Hugging Face just made life easier with the new hf CLI! huggingface-cli to hf With renaming the CLI, there are new features added like hf jobs. We can now run any script or Docker image on dedicated Hugging Face infrastructure with a simple command. It's a good addition for running experiments and jobs on the fly. To get started, just run: pip install -U huggingface_hub List of hf CLI Commands
Main Commands hf auth: Manage authentication (login, logout, etc.). hf cache: Manage the local cache directory. hf download: Download files from the Hub. hf jobs: Run and manage Jobs on the Hub. hf repo: Manage repos on the Hub. hf upload: Upload a file or a folder to the Hub. hf version: Print information about the hf version. hf env: Print information about the environment. Authentication Subcommands (hf auth) login: Log in using a Hugging Face token. logout: Log out of your account. whoami: See which account you are logged in as. switch: Switch between different stored access tokens/profiles. list: List all stored access tokens. Jobs Subcommands (hf jobs) run: Run a Job on Hugging Face infrastructure. inspect: Display detailed information on one or more Jobs. logs: Fetch the logs of a Job. ps: List running Jobs. cancel: Cancel a Job.
Explore OCR, Captioning, and Visual Understanding with Cutting-Edge Models on Hugging Face. π€π§ͺ
Iβve put together a collection of Google Colab notebooks to experiment with some of the most exciting models available on the Hugging Face Hub focused on OCR, image captioning, and visual understanding tasks. [Image-to-Text] / [Image-Text-to-Text]
These notebooks are built for quick prototyping and run on free T4 GPUs, making them perfect for experimentation, testing ideas, or just exploring whatβs possible with modern vision-language models.
Note: The experimental notebooks are compiled with models that fit within the T4 GPU (free-tier) limits. More models along with their notebooks will be added over time.