What problem does it solve?
This Skill provides a comprehensive operational framework for running training and fine-tuning jobs on Hugging Face Jobs, mitigating common failure modes and ensuring efficient, effective training runs.
Core Features & Use Cases
- Pre-Flight Checklist: A detailed checklist to ensure all prerequisites are met before submitting a job.
- GPU Hardware Sizing: Guidelines for selecting appropriate GPU hardware based on model size and training requirements.
- OOM Recovery: Strategies for handling out-of-memory errors without changing the training task.
- Sandbox-First Development: Encourages testing in a CPU/GPU sandbox to catch potential issues early.
- push_to_hub Discipline: Ensures that all job outputs are durable and retrievable.
- Training Logging: Best practices for logging that survives the logs tab.
- Submit-One-Then-Batch: Technique for submitting hyperparameter sweep jobs to avoid early failures.
- Dataset Format by Training Method: Guidance on dataset format requirements for different training methods.
- Prebuilt Kernels: Recommendations for using prebuilt attention kernels instead of compiling from source.
- Decision Table: When to use Hugging Face Jobs versus alternative platforms.
Quick Start
Use the hf-jobs-workflow skill to set up a training job on Hugging Face Jobs, ensuring all best practices are followed.
