Do I need a specific Hugging Face subscription to use this skill?

Yes, using this skill requires a Hugging Face PRO subscription or organizational compute access to utilize the Hugging Face Jobs infrastructure.

Can this skill handle models larger than 7B parameters?

Absolutely. The skill provides hardware mappings for models up to 70B, recommending instances like A100 80GB or H100 for higher parameter counts.

How does the cost estimation work?

The skill includes an estimation script that calculates projected costs based on your total dataset tokens and the hourly rate of the selected Hugging Face GPU instance.

Is Weights & Biases (WandB) mandatory for training?

No, WandB integration is optional. It is supported for users who want advanced experiment tracking, but training can proceed without it.

Hugging Face Training Manager

Name: Hugging Face Training Manager
Author: chrisvoncsefalvay

bychrisvoncsefalvay

•

Data Science & ML

Simplifies the deployment and management of Unsloth fine-tuning jobs on Hugging Face cloud GPUs.

About

The funsloth-hfjobs skill enables developers to launch and monitor large language model (LLM) fine-tuning tasks specifically on Hugging Face Jobs infrastructure. It automates the transition from local notebooks to cloud-ready PEP 723 scripts, provides detailed hardware recommendations based on model size (7B to 70B), and includes built-in cost estimation to manage GPU budgets effectively. By integrating Unsloth's performance with Hugging Face's compute, this skill provides a streamlined workflow for professional model training with optional Weights & Biases monitoring.

Key Features

Automated conversion of training notebooks to PEP 723 scripts for cloud compatibility
Integrated token-based cost estimation specifically for Hugging Face Jobs platform
4 GitHub stars
Hardware-to-model mapping for optimized GPU selection from A10G to H100
Streamlined job lifecycle management from creation to artifact retrieval via Hub
Built-in support for Weights & Biases (WandB) real-time training monitoring

Use Cases

Fine-tuning Llama-3 or Mistral models on cloud GPUs without manual server configuration
Monitoring long-running training jobs through real-time logs and visual dashboards
Estimating and managing training costs for large-scale LoRA or SFT tasks

About

Key Features

Automated conversion of training notebooks to PEP 723 scripts for cloud compatibility
Integrated token-based cost estimation specifically for Hugging Face Jobs platform
4 GitHub stars
Hardware-to-model mapping for optimized GPU selection from A10G to H100
Streamlined job lifecycle management from creation to artifact retrieval via Hub
Built-in support for Weights & Biases (WandB) real-time training monitoring

Use Cases

Fine-tuning Llama-3 or Mistral models on cloud GPUs without manual server configuration
Monitoring long-running training jobs through real-time logs and visual dashboards
Estimating and managing training costs for large-scale LoRA or SFT tasks