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| title: README | |
| emoji: 🚀 | |
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| short_description: Description of HF Organization | |
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| HFStack is an open-source organization focused on building reproducible ML infrastructure around the Hugging Face stack. | |
| --- | |
| ## 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. | |
| --- | |
| ## 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. | |
| --- | |
| Contributions and ecosystem collaborations are welcome. |