpointing and smaller batch sizes to avoid OOM and reduce wasted spend.

Example use cases

• Fine-tune a 13B model using an A100 40GB instance with WandB experiment tracking.
• Run multiple short hyperparameter sweeps on A10G to find optimal learning rates cost-effectively.
• Migrate a local Colab notebook into a reproducible HF Jobs script for team training.
• Recover artifacts and model checkpoints from a completed HF Jobs run to publish on the Hub.
• Diagnose training failures by streaming job logs and checking common error resolutions.