diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
index 10f243e59..a77c07340 100644
--- a/.github/workflows/publish.yml
+++ b/.github/workflows/publish.yml
@@ -92,6 +92,7 @@ jobs:
           - "plugins/pandera"
           - "plugins/hydra"
           - "plugins/omegaconf"
+          - "plugins/huggingface"
         include:
           - workdir: "plugins/sglang"
             image-type: sglang
diff --git a/plugins/huggingface/README.md b/plugins/huggingface/README.md
new file mode 100644
index 000000000..0594fc261
--- /dev/null
+++ b/plugins/huggingface/README.md
@@ -0,0 +1,333 @@
+# Hugging Face Plugin
+
+Native Flyte support for Hugging Face integrations in Flyte.
+
+This plugin provides dataset support for Hugging Face `datasets.Dataset`
+and `datasets.IterableDataset` objects. It gives you two related capabilities:
+
+1. Use `from_hf(...)` to reference a dataset on the Hugging Face Hub as a task
+   input default.
+2. Pass Hugging Face dataset objects between Flyte tasks with automatic Parquet
+   serialization.
+
+The plugin works by treating Hub datasets as Parquet-backed structured data. For
+Hub sources, it first resolves the dataset's converted Parquet shards, then
+materializes them either into a generated path for the current run or into a
+shared artifact registry rooted at `cache_root`.
+
+## Installation
+
+```bash
+pip install flyteplugins-huggingface
+```
+
+## Quick start
+
+```python
+import datasets
+import flyte
+from flyteplugins.huggingface.datasets import from_hf
+
+env = flyte.TaskEnvironment(name="hf-example")
+
+@env.task
+async def count_reviews(
+    ds: datasets.Dataset = from_hf(
+        "stanfordnlp/imdb",
+        name="plain_text",
+        split="train",
+    ),
+) -> int:
+    return len(ds)
+```
+
+At the Flyte literal level this source is represented as an `hf://` URI, for
+example:
+
+```text
+hf://stanfordnlp/imdb?name=plain_text&split=train
+```
+
+The task receives a hydrated `datasets.Dataset`. The `hf://` URI is only the
+reference used between Flyte and the plugin.
+
+## `from_hf(...)`
+
+`from_hf(...)` is the entry point for Hub-backed task defaults:
+
+```python
+from flyteplugins.huggingface.datasets import from_hf
+
+from_hf(
+    repo: str,
+    *,
+    name: str | None = None,
+    split: str | None = None,
+    revision: str | None = None,
+    cache_root: str | None = None,
+)
+```
+
+Arguments:
+
+- `repo`: Hugging Face dataset repo, such as `"stanfordnlp/imdb"` or `"glue"`.
+- `name`: Optional dataset config/subset.
+- `split`: Optional split such as `"train"` or `"validation"`.
+- `revision`: Optional Hub revision. Defaults to `refs/convert/parquet`.
+- `cache_root`: Optional shared remote cache root for cross-run reuse.
+
+`from_hf(...)` returns a Flyte `DataFrame` reference, not an eagerly loaded
+dataset object. When the task input is typed as `datasets.Dataset` or
+`datasets.IterableDataset`, the plugin decoder materializes that reference into
+the requested Hugging Face type.
+
+## Config resolution
+
+If you specify `name`, the plugin uses that config directly.
+
+If you omit `name`, the plugin resolves the config as follows:
+
+1. Try actual converted-parquet config `default`.
+2. If `default` does not exist and there is exactly one config, use that one.
+3. If there are multiple configs, raise an error and ask for `name=...`.
+
+Examples:
+
+```python
+# Works: imdb has a single converted-parquet config, plain_text.
+from_hf("stanfordnlp/imdb", split="train")
+
+# Required: glue has multiple configs such as mrpc, sst2, qnli, ...
+from_hf("glue", name="mrpc", split="train")
+```
+
+Using `name=` explicitly is recommended in examples and production code because
+it makes the UI literal and task signature more obvious.
+
+## Split behavior
+
+If you specify `split`, only that split is materialized:
+
+```python
+@env.task
+async def train_split(
+    ds: datasets.Dataset = from_hf(
+        "stanfordnlp/imdb",
+        name="plain_text",
+        split="train",
+    ),
+) -> int:
+    return len(ds)
+```
+
+If you omit `split`, the plugin reads every converted Parquet split under the
+resolved config and presents them as one dataset stream/table:
+
+```python
+@env.task
+async def all_splits(
+    ds: datasets.Dataset = from_hf(
+        "stanfordnlp/imdb",
+        name="plain_text",
+    ),
+) -> list[str]:
+    return ds.column_names
+```
+
+That means the result is a combined dataset, not a mapping of split name to
+dataset.
+
+## Cross-run reuse with `cache_root`
+
+Without `cache_root`, a Hub source is materialized into a generated path for the
+current execution only.
+
+With `cache_root`, the plugin uses a shared cache registry so later runs can
+skip the Hub download entirely:
+
+```python
+@env.task
+async def train_cached(
+    ds: datasets.Dataset = from_hf(
+        "stanfordnlp/imdb",
+        name="plain_text",
+        split="train",
+        cache_root="s3://my-bucket/flyte-hf-cache",
+    ),
+) -> int:
+    return len(ds)
+```
+
+The shared cache layout is:
+
+```text
+{cache_root}/huggingface/datasets/
+  by-key/{source-cache-key}.json
+  blobs/{source-cache-key}/...
+```
+
+The cache key is derived from:
+
+- repo
+- config name
+- split
+- revision
+- resolved Parquet shard metadata
+
+This means the cache is stable across runs as long as the underlying converted
+Parquet source does not change.
+
+The canonical artifact location is always
+`{cache_root}/huggingface/datasets/blobs/{source-cache-key}/...`. The registry
+record under `by-key/` is metadata for that cache key.
