From 19452ea41488106f9350b39e2fd521b7fb3103af Mon Sep 17 00:00:00 2001 From: kilavvy <140459108+kilavvy@users.noreply.github.com> Date: Sat, 14 Jun 2025 09:06:10 +0200 Subject: [PATCH 1/2] Update spaces-sdks-docker-tabby.md --- docs/hub/spaces-sdks-docker-tabby.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/hub/spaces-sdks-docker-tabby.md b/docs/hub/spaces-sdks-docker-tabby.md index f5f0d87c96..30d86a0f30 100644 --- a/docs/hub/spaces-sdks-docker-tabby.md +++ b/docs/hub/spaces-sdks-docker-tabby.md @@ -6,7 +6,7 @@ In this guide, you will learn how to deploy your own Tabby instance and use it f ## Your first Tabby Space -In this section, you will learn how to deploy a Tabby Space and use it for yourself or your orgnization. +In this section, you will learn how to deploy a Tabby Space and use it for yourself or your organization. ### Deploy Tabby on Spaces From 5ea62a67466dc7e1791299baa1ebe336da3a4a21 Mon Sep 17 00:00:00 2001 From: kilavvy <140459108+kilavvy@users.noreply.github.com> Date: Sat, 14 Jun 2025 09:06:35 +0200 Subject: [PATCH 2/2] Update datasets-dask.md --- docs/hub/datasets-dask.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/hub/datasets-dask.md b/docs/hub/datasets-dask.md index 931d875154..d2a629198c 100644 --- a/docs/hub/datasets-dask.md +++ b/docs/hub/datasets-dask.md @@ -93,7 +93,7 @@ the `meta` argument to know the type of the new column in the meantime. When reading Parquet data from Hugging Face, Dask automatically leverages the metadata in Parquet files to skip entire files or row groups if they are not needed. For example if you apply a filter (predicate) on a Hugging Face Dataset in Parquet format or if you select a subset of the columns (projection), Dask will read the metadata of the Parquet files to discard the parts that are not needed without downloading them. -This is possible thanks to a [reimplmentation of the Dask DataFrame API](https://docs.coiled.io/blog/dask-dataframe-is-fast.html?utm_source=hf-docs) to support query optimization, which makes Dask faster and more robust. +This is possible thanks to a [reimplementation of the Dask DataFrame API](https://docs.coiled.io/blog/dask-dataframe-is-fast.html?utm_source=hf-docs) to support query optimization, which makes Dask faster and more robust. For example this subset of FineWeb-Edu contains many Parquet files. If you can filter the dataset to keep the text from recent CC dumps, Dask will skip most of the files and only download the data that match the filter: