🤗 xet-core - xet client tech, used in huggingface_hub

Welcome

xet-core enables huggingface_hub to utilize xet storage for uploading and downloading to HF Hub. Xet storage provides chunk-based deduplication, efficient storage/retrieval with local disk caching, and backwards compatibility with Git LFS. This library is not meant to be used directly, and is instead intended to be used from huggingface_hub.

Key features

♻ chunk-based deduplication implementation: avoid transferring and storing chunks that are shared across binary files (models, datasets, etc).

🤗 Python bindings: bindings for huggingface_hub package.

↔ network communications: concurrent communication to HF Hub Xet backend services (CAS).

🔖 local disk caching: chunk-based cache that sits alongside the existing huggingface_hub disk cache.

Packages

This repository produces the following packages:

Rust Crates (crates.io)

Crate	Description
hf-xet	High-level client library for uploading and downloading files with chunk-based deduplication
xet-client	HTTP client for communicating with Hugging Face Xet storage servers
xet-data	Data processing pipeline for chunking, deduplication, and file reconstruction
xet-core-structures	Core data structures including MerkleHash, metadata shards, and Xorb objects
xet-runtime	Async runtime, configuration, logging, and utility infrastructure

Python Package (PyPI)

Package	Description
hf-xet	Python bindings for the Xet storage system, used by huggingface_hub

Built from the hf_xet/ directory using maturin.

CLI Binary

Binary	Description
git-xet	Git LFS compatible command-line tool for Xet storage

Built from the git_xet/ directory. Distributed via GitHub releases.

Contributions (feature requests, bugs, etc.) are encouraged & appreciated 💙💚💛💜🧡❤️

Please join us in making xet-core better. We value everyone's contributions. Code is not the only way to help. Answering questions, helping each other, improving documentation, filing issues all help immensely. If you are interested in contributing (please do!), check out the contribution guide for this repository.

Issues, Diagnostics & Debugging

If you encounter an issue with hf-xet, please collect diagnostic information and attach it when creating a new Issue.

The scripts/diag/ directory contains platform-specific scripts that download debug symbols, configure logging, and capture periodic stack traces and core dumps:

OS	Script
Linux	scripts/diag/hf-xet-diag-linux.sh
macOS	scripts/diag/hf-xet-diag-macos.sh
Windows (Git-Bash)	scripts/diag/hf-xet-diag-windows.sh

# prefix your failing command with the script for your OS, e.g.:
./scripts/diag/hf-xet-diag-macos.sh -- python my-script.py

See scripts/diag/README.md for full usage, output layout, dump analysis instructions, and how to install debug symbols manually.

Quick debugging environment variables:

RUST_BACKTRACE=full          # full Rust backtraces on panic
RUST_LOG=info                # enable hf-xet logging
HF_XET_LOG_FILE=/tmp/xet.log # write logs to a file (defaults to stdout)

Local Development

Repo Organization

• xet_pkg/ (hf-xet): High-level session API for uploading and downloading files with deduplication.
• xet_client/ (xet-client): HTTP client for CAS and Hub backend services.
• xet_data/ (xet-data): Chunking, deduplication, and file reconstruction pipeline.
• xet_core_structures/ (xet-core-structures): MerkleHash, metadata shards, Xorb objects, and shared data structures.
• xet_runtime/ (xet-runtime): Async runtime, configuration, logging, and utilities.
• hf_xet/: Python bindings (maturin/PyO3), produces the hf-xet PyPI package.
• git_xet/: Git LFS compatible CLI tool (git-xet).
• wasm/: WebAssembly builds (hf_xet_wasm, hf_xet_thin_wasm).
• simulation/: Simulation and benchmarking infrastructure.

Build, Test & Benchmark

To build xet-core, look at requirements in GitHub Actions CI Workflow for the Rust toolchain to install. Follow Rust documentation for installing rustup and that version of the toolchain. Use the following steps for building, testing, benchmarking.

Many of us on the team use VSCode, so we have checked in some settings in the .vscode directory. Install the rust-analyzer extension.

Build:

cargo build

Test:

cargo test

Benchmark:

cargo bench

Linting:

cargo clippy -r --verbose -- -D warnings

Formatting (requires nightly toolchain):

cargo +nightly fmt --manifest-path ./Cargo.toml --all

Building Python package and running locally (on *nix systems):

1. Create Python3 virtualenv: python3 -mvenv ~/venv
2. Activate virtualenv: source ~/venv/bin/activate
3. Install maturin: pip3 install maturin ipython
4. Go to hf_xet crate: cd hf_xet
5. Build: maturin develop
6. Test:

ipython
import hf_xet as hfxet
hfxet.upload_files()
hfxet.download_files()

Developing with tokio console

Prerequisite is installing tokio-console (cargo install tokio-console). See https://github.com/tokio-rs/console

To use tokio-console with hf-xet there are compile hf_xet with the following command:

RUSTFLAGS="--cfg tokio_unstable" maturin develop -r --features tokio-console

Then while hf_xet is running (via a hf cli command or huggingface_hub python code), tokio-console will be able to connect.

Ex.

# In one terminal:
pip install huggingface_hub
RUSTFLAGS="--cfg tokio_unstable" maturin develop -r --features tokio-console
hf download openai/gpt-oss-20b

# In another terminal
cargo install tokio-console
tokio-console

Building universal whl for MacOS:

From hf_xet directory:

MACOSX_DEPLOYMENT_TARGET=10.9 maturin build --release --target universal2-apple-darwin --features openssl_vendored

Note: You may need to install x86_64: rustup target add x86_64-apple-darwin

Testing

Unit-tests are run with cargo test, benchmarks are run with cargo bench. Some crates have a main.rs that can be run for manual testing.

References & History

• Technical Blog posts
• Git is for Data 'CIDR paper
• History: xet-core is adapted from xet-core, which contains deep git integration, along with very different backend services implementation.