+
+## What the plugin logs
+
+When `LOG_LEVEL` is `INFO` or lower, the plugin logs whether it is:
+
+- checking the shared dataset cache
+- materializing from the Hugging Face Hub
+- using a cached artifact
+- reading Parquet from a local or remote directory
+
+This is the easiest way to confirm whether a run is reading from the Hub or
+from your shared cache artifact.
+
+## `datasets.Dataset` between tasks
+
+You can return and pass real `datasets.Dataset` objects between tasks:
+
+```python
+import datasets
+import flyte
+
+env = flyte.TaskEnvironment(name="hf-transform")
+
+
+@env.task
+async def create_dataset() -> datasets.Dataset:
+    return datasets.Dataset.from_dict(
+        {
+            "text": ["hello", "world", "flyte"],
+            "label": [0, 1, 0],
+        }
+    )
+
+
+@env.task
+async def filter_positive(ds: datasets.Dataset) -> datasets.Dataset:
+    return ds.filter(lambda row: row["label"] == 1)
+```
+
+Task-produced in-memory datasets are serialized to Parquet automatically. This
+is separate from `from_hf(...)`, which is a source reference rather than a
+materialized dataset object.
+
+## `datasets.IterableDataset`
+
+Use `datasets.IterableDataset` when you want row streaming behavior instead of a
+fully materialized table:
+
+```python
+@env.task
+async def stream_reviews(
+    ds: datasets.IterableDataset = from_hf(
+        "stanfordnlp/imdb",
+        name="plain_text",
+        split="train",
+        cache_root="s3://my-bucket/flyte-hf-cache",
+    ),
+) -> datasets.IterableDataset:
+    def add_length(batch):
+        batch["length"] = [len(text) for text in batch["text"]]
+        return batch
+
+    return ds.map(add_length, batched=True)
+```
+
+Notes:
+
+- The returned Hugging Face `IterableDataset` is consumed with normal synchronous
+  iteration.
+- Internally the plugin streams row batches from Parquet files.
+- Iterable outputs are written back as sharded Parquet directories.
+
+## Column projection
+
+Use a Flyte structured-dataset column annotation when you only want selected
+columns:
+
+```python
+from collections import OrderedDict
+from typing import Annotated
+
+
+@env.task
+async def load_text_only(
+    ds: Annotated[datasets.Dataset, OrderedDict(text=str)] = from_hf(
+        "stanfordnlp/imdb",
+        name="plain_text",
+        split="train",
+    ),
+) -> list[str]:
+    return ds["text"][:10]
+```
+
+The plugin uses the annotation to request only those columns when reading
+Parquet.
+
+## Revision selection
+
+Use `revision=` if you want to pin a specific converted-Parquet revision:
+
+```python
+@env.task
+async def pinned_revision(
+    ds: datasets.Dataset = from_hf(
+        "stanfordnlp/imdb",
+        name="plain_text",
+        split="train",
+        revision="refs/convert/parquet",
+        cache_root="s3://my-bucket/flyte-hf-cache",
+    ),
+) -> int:
+    return len(ds)
+```
+
+If you do not specify a revision, the plugin uses `refs/convert/parquet`.
+
+## Local vs remote behavior
+
+There are two distinct layers to keep in mind:
+
+1. Task inputs and outputs inside Flyte tasks.
+2. What your launcher process sees when a run completes.
+
+Inside a task, a parameter typed as `datasets.Dataset` or
+`datasets.IterableDataset` is hydrated by the plugin into a Hugging Face object.
+
+Outside the task, especially for remote runs, outputs are often represented to
+the launcher as Flyte `DataFrame` references rather than already-opened Hugging
+Face dataset objects. That is expected: the structured dataset literal remains
+the transport format.
+
+## Private datasets
+
+Set `HF_TOKEN` in the task environment to access private Hugging Face datasets.
+Without it, the plugin uses anonymous Hub access.
+
+## Failure modes
+
+Common issues:
+
+- Missing `name` for a dataset with multiple configs:
+  the plugin raises and asks for `name=...`.
+- No converted Parquet shards available:
+  the dataset may not have an auto-converted Parquet representation yet.
+- Remote cache path credentials:
+  your Flyte runtime must be able to read and write the chosen `cache_root`.
+
+## Example
+
+See the example workflow in `plugins/huggingface/examples/hf_dataset_workflow.py`
+for end-to-end local and remote scenarios.
diff --git a/plugins/huggingface/examples/hf_dataset_workflow.py b/plugins/huggingface/examples/hf_dataset_workflow.py
new file mode 100644
index 000000000..a548ec4a2
--- /dev/null
+++ b/plugins/huggingface/examples/hf_dataset_workflow.py
@@ -0,0 +1,267 @@
+"""
+Example: HuggingFace Datasets with Flyte.
+
+This example demonstrates:
+- Uncached hf:// source materialization
+- Shared dataset materialization with cache_root
+- Dataset configs, splits, and all-split loading
+- Loading selected columns with Annotated
+- Using datasets.IterableDataset for lazy streaming from remote storage
+- Serializing task-produced datasets.Dataset objects to parquet, locally
+"""
+
+import logging
+import os
+import tempfile
+from collections import OrderedDict
+from pathlib import Path
+from typing import Annotated, Any, Callable
+
+import datasets
+import flyte
+from flyte._image import PythonWheels
+from flyte.io import DataFrame
+
+from flyteplugins.huggingface.datasets import from_hf
+
+env = flyte.TaskEnvironment(
+    name="hf-dataset-example",
+    image=flyte.Image.from_debian_base(name="hf-dataset-example").clone(
+        addl_layer=PythonWheels(
+            wheel_dir=Path(__file__).parent.parent / "dist",
+            package_name="flyteplugins-huggingface",
+        ),
+    ),
+)
+
+
+RUN_MODE = os.environ.get("HF_EXAMPLE_RUN_MODE", "local")
+IS_LOCAL_RUN = RUN_MODE == "local"
+
+LOG_LEVEL_NAME = os.environ.get("HF_EXAMPLE_LOG_LEVEL", "INFO")
+LOG_LEVEL = int(LOG_LEVEL_NAME) if LOG_LEVEL_NAME.isdigit() else getattr(logging, LOG_LEVEL_NAME.upper(), logging.INFO)
+
+LOCAL_HF_CACHE_ROOT = tempfile.mkdtemp(prefix="flyte-hf-cache-")
+REMOTE_HF_CACHE_ROOT = "s3://my-bucket/flyte-hf-cache"  # TODO: replace with your own remote cache path for remote runs
+HF_CACHE_ROOT = REMOTE_HF_CACHE_ROOT if RUN_MODE == "remote" else LOCAL_HF_CACHE_ROOT
+
+HF_PARQUET_REVISION = "refs/convert/parquet"
+IMDB_CONFIG = "plain_text"
+
+IMDB_TRAIN = from_hf(
+    "stanfordnlp/imdb",
+    name=IMDB_CONFIG,
+    split="train",
+    cache_root=HF_CACHE_ROOT,
+)
+GLUE_MRPC_TRAIN = from_hf(
+    "glue",
+    name="mrpc",
+    split="train",
+    cache_root=HF_CACHE_ROOT,
+)
+IMDB_ALL_SPLITS = from_hf(
+    "stanfordnlp/imdb",
+    name=IMDB_CONFIG,
+    cache_root=HF_CACHE_ROOT,
+)
+IMDB_TRAIN_EXPLICIT_REVISION = from_hf(
+    "stanfordnlp/imdb",
+    name=IMDB_CONFIG,
+    split="train",
+    revision=HF_PARQUET_REVISION,
+    cache_root=HF_CACHE_ROOT,
+)
+
+
+def dataset_summary(ds: datasets.Dataset) -> str:
+    return f"{len(ds)} rows, columns: {ds.column_names}"
+
+
+def iterable_dataset_summary(_ds: datasets.IterableDataset) -> str:
+    return "IterableDataset"
+
+
+def dataframe_reference_summary(df: DataFrame) -> str:
+    details = [f"uri={df.uri!r}", f"format={df.format!r}"]
+    if df.hash:
+        details.append(f"hash={df.hash!r}")
+    return f"DataFrame({', '.join(details)})"
+
+
+def output_summary(output: Any) -> str:
+    if isinstance(output, DataFrame):
+        return dataframe_reference_summary(output)
+    if isinstance(output, datasets.Dataset):
+        return dataset_summary(output)
+    if isinstance(output, datasets.IterableDataset):
+        return iterable_dataset_summary(output)
+    return str(output)
+
+
+def print_run(
+    label: str,
+    run,
+    formatter: Callable[[Any], str] | None = None,
+) -> Any | None:
+    print(f"{label} URL: {run.url}")
+    try:
+        run.wait()
+        output = run.outputs()[0]
+        output_text = (formatter or output_summary)(output)
+    except Exception as e:
+        print(f"{label} output: unavailable ({type(e).__name__}: {e})")
+        return None
+
+    print(f"{label} output: {output_text}")
+    return output
+
+
+def run_task(task, *args, **kwargs):
+    return flyte.with_runcontext(
+        RUN_MODE,
+        log_level=LOG_LEVEL,
+        env_vars={"LOG_LEVEL": str(LOG_LEVEL)},
+    ).run(task, *args, **kwargs)
+
+
+@env.task
+async def count_imdb_uncached_source(
+    ds: datasets.Dataset = from_hf(
+        "stanfordnlp/imdb",
+        name=IMDB_CONFIG,
+        split="train",
+    ),
+) -> int:
+    """Materialize to a generated Flyte raw-data path for this execution."""
+    return len(ds)
+
+
+@env.task
+async def tokenize_imdb_cached_source(
+    ds: datasets.Dataset = IMDB_TRAIN,
+) -> datasets.Dataset:
+    """Add a word-count column. On first run the plugin fetches the IMDB
+    dataset from HuggingFace Hub and caches it in remote storage; subsequent
+    runs skip the download entirely."""
+    word_counts = [len(text.split()) for text in ds["text"]]
+    return ds.add_column("word_count", word_counts)
+
+
+@env.task
+async def count_glue_mrpc_cached_source(ds: datasets.Dataset = GLUE_MRPC_TRAIN) -> int:
+    """Load a dataset repo that requires a config/subset name."""
+    return len(ds)
+
+
+@env.task
+async def list_imdb_cached_all_splits(
+    ds: datasets.Dataset = IMDB_ALL_SPLITS,
+) -> list[str]:
+    """Omitting split fetches all converted parquet splits for the config."""
+    return ds.column_names
+
+
+@env.task
+async def count_imdb_cached_explicit_revision(
+    ds: datasets.Dataset = IMDB_TRAIN_EXPLICIT_REVISION,
+) -> int:
+    """Use a specific Hugging Face revision for converted parquet files."""
+    return len(ds)
+
+
+@env.task
+async def sample_imdb_cached_text_column(
+    ds: Annotated[
+        datasets.Dataset,
+        OrderedDict(text=str),
+    ] = IMDB_TRAIN,
+) -> list[str]:
+    """Only the requested columns are read from parquet."""
+    return ds["text"][:10]
+
+
+@env.task
+async def filter_tokenized_reviews(ds: datasets.Dataset) -> datasets.Dataset:
+    """Return a new in-memory datasets.Dataset produced by task code."""
+    return ds.filter(lambda row: row["word_count"] > 100)
+
+
+@env.task
+async def summarize_dataset(ds: datasets.Dataset) -> str:
+    return f"{len(ds)} rows, columns: {ds.column_names}"
+
+
+@env.task
+async def stream_tokenize_imdb_cached_source(
+    ds: datasets.IterableDataset = IMDB_TRAIN,
+) -> datasets.IterableDataset:
+    """IterableDataset variant: stream from cached parquet without a full load."""
+
+    def add_word_count(batch):
+        batch["word_count"] = [len(t.split()) for t in batch["text"]]
+        return batch
+
+    return ds.map(add_word_count, batched=True)
+
+
+@env.task
+async def count_stream_sample(ds: datasets.IterableDataset) -> int:
+    """Consume an IterableDataset produced by another task."""
+    count = 0
+    for _row in ds.take(1_000):
+        count += 1
+    return count
+
+
+if __name__ == "__main__":
+    flyte.init_from_config(log_level=LOG_LEVEL)
+
+    # Scenario 1: uncached hf:// source.
+    uncached_run = run_task(count_imdb_uncached_source)
+    print_run("uncached imdb count", uncached_run)
+
+    # Scenario 2: cached hf:// source. The second run should reuse cache_root.
+    cached_run = run_task(tokenize_imdb_cached_source)
+    tokenized = print_run("cached imdb tokenize", cached_run)
+
+    second_cached_run = run_task(tokenize_imdb_cached_source)
+    print_run("cached imdb tokenize again", second_cached_run)
+
+    # Scenario 3: config, split, revision, and column projection.
+    glue_run = run_task(count_glue_mrpc_cached_source)
+    print_run("cached glue/mrpc count", glue_run)
+
+    all_splits_run = run_task(list_imdb_cached_all_splits.override(resources=flyte.Resources(memory="2Gi")))
+    print_run("cached imdb all-splits columns", all_splits_run)
+
+    revision_run = run_task(count_imdb_cached_explicit_revision)
+    print_run("cached imdb explicit revision count", revision_run)
+
+    text_column_run = run_task(sample_imdb_cached_text_column)
+    print_run(
+        "cached imdb text column sample",
+        text_column_run,
+        lambda rows: f"{len(rows)} rows",
+    )
+
+    # Scenario 4: IterableDataset source.
+    stream_run = run_task(stream_tokenize_imdb_cached_source)
+    stream = print_run(
+        "cached imdb stream tokenize",
+        stream_run,
+        iterable_dataset_summary,
+    )
+
+    # Scenario 5: local-only in-memory Dataset output round trip.
+    if IS_LOCAL_RUN and tokenized is not None:
+        filtered_run = run_task(filter_tokenized_reviews, tokenized)
+        filtered = print_run("filter tokenized reviews", filtered_run, dataset_summary)
+
+        if filtered is not None:
+            summary_run = run_task(summarize_dataset, filtered)
+            print_run("summarize filtered dataset", summary_run)
+
+    if IS_LOCAL_RUN and stream is not None:
+        stream_count_run = run_task(count_stream_sample, stream)
+        print_run("count stream sample", stream_count_run)
diff --git a/plugins/huggingface/pyproject.toml b/plugins/huggingface/pyproject.toml
new file mode 100644
index 000000000..a6125be43
--- /dev/null
+++ b/plugins/huggingface/pyproject.toml
@@ -0,0 +1,84 @@
+[project]
+name = "flyteplugins-huggingface"
+dynamic = ["version"]
+description = "Hugging Face Plugin for Flyte"
+readme = "README.md"
+authors = [{ name = "André Ahlert", email = "andre@aex.partners" }, { name = "Samhita Alla", email = "samhita@union.ai" }]
+requires-python = ">=3.10"
+dependencies = [
+    "datasets",
+    "huggingface-hub",
+    "pyarrow",
+    "flyte"
+]
+
+[project.entry-points."flyte.plugins.types"]
+huggingface = "flyteplugins.huggingface.datasets:register_huggingface_dataset_transformers"
+
+[build-system]
+requires = ["setuptools", "setuptools_scm"]
+build-backend = "setuptools.build_meta"
+
+[dependency-groups]
+dev = [
+    "pytest>=8.3.5",
+    "pytest-asyncio>=0.26.0",
+]
+
+[tool.setuptools]
+include-package-data = true
+license-files = ["licenses/*.txt", "LICENSE"]
+
+[tool.setuptools.packages.find]
+where = ["src"]
+include = ["flyteplugins*"]
+
+[tool.setuptools_scm]
+root = "../../"
+
+[tool.pytest.ini_options]
+norecursedirs = []
+log_cli = true
+log_cli_level = 20
+markers = []
+asyncio_mode = "auto"
+asyncio_default_fixture_loop_scope = "function"
+
+[tool.coverage.run]
+branch = true
+
+[tool.ruff]
+line-length = 120
+
+[tool.ruff.lint]
+select = [
+    "E",
+    "W",
+    "F",
+    "I",
+    "PLW",
+    "YTT",
+    "ASYNC",
+    "C4",
+    "T10",
+    "EXE",
+    "ISC",
+    "LOG",
+    "PIE",
+    "Q",
+    "RSE",
+    "FLY",
+    "PGH",
+    "PLC",
+    "PLE",
+    "PLW",
+    "FURB",
+    "RUF",
+]
+ignore = ["PGH003", "PLC0415", "ASYNC240"]
+
+[tool.ruff.lint.per-file-ignores]
+"examples/*" = ["E402"]
+
+[tool.uv.sources]
+flyte = { path = "../../", editable = true }
diff --git a/plugins/huggingface/src/flyteplugins/huggingface/__init__.py b/plugins/huggingface/src/flyteplugins/huggingface/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/plugins/huggingface/src/flyteplugins/huggingface/datasets/__init__.py b/plugins/huggingface/src/flyteplugins/huggingface/datasets/__init__.py
new file mode 100644
index 000000000..bfa1cbd5c
--- /dev/null
+++ b/plugins/huggingface/src/flyteplugins/huggingface/datasets/__init__.py
@@ -0,0 +1,33 @@
+import functools
+
+from ._source import HFSource, from_hf
+
+__all__ = ["HFSource", "from_hf"]
+
+
+@functools.lru_cache(maxsize=None)
+def register_huggingface_dataset_transformers():
+    """Register Hugging Face Dataset encoders and decoders."""
