Manage models, datasets, and Spaces on the Hugging Face Hub via CLI.
npx skills add https://github.com/graniet/kheish --skill huggingface-hub-graniet
Please help me install this Agent Skill. Skill: huggingface-hub Source: https://github.com/graniet/kheish/tree/main/skills/mlops/huggingface-hub Command: npx skills add https://github.com/graniet/kheish --skill huggingface-hub-graniet
Manually managing machine learning assets like models, datasets, and Spaces on the Hugging Face Hub is tedious, error-prone, and difficult to scale for teams or automated MLOps pipelines, especially when working with large files or recurring workflows.
Use the huggingface-hub skill to download the 'bert-base-uncased' model to your local environment and upload a sample inference script to your private Hugging Face Hub Space.
High-intent search queries and answers about installing and using this skill.
Automating Hugging Face Hub management involves using the Hub CLI to script repetitive MLOps tasks. This includes programmatically searching, downloading, and uploading models or datasets, alongside deploying inference endpoints, to eliminate manual web UI interaction.
Running SQL queries on dataset parquet files is supported through the Hugging Face Hub CLI. This allows you to filter large public datasets for specific training samples and streamline dataset curation without downloading entire repositories locally.
An HF_TOKEN is required for write operations like uploading models and deploying inference endpoints. You must install the official Hugging Face Hub CLI and authenticate with a valid token to enable automated repository management and team collaboration.
Pushing processed machine learning results to a private Space uses the Hub CLI for direct repository uploads. This automates file transfers to your private Hugging Face Hub repository, allowing teams to share assets efficiently without manual web uploads.
Bulk file transfer for Hugging Face Hub repositories supports large files within automated workflows. By leveraging the Hub CLI, you can execute bulk transfers and manage cloud storage buckets programmatically, avoiding the errors common in manual uploads.
carterwayneskhizeineManage Hugging Face Hub repositories for models, datasets, and Spaces via CLI.
BermudaLocalsExecute local GGUF inference and discover Hugging Face models with llama.cpp.
Design-System-ETDesign Kubeflow or Airflow DAGs for ML training workflows with MLflow tracking.
Brilly-BohyunManage models, datasets, and repositories on the Hugging Face Hub.
