huggingface git Complete Tutorial and Reference Guide

Hugging Face's Git is a powerful tool for managing and versioning your models, datasets, and other assets. It's a crucial part of the Hugging Face ecosystem, and understanding how it works can save you a lot of time and headaches.

Hugging Face's Git is built on top of the standard Git version control system, but with some additional features and integrations that make it specifically useful for machine learning and natural language processing tasks. For example, it allows you to easily manage and share your models, datasets, and other assets across your team or organization.

To get started with Hugging Face's Git, you'll need to create a Hugging Face account and install the Hugging Face CLI (Command Line Interface) on your computer. This will give you access to the Hugging Face Git repository, where you can store and manage your models and other assets.

Expand your knowledge: Ollama Huggingface

To install Hugging Face's Git repository, you'll need to set up your environment first. You should install Hugging Face's library in a virtual environment to avoid compatibility issues between dependencies.

Create a virtual environment in your project directory, and then activate it on Linux and MacOs using the command `source bin/activate`. This will make it easier to manage different projects.

You can install Hugging Face's library with pip using the command `pip install transformers`. If you're using a deep learning library like PyTorch, TensorFlow, or Flax, you can install Hugging Face's library and the library in one line, like `pip install transformers torch` for PyTorch.

For CPU-support only, you can install Hugging Face's library and a deep learning library in one line. For example, install Hugging Face's library and PyTorch with `pip install transformers torch`.

Intriguing read: Huggingface Transformers Model Loading Slow

Repository Management is a breeze with Hugging Face Hub. You can create and manage a repository with ease, including creating and deleting a repository, managing branches and tags, renaming your repository, updating your repository visibility, and managing a local copy of your repository.

To create a repository, simply use the create_repo() function and give it a name with the repo_id parameter. This will create an empty repository with the specified name, and you can set its visibility to private if needed.

For another approach, see: Create Feature for Dataset Huggingface

If you want to duplicate a repository, you can use the duplicate_space() method, but this is only possible for Spaces. This will duplicate the whole repository, but you'll still need to configure your own settings.

Here are the key tasks you can perform on a repository:

To create a repository, you can use the `create_repo()` function. This function allows you to create an empty repository with a specified name.

Give your repository a name with the `repo_id` parameter, which is your namespace followed by the repository name: `username_or_org/repo_name`. You can also specify another repository type using the `repo_type` parameter. For example, to create a dataset repository, you can use `repo_type='dataset'`.

By default, `create_repo()` creates a model repository, but you can set your repository visibility with the `private` parameter. If you want to change the repository visibility at a later time, you can use the `update_repo_visibility()` function.

Here's a summary of the steps to create a repository:

Renaming your repository is a straightforward process that can be done on the Hub using the move_repo() method. This method allows you to not only rename your repository but also move it from a user to an organization.

You can't transfer your repo to another user. This is one of the limitations you should be aware of when using the move_repo() method.

Renaming your repository is a great way to give it a more descriptive name that accurately reflects its contents. Just keep in mind the limitations of the move_repo() method.

You're trying to manage a repository, but you're not sure where to start. One thing you should know is that git clone works fine with getting models from huggingface.

If you're using git clone to download a repository that includes Git LFS files, you'll need to have git lfs installed. If you run git lfs --version and get a "Command not recognized" message, you haven't got it installed. You can get the latest version from the official git-lfs website, or install an older version using a package manager.

To clone a repository that includes Git LFS files, you can use the command git-lfs clone, but be aware that it's deprecated and will not be updated with new flags from 'git clone'. However, git clone has been updated in upstream Git to have comparable speeds to 'git lfs clone'.

To download models from huggingface, you can use the official CLI tool huggingface-cli or the Python method snapshot_download from the huggingface_hub library. Alternatively, you can use the command curl -L -O url to download a file from a URL.

If you're having trouble downloading large files, you may want to consider deleting the whole .git folder to free up some HDD space, especially if you're not interested in the commit history.

Here are some common issues you might encounter when using git clone with Git LFS files:

You should install 🤗 Transformers in a virtual environment. If you're unfamiliar with Python virtual environments, check out the user guide.

Creating a virtual environment is a good idea because it makes it easier to manage different projects and avoid compatibility issues between dependencies.

First, create a virtual environment with the version of Python you're going to use and activate it. This will ensure that your project uses the correct version of Python.

To install 🤗 Transformers, you'll need to install at least one of Flax, PyTorch, or TensorFlow. Please refer to the specific installation command for your platform on the TensorFlow installation page, PyTorch installation page, or Flax and Jax installation pages.

The version of 🤗 Transformers you can install depends on the version of Python you're using. For example, the repository is tested on Python 3.9+, Flax 0.4.1+, PyTorch 1.11+, and TensorFlow 2.6+.

You can install 🤗 Transformers using pip, but you'll need to install it from source if you want to play with the examples or need the bleeding edge of the code and can't wait for a new release.

If this caught your attention, see: Is Huggingface Transformers Good

Working with repositories on the Hugging Face Hub is a breeze, especially if you're familiar with platforms like GitLab or GitHub. You can create and manage a repository using the Hub's custom HTTP methods, which are more efficient than cloning the repository with Git CLI.

To create a repository, you can use the `create_repo()` function, specifying a name with the `repo_id` parameter in the format `username_or_org/repo_name`. By default, this creates a model repository, but you can specify another type using the `repo_type` parameter.

You can also set the repository visibility to private or public when creating it, and update it later using the `update_repo_visibility()` function. To make things more convenient, you can use the Hub's custom HTTP methods to create and manage your repository, rather than relying on Git CLI commands like `git clone`, `git add`, and `git commit`.

Here are the key actions you can perform on a repository:

You can clone a repository using the clone_from parameter, which takes a Hugging Face repository ID or a URL as an argument. This will create a local copy of the repository in the specified directory.

To clone a repository from a Hugging Face repository ID, use the clone_from parameter with the local_dir argument. You can also use a URL instead of the repository ID.

If you want to create and clone a repository at the same time, you can combine the clone_from parameter with the create_repo() method. This will create a new repository and clone the specified repository into it.

When cloning a repository, you can also configure a Git username and email by specifying the git_user and git_email parameters. This ensures that Git knows who the commit author is when users make changes to the repository.

Check this out: How to Use Huggingface Models in Python

You can download models and tokenizers from the Model Hub to use offline, which is super convenient.

There are three ways to download a file: you can click the ↓ icon on the Model Hub, use the PreTrainedModel.from_pretrained() and PreTrainedModel.save_pretrained() workflow, or programmatically download files with the huggingface_hub library.

Curious to learn more? Check out: How to Use Hugging Face Models

You can download a file through the user interface on the Model Hub by clicking on the ↓ icon, or use the PreTrainedModel.from_pretrained() and PreTrainedModel.save_pretrained() workflow, or programmatically download files with the huggingface_hub library.

Once your file is downloaded and locally cached, you can load and use it by specifying its local path.

Here are the three ways to download a file:

Branches in a Git repository store different versions of the same repository. This is super useful for tracking changes and experimenting with new ideas without affecting the main codebase.

You can create new branches using the create_branch() function. This allows you to work on a new version of the code without disrupting the existing one.

Tags are used to flag a specific state of your repository, like when releasing a version. This helps keep track of important milestones and versions.

You can create new tags using the create_tag() function, just like you would create a new branch. This is a convenient way to mark significant events in your repository's history.

Branches and tags are referred to as git references. This is because they help you reference and navigate different versions of your code.