+    from flyte.io.extend import DataFrameTransformerEngine
+
+    from ._transformers import (
+        HFToHuggingFaceDatasetDecodingHandler,
+        HFToHuggingFaceIterableDatasetDecodingHandler,
+        HuggingFaceDatasetToParquetEncodingHandler,
+        HuggingFaceIterableDatasetToParquetEncodingHandler,
+        ParquetToHuggingFaceDatasetDecodingHandler,
+        ParquetToHuggingFaceIterableDatasetDecodingHandler,
+    )
+
+    DataFrameTransformerEngine.register(HuggingFaceDatasetToParquetEncodingHandler(), default_format_for_type=True)
+    DataFrameTransformerEngine.register(ParquetToHuggingFaceDatasetDecodingHandler(), default_format_for_type=True)
+    DataFrameTransformerEngine.register(HFToHuggingFaceDatasetDecodingHandler())
+    DataFrameTransformerEngine.register(
+        HuggingFaceIterableDatasetToParquetEncodingHandler(),
+        default_format_for_type=True,
+    )
+    DataFrameTransformerEngine.register(
+        ParquetToHuggingFaceIterableDatasetDecodingHandler(),
+        default_format_for_type=True,
+    )
+    DataFrameTransformerEngine.register(HFToHuggingFaceIterableDatasetDecodingHandler())
diff --git a/plugins/huggingface/src/flyteplugins/huggingface/datasets/_io.py b/plugins/huggingface/src/flyteplugins/huggingface/datasets/_io.py
new file mode 100644
index 000000000..a9fcc6227
--- /dev/null
+++ b/plugins/huggingface/src/flyteplugins/huggingface/datasets/_io.py
@@ -0,0 +1,499 @@
+from __future__ import annotations
+
+import asyncio
+import json
+import os
+import typing
+
+import flyte.storage as storage
+from flyte._logging import logger
+from fsspec.asyn import AsyncFileSystem
+
+from ._source import (
+    HFShard,
+    HFSource,
+    hf_revision,
+    hf_source_cache_key,
+    hf_source_payload,
+)
+
+_HF_CACHE_MANIFEST = "_flyte_hf_manifest.json"
+_HF_DATASET_REGISTRY = "huggingface/datasets"
+
+
+class HFParquetError(ValueError):
+    """Raised when a Hugging Face parquet conversion cannot be resolved."""
+
+
+def get_hf_cache_path(source: HFSource, cache_key: str | None = None) -> str:
+    """Return a deterministic remote-storage path for source."""
+    if source.cache_root is None:
+        raise ValueError("cache_root is required for deterministic HF cache paths")
+
+    cache_key = cache_key or hf_source_cache_key(source)
+    return join_uri_path(source.cache_root, _HF_DATASET_REGISTRY, "blobs", cache_key)
+
+
+def get_hf_registry_record_path(source: HFSource, cache_key: str) -> str:
+    if source.cache_root is None:
+        raise ValueError("cache_root is required for HF registry records")
+
+    return join_uri_path(
+        source.cache_root,
+        _HF_DATASET_REGISTRY,
+        "by-key",
+        f"{cache_key}.json",
+    )
+
+
+def get_random_hf_path() -> str:
+    return os.path.join(str(storage.get_random_local_directory()), "hf-dataset")
+
+
+def join_uri_path(base: str, *parts: str) -> str:
+    """Join URI/object-store path components with POSIX separators."""
+    joined = base.rstrip("/")
+    for part in parts:
+        cleaned = part.strip("/")
+        if cleaned:
+            joined = f"{joined}/{cleaned}" if joined else cleaned
+    return joined
+
+
+def _storage_kind(path: str) -> str:
+    return "remote" if storage.is_remote(path) else "local"
+
+
+def _source_log_description(source: HFSource) -> str:
+    config = source.name if source.name is not None else "auto"
+    split = source.split if source.split is not None else "all"
+    return f"config={config}, split={split}"
+
+
+async def run_sync_io(
+    label: str,
+    func: typing.Callable[..., typing.Any],
+    *args: typing.Any,
+    **kwargs: typing.Any,
+) -> typing.Any:
+    """Run blocking sync IO without blocking the active event loop."""
+
+    try:
+        # Using to_thread() as the blocking calls are mostly IO-bound.
+        return await asyncio.to_thread(func, *args, **kwargs)
+    except asyncio.CancelledError:
+        logger.warning(
+            f"Cancellation requested while running {label}. The active sync IO "
+            "call may finish in its worker thread before stopping."
+        )
+        raise
+
+
+async def storage_path_exists(path: str) -> bool:
+    if storage.is_remote(path):
+        try:
+            return await storage.exists(path)
+        except Exception as e:
+            logger.debug(f"Unable to check whether {path} exists: {e}")
+            return False
+
+    return typing.cast(bool, await run_sync_io("local exists", os.path.exists, path))
+
+
+async def storage_read_bytes(path: str) -> bytes:
+    if storage.is_remote(path):
+        local_path = storage.get_random_local_path(file_path_or_file_name=os.path.basename(path))
+        await storage.get(path, str(local_path))
+        path = str(local_path)
+
+    def _read() -> bytes:
+        with open(path, "rb") as fh:
+            return fh.read()
+
+    return typing.cast(bytes, await run_sync_io("read local file", _read))
+
+
+async def storage_write_bytes(path: str, data: bytes) -> None:
+    if storage.is_remote(path):
+        await storage.put_stream(data, to_path=path)
+        return
+
+    parent = os.path.dirname(path)
+    if parent:
+        os.makedirs(parent, exist_ok=True)
+
+    def _write() -> None:
+        with open(path, "wb") as fh:
+            fh.write(data)
+
+    await run_sync_io("write local file", _write)
+
+
+async def list_parquet_files(uri: str, filesystem) -> list[str]:
+    """Return sorted parquet file paths under uri, recursively."""
