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title: README
emoji: 🚀
colorFrom: green
colorTo: blue
sdk: static
pinned: false
short_description: Description of HF Organization
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![ChatGPT Image May 15, 2026, 11_56_18 PM (1)](https://cdn-uploads.huggingface.co/production/uploads/64e8ea3892d9db9a93580fe3/bcKSfhanoCR3l0fzNJSoH.jpeg)

 HFStack is an open-source organization focused on building reproducible ML infrastructure around the Hugging Face stack.

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## Focus Areas

### Datasets & Storage

Building reproducible workflows around Hugging Face Datasets and HF Buckets.

### Trackio & Observability

Experiment tracking, artifact lineage, and reproducible evaluation pipelines using Trackio.

### Benchmarking & Runtime Systems

Inference benchmarking, optimization workflows, and runtime evaluation tooling.

### Orchestration & Integrations

Composable integrations with tools like Dagster and ecosystem-native ML workflows.

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## Philosophy

HFStack focuses on the systems surrounding modern ML:

* reproducibility
* interoperability
* observability
* infrastructure simplicity

The goal is to make Hugging Face workflows easier to build and operationalize.

## Projects & Integrations

- `dagster-hf-datasets`: [Dagster-HF-Datasets](https://github.com/dagster-io/community-integrations/tree/main/libraries/dagster-hf-datasets) integrates Hugging Face datasets with Dagster for building reproducible, observable data pipelines.
Load datasets directly as Dagster assets, apply transformations, and publish results back to the Hub.

- `Open-Source AI Cookbook with Transformers and Optuna`: Contributed a [recipe](https://huggingface.co/learn/cookbook/en/optuna_hpo_with_transformers#hyperparameter-optimization-with-optuna-and-transformers) which showcases how  best hyperparameters to fine-tune
a lightweight BERT model for text classification on a subset of the IMDB dataset.

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Contributions and ecosystem collaborations are welcome.