Introducing swift-huggingface: The Complete Swift Client for Hugging Face

Today, we're announcing swift-huggingface, a new Swift package that provides a complete client for the Hugging Face Hub.

You can start using it today as a standalone package, and it will soon integrate into swift-transformers as a replacement for its current HubApi implementation.

The Problem

When we released swift-transformers 1.0 earlier this year, we heard loud and clear from the community:

• Downloads were slow and unreliable. Large model files (often several gigabytes) would fail partway through with no way to resume. Developers resorted to manually downloading models and bundling them with their apps — defeating the purpose of dynamic model loading.
• No shared cache with the Python ecosystem. The Python transformers library stores models in ~/.cache/huggingface/hub. Swift apps downloaded to a different location with a different structure. If you'd already downloaded a model using the Python CLI, you'd download it again for your Swift app.
• Authentication is confusing. Where should tokens come from? Environment variables? Files? Keychain? The answer is, "It depends", and the existing implementation didn't make the options clear.

Introducing swift-huggingface

swift-huggingface is a ground-up rewrite focused on reliability and developer experience. It provides:

• Complete Hub API coverage — models, datasets, spaces, collections, discussions, and more
• Robust file operations — progress tracking, resume support, and proper error handling
• Python-compatible cache — share downloaded models between Swift and Python clients
• Flexible authentication — a TokenProvider pattern that makes credential sources explicit
• OAuth support — first-class support for user-facing apps that need to authenticate users
• Xet storage backend support (Coming soon!) — chunk-based deduplication for significantly faster downloads

Let's look at some examples.

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Flexible Authentication with TokenProvider

One of the biggest improvements is how authentication works. The TokenProvider pattern makes it explicit where credentials come from:

import HuggingFace



let client = HubClient.default


let client = HubClient(tokenProvider: .static("hf_xxx"))


let client = HubClient(tokenProvider: .keychain(service: "com.myapp", account: "hf_token"))

The auto-detection follows the same conventions as the Python huggingface_hub library:

1. HF_TOKEN environment variable
2. HUGGING_FACE_HUB_TOKEN environment variable
3. HF_TOKEN_PATH environment variable (path to token file)
4. $HF_HOME/token file
5. ~/.cache/huggingface/token (standard HF CLI location)
6. ~/.huggingface/token (fallback location)

This means if you've already logged in with hf auth login, swift-huggingface will automatically find and use that token.

OAuth for User-Facing Apps

Building an app where users sign in with their Hugging Face account? swift-huggingface includes a complete OAuth 2.0 implementation:

import HuggingFace


let authManager = try HuggingFaceAuthenticationManager(
    clientID: "your_client_id",
    redirectURL: URL(string: "yourapp://oauth/callback")!,
    scope: [.openid, .profile, .email],
    keychainService: "com.yourapp.huggingface",
    keychainAccount: "user_token"
)


try await authManager.signIn()


let client = HubClient(tokenProvider: .oauth(manager: authManager))


let userInfo = try await client.whoami()
print("Signed in as: \(userInfo.name)")

The OAuth manager handles token storage in Keychain, automatic refresh, and secure sign-out. No more manual token management.

Reliable Downloads

Downloading large models is now straightforward with proper progress tracking and resume support:

let progress = Progress(totalUnitCount: 0)

Task {
    for await _ in progress.publisher(for: \.fractionCompleted).values {
        print("Download: \(Int(progress.fractionCompleted * 100))%")
    }
}

let fileURL = try await client.downloadFile(
    at: "model.safetensors",
    from: "microsoft/phi-2",
    to: destinationURL,
    progress: progress
)

If a download is interrupted, you can resume it:

let fileURL = try await client.resumeDownloadFile(
    resumeData: savedResumeData,
    to: destinationURL,
    progress: progress
)

For downloading entire model repositories, downloadSnapshot handles everything:

let modelDir = try await client.downloadSnapshot(
    of: "mlx-community/Llama-3.2-1B-Instruct-4bit",
    to: cacheDirectory,
    matching: ["*.safetensors", "*.json"],  
    progressHandler: { progress in
        print("Downloaded \(progress.completedUnitCount) of \(progress.totalUnitCount) files")
    }
)

The snapshot function tracks metadata for each file, so subsequent calls only download files that have changed.

Shared Cache with Python

Remember the second problem we mentioned? "No shared cache with the Python ecosystem." That's now solved.

swift-huggingface implements a Python-compatible cache structure that allows seamless sharing between Swift and Python clients:

~/.cache/huggingface/hub/
├── models--deepseek-ai--DeepSeek-V3.2/
│   ├── blobs/
│   │   └── <etag>           # actual file content
│   ├── refs/
│   │   └── main             # contains commit hash
│   └── snapshots/
│       └── <commit_hash>/
│           └── config.json  # symlink → ../../blobs/<etag>

This means:

• Download once, use everywhere. If you've already downloaded a model with the hf CLI or the Python library, swift-huggingface will find it automatically.
• Content-addressed storage. Files are stored by their ETag in the blobs/ directory. If two revisions share the same file, it's only stored once.
• Symlinks for efficiency. Snapshot directories contain symlinks to blobs, minimizing disk usage while maintaining a clean file structure.

The cache location follows the same environment variable conventions as Python:

1. HF_HUB_CACHE environment variable
2. HF_HOME environment variable + /hub
3. ~/.cache/huggingface/hub (default)

You can also use the cache directly:

let cache = HubCache.default


if let cachedPath = cache.cachedFilePath(
    repo: "deepseek-ai/DeepSeek-V3.2",
    kind: .model,
    revision: "main",
    filename: "config.json"
) {
    let data = try Data(contentsOf: cachedPath)
    
}

To prevent race conditions when multiple processes access the same cache, swift-huggingface uses file locking (flock(2)).

Before and After

Here's what downloading a model snapshot looked like with the old HubApi:

let hub = HubApi()
let repo = Hub.Repo(id: "mlx-community/Llama-3.2-1B-Instruct-4bit")


let modelDir = try await hub.snapshot(
    from: repo,
    matching: ["*.safetensors", "*.json"]
) { progress in
    
    print(progress.fractionCompleted)
}

And here's the same operation with swift-huggingface:

let client = HubClient.default

let modelDir = try await client.downloadSnapshot(
    of: "mlx-community/Llama-3.2-1B-Instruct-4bit",
    to: cacheDirectory,
    matching: ["*.safetensors", "*.json"],
    progressHandler: { progress in
        
        print("\(progress.completedUnitCount)/\(progress.totalUnitCount) files")
    }
)

The API is similar, but the implementation is completely different — built on URLSession download tasks with proper delegate handling, resume data support, and metadata tracking.

Beyond Downloads

But wait, there's more! swift-huggingface contains a complete Hub client:

let models = try await client.listModels(
    filter: "library:mlx",
    sort: "trending",
    limit: 10
)


let model = try await client.getModel("mlx-community/Llama-3.2-1B-Instruct-4bit")
print("Downloads: \(model.downloads ?? 0)")
print("Likes: \(model.likes ?? 0)")


let collections = try await client.listCollections(owner: "huggingface", sort: "trending")


let discussions = try await client.listDiscussions(kind: .model, "username/my-model")

And that's not all! swift-huggingface has everything you need to interact with Hugging Face Inference Providers, giving your app instant access to hundreds of machine learning models, powered by world-class inference providers:

import HuggingFace


let client = InferenceClient.default


let response = try await client.textToImage(
    model: "black-forest-labs/FLUX.1-schnell",
    prompt: "A serene Japanese garden with cherry blossoms",
    provider: .hfInference,
    width: 1024,
    height: 1024,
    numImages: 1,
    guidanceScale: 7.5,
    numInferenceSteps: 50,
    seed: 42
)


try response.image.write(to: URL(fileURLWithPath: "generated.png"))

Check the README for a full list of everything that's supported.

What's Next

We're actively working on two fronts:

Integration with swift-transformers. We have a pull request in progress to replace HubApi with swift-huggingface. This will bring reliable downloads to everyone using swift-transformers, mlx-swift-lm, and the broader ecosystem. If you maintain a Swift-based library or app and want help adopting swift-huggingface, reach out — we're happy to help.

Faster downloads with Xet. We're adding support for the Xet storage backend, which enables chunk-based deduplication and significantly faster downloads for large models. More on this soon.

Try It Out

Add swift-huggingface to your project:

dependencies: [
    .package(url: "https://github.com/huggingface/swift-huggingface.git", from: "0.4.0")
]

We'd love your feedback. If you've been frustrated with model downloads in Swift, give this a try and let us know how it goes. Your experience reports will help us prioritize what to improve next.

Resources

• swift-huggingface on GitHub
• swift-transformers
• mlx-swift-examples
• AnyLanguageModel

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Thanks to the swift-transformers community for the feedback that shaped this project, and to everyone who filed issues and shared their experiences. This is for you. ❤️