+    try:
+        if isinstance(filesystem, AsyncFileSystem):
+            raw = sorted(f for f in await filesystem._find(uri) if f.endswith(".parquet"))
+        else:
+            found = await run_sync_io("filesystem find parquet", filesystem.find, uri)
+            raw = sorted(f for f in found if f.endswith(".parquet"))
+
+        if not raw:
+            return [join_uri_path(uri, f"{0:05}.parquet")]
+
+        if "://" in uri and "://" not in raw[0]:
+            proto = uri.split("://", maxsplit=1)[0] + "://"
+            raw = [f"{proto}{f}" for f in raw]
+        return raw
+    except Exception as e:
+        logger.warning(f"Unable to list parquet files under {uri}: {e}")
+        return [join_uri_path(uri, f"{0:05}.parquet")]
+
+
+def _format_names(names: list[str]) -> str:
+    return ", ".join(names) if names else "none"
+
+
+def _list_hf_dir(hfs, path: str, revision: str) -> list[dict[str, typing.Any]]:
+    return typing.cast(
+        list[dict[str, typing.Any]],
+        hfs.ls(path, revision=revision, detail=True),
+    )
+
+
+def _try_list_hf_dir(
+    hfs,
+    path: str,
+    revision: str,
+) -> list[dict[str, typing.Any]] | None:
+    try:
+        return _list_hf_dir(hfs, path, revision)
+    except FileNotFoundError:
+        return None
+
+
+def _entry_name(entry: dict[str, typing.Any]) -> str:
+    return typing.cast(str, entry["name"]).rstrip("/").split("/")[-1]
+
+
+def _is_directory(entry: dict[str, typing.Any]) -> bool:
+    return entry.get("type") == "directory"
+
+
+def _is_parquet_file(entry: dict[str, typing.Any]) -> bool:
+    return entry.get("type") == "file" and typing.cast(str, entry.get("name", "")).endswith(".parquet")
+
+
+def _config_dirs(entries: list[dict[str, typing.Any]]) -> list[dict[str, typing.Any]]:
+    return sorted(
+        [entry for entry in entries if _is_directory(entry)],
+        key=lambda entry: typing.cast(str, entry["name"]),
+    )
+
+
+def _resolve_hf_config(
+    hfs,
+    source: HFSource,
+    revision: str,
+) -> tuple[str, str, list[dict[str, typing.Any]]]:
+    """Return the config name, path, and entries for source, or raise HFParquetError."""
+    repo_path = join_uri_path("datasets", source.repo)
+
+    if source.name is not None:
+        config_path = join_uri_path(repo_path, source.name)
+        entries = _try_list_hf_dir(hfs, config_path, revision)
+        if entries is not None:
+            return source.name, config_path, entries
+
+        repo_entries = _try_list_hf_dir(hfs, repo_path, revision) or []
+        available = [_entry_name(entry) for entry in _config_dirs(repo_entries)]
+        raise HFParquetError(
+            f"No Hugging Face parquet config named {source.name!r} found for "
+            f"{source.repo} at revision {revision!r}. Available configs: "
+            f"{_format_names(available)}."
+        )
+
+    default_path = join_uri_path(repo_path, "default")
+    default_entries = _try_list_hf_dir(hfs, default_path, revision)
+    if default_entries is not None:
+        return "default", default_path, default_entries
+
+    repo_entries = _try_list_hf_dir(hfs, repo_path, revision)
+    if repo_entries is None:
+        raise HFParquetError(f"No Hugging Face parquet conversion found for {source.repo} at revision {revision!r}.")
+
+    configs = _config_dirs(repo_entries)
+    config_names = [_entry_name(entry) for entry in configs]
+    if len(configs) == 1:
+        config_path = typing.cast(str, configs[0]["name"])
+        return config_names[0], config_path, _list_hf_dir(hfs, config_path, revision)
+
+    if not configs:
+        raise HFParquetError(f"No Hugging Face parquet configs found for {source.repo} at revision {revision!r}.")
+
+    raise HFParquetError(
+        f"Hugging Face dataset {source.repo} has multiple parquet configs: "
+        f"{_format_names(config_names)}. Pass name=... to from_hf()."
+    )
+
+
+def collect_hf_shards(source: HFSource) -> list[HFShard]:
+    """Return parquet shards for a Hugging Face dataset source."""
+    import huggingface_hub
+
+    token = os.environ.get("HF_TOKEN")
+    if token is None:
+        logger.warning("HF_TOKEN not set, using anonymous access. Private datasets will fail.")
+
+    hfs = huggingface_hub.HfFileSystem(token=token)
+    revision = hf_revision(source)
+    config, base_path, base_entries = _resolve_hf_config(hfs, source, revision)
+
+    if source.split:
+        split_paths = [(source.split, join_uri_path(base_path, source.split))]
+    else:
+        split_paths = [
+            (_entry_name(entry), typing.cast(str, entry["name"])) for entry in base_entries if _is_directory(entry)
+        ]
+        if not split_paths and any(_is_parquet_file(entry) for entry in base_entries):
+            split_paths = [("data", base_path)]
+
+    shards: list[HFShard] = []
+    for split_name, search_path in split_paths:
+        entries = _try_list_hf_dir(hfs, search_path, revision)
+        if entries is None:
+            raise HFParquetError(
+                f"No Hugging Face parquet split named {split_name!r} found for "
+                f"{source.repo} (config={config}) at revision {revision!r}."
+            )
+
+        for file_info in entries:
+            if _is_parquet_file(file_info):
+                file_name = file_info["name"].split("/")[-1]
+                rel = file_name if source.split else join_uri_path(split_name, file_name)
+                shards.append(
+                    HFShard(
+                        rel_path=rel,
+                        hf_name=file_info["name"],
+                        size=file_info.get("size"),
+                        etag=file_info.get("etag") or file_info.get("ETag"),
+                        last_modified=file_info.get("last_modified"),
+                    )
+                )
+
+    if not shards:
+        raise HFParquetError(
+            f"No parquet files found for {source.repo} "
+            f"(config={config}, split={source.split}). "
+            "The dataset may not have been auto-converted to parquet yet."
+        )
+    return sorted(shards, key=lambda s: s.rel_path)
+
+
+def manifest_path(remote_path: str) -> str:
+    return join_uri_path(remote_path, _HF_CACHE_MANIFEST)
+
+
+def hf_cache_manifest(
+    source: HFSource,
+    shards: list[HFShard],
+    cache_key: str,
+) -> dict[str, typing.Any]:
+    return {
+        "version": 1,
+        "cache_key": cache_key,
+        "source": hf_source_payload(source),
+        "shards": hf_source_payload(source, shards)["shards"],
+    }
+
+
+async def read_cache_manifest(remote_path: str) -> dict[str, typing.Any] | None:
+    path = manifest_path(remote_path)
+    try:
+        if not await storage_path_exists(path):
+            return None
+        return typing.cast(
+            dict[str, typing.Any],
+            json.loads((await storage_read_bytes(path)).decode("utf-8")),
+        )
+    except Exception as e:
+        logger.debug(f"Unable to read HF cache manifest {path}: {e}")
+        return None
+
+
+async def write_cache_manifest(remote_path: str, manifest: dict[str, typing.Any]) -> None:
+    data = json.dumps(manifest, sort_keys=True, indent=2).encode("utf-8")
+    await storage_write_bytes(manifest_path(remote_path), data)
+
+
+async def read_registry_record(
+    source: HFSource,
+    cache_key: str,
+) -> dict[str, typing.Any] | None:
+    path = get_hf_registry_record_path(source, cache_key)
+    try:
+        if not await storage_path_exists(path):
+            return None
+        return typing.cast(
+            dict[str, typing.Any],
+            json.loads((await storage_read_bytes(path)).decode("utf-8")),
+        )
+    except Exception as e:
+        logger.debug(f"Unable to read HF registry record {path}: {e}")
+        return None
+
+
+async def write_registry_record(
+    source: HFSource,
+    cache_key: str,
+    manifest: dict[str, typing.Any],
+) -> None:
+    record = dict(manifest)
+    data = json.dumps(record, sort_keys=True, indent=2).encode("utf-8")
+    await storage_write_bytes(get_hf_registry_record_path(source, cache_key), data)
+
+
+async def open_sync_hf_reader(hfs, hf_name: str, revision: str):
+    return await run_sync_io(
+        "open HF shard",
+        hfs.open,
+        hf_name,
+        "rb",
+        revision=revision,
+    )
+
+
+async def close_sync_file(label: str, file_obj) -> None:
+    close = getattr(file_obj, "close", None)
+    if close is not None:
+        await run_sync_io(label, close)
+
+
+async def iter_hf_shard_chunks(
+    hfs,
+    shard: HFShard,
+    *,
+    revision: str,
+    chunk_size: int,
+) -> typing.AsyncIterator[bytes]:
+    src = await open_sync_hf_reader(hfs, shard.hf_name, revision)
+    try:
+        while chunk := await run_sync_io("read HF shard chunk", src.read, chunk_size):
+            yield chunk
+    finally:
+        await close_sync_file("close HF shard", src)
+
+
+async def stream_hf_shard(
+    hfs,
+    shard: HFShard,
+    dest: str,
+    *,
+    revision: str,
+    chunk_size: int,
+) -> None:
+    if not storage.is_remote(dest):
+        parent = os.path.dirname(dest)
+        if parent:
+            os.makedirs(parent, exist_ok=True)
+
+    await storage.put_stream(
+        iter_hf_shard_chunks(
+            hfs,
+            shard,
+            revision=revision,
+            chunk_size=chunk_size,
+        ),
+        to_path=dest,
+    )
+
+
+async def stream_hf_to_remote(
+    source: HFSource,
+    remote_path: str,
+    shards: list[HFShard] | None = None,
+    manifest: dict[str, typing.Any] | None = None,
+) -> None:
+    """Stream parquet shards from Hugging Face Hub to Flyte remote storage."""
+    import huggingface_hub
+
+    token = os.environ.get("HF_TOKEN")
+    hfs = huggingface_hub.HfFileSystem(token=token)
+    chunk_size = 64 * 1024 * 1024
+    revision = hf_revision(source)
+
+    if shards is None:
+        shards = typing.cast(
+            list[HFShard],
+            await run_sync_io("collect HF parquet shards", collect_hf_shards, source),
+        )
+
+    logger.info(
+        f"Streaming {len(shards)} Hugging Face parquet shard(s) for {source.repo} "
+        f"to {_storage_kind(remote_path)} path {remote_path}"
+    )
+    for shard in shards:
+        dest = join_uri_path(remote_path, shard.rel_path)
+        logger.info(f"Streaming {shard.rel_path} to {dest}")
+        await stream_hf_shard(
+            hfs,
+            shard,
+            dest,
+            revision=revision,
+            chunk_size=chunk_size,
+        )
+
+    if manifest is not None:
+        await write_cache_manifest(remote_path, manifest)
+
+    logger.info(f"Streamed {len(shards)} parquet file(s) to {remote_path}")
+
+
+async def ensure_hf_cached(source: HFSource) -> str:
+    """Return the remote path for source, fetching from HF if not cached."""
+    shards = typing.cast(
+        list[HFShard],
+        await run_sync_io("collect HF parquet shards", collect_hf_shards, source),
+    )
+    cache_key = hf_source_cache_key(source, shards)
+    expected_manifest = hf_cache_manifest(source, shards, cache_key)
+
+    if source.cache_root is None:
+        remote_path = get_random_hf_path()
+        logger.info(
+            f"Materializing Hugging Face dataset {source.repo} "
+            f"({_source_log_description(source)}) "
+            f"to local path {remote_path}"
+        )
+        await stream_hf_to_remote(source, remote_path, shards, expected_manifest)
+        return remote_path
+
+    default_remote_path = get_hf_cache_path(source, cache_key)
+    logger.info(
+        f"Checking Hugging Face dataset cache for {source.repo} "
+        f"({_source_log_description(source)}) "
+        f"under {source.cache_root}"
+    )
+    registry_record = await read_registry_record(source, cache_key)
+    if await read_cache_manifest(default_remote_path) == expected_manifest:
+        if registry_record is None:
+            await write_registry_record(
+                source,
+                cache_key,
+                expected_manifest,
+            )
+        logger.info(f"Using cached Hugging Face dataset at {default_remote_path}")
+        return default_remote_path
+
+    logger.info(
+        f"Materializing Hugging Face dataset {source.repo} "
+        f"({_source_log_description(source)}) "
+        f"to remote cache artifact {default_remote_path}"
+    )
+    await stream_hf_to_remote(source, default_remote_path, shards, expected_manifest)
+    await write_registry_record(
+        source,
+        cache_key,
+        expected_manifest,
+    )
+    return default_remote_path
diff --git a/plugins/huggingface/src/flyteplugins/huggingface/datasets/_source.py b/plugins/huggingface/src/flyteplugins/huggingface/datasets/_source.py
new file mode 100644
index 000000000..50cb53e3c
--- /dev/null
+++ b/plugins/huggingface/src/flyteplugins/huggingface/datasets/_source.py
@@ -0,0 +1,158 @@
+from __future__ import annotations
+
+import hashlib
+import json
+import typing
+from dataclasses import dataclass
+from urllib.parse import parse_qs, urlencode
+
+from flyte.io import PARQUET, DataFrame
+
+_HF_PARQUET_REVISION = "refs/convert/parquet"
+
+
+@dataclass
+class HFSource:
+    """HuggingFace dataset source for task parameter defaults."""
+
+    repo: str
+    name: str | None = None
+    split: str | None = None
+    revision: str | None = None
+    cache_root: str | None = None
+
+    def __post_init__(self) -> None:
+        self.repo = self._normalize_required_field("repo", self.repo)
+        self.name = self._normalize_optional_field("name", self.name)
+        self.split = self._normalize_optional_field("split", self.split)
+        self.revision = self._normalize_optional_field("revision", self.revision)
+        self.cache_root = self._normalize_optional_field("cache_root", self.cache_root)
+
+    @staticmethod
+    def _normalize_required_field(field_name: str, value: str) -> str:
+        normalized = value.strip()
+        if not normalized:
+            raise ValueError(f"HFSource {field_name} must not be empty")
+        return normalized
+
+    @staticmethod
+    def _normalize_optional_field(field_name: str, value: str | None) -> str | None:
+        if value is None:
+            return None
+        normalized = value.strip()
+        if not normalized:
+            raise ValueError(f"HFSource {field_name} must not be blank")
+        return normalized
+
+    def to_hf_uri(self) -> str:
+        uri = f"hf://{self.repo}"
+        params = {}
+        if self.name:
+            params["name"] = self.name
+        if self.split:
+            params["split"] = self.split
+        if self.cache_root:
+            params["cache_root"] = self.cache_root
+        if self.revision:
+            params["revision"] = self.revision
+        if params:
+            uri = f"{uri}?{urlencode(params, safe=':/')}"
+        return uri
+
+    @classmethod
+    def from_hf_uri(cls, uri: str) -> "HFSource":
+        if not uri.startswith("hf://"):
+            raise ValueError(f"Invalid HF URI: {uri}")
+
+        path, _, query = uri[5:].partition("?")
+        repo = path.strip("/")
+        if not repo:
+            raise ValueError(f"Invalid HF URI: {uri}")
+
+        query_params = parse_qs(query)
+        name = query_params.get("name", [None])[0]
+        split = query_params.get("split", [None])[0]
+        revision = query_params.get("revision", [None])[0]
+        cache_root = query_params.get("cache_root", [None])[0]
+
+        return cls(
+            repo=repo,
+            name=name,
+            split=split,
+            revision=revision,
+            cache_root=cache_root,
+        )
+
+
+def from_hf(
+    repo: str,
+    *,
+    name: str | None = None,
+    split: str | None = None,
+    revision: str | None = None,
+    cache_root: str | None = None,
+) -> DataFrame:
+    """Return a DataFrame reference for use as a task parameter default.
+
+    cache_root optionally points at a stable remote directory that can be reused
+    across runs. Without it, the dataset is materialized to a generated Flyte raw-data
+    path for this run. If name is omitted, the plugin resolves the dataset's
+    default converted-parquet config, or the only available config when there is
+    exactly one.
+    """
+    source = HFSource(
+        repo=repo,
+        name=name,
+        split=split,
+        revision=revision,
+        cache_root=cache_root,
+    )
+
+    return DataFrame(
+        uri=source.to_hf_uri(),
+        format=PARQUET,
+        hash=hf_source_cache_key(source),
+    )
+
+
+@dataclass(frozen=True)
+class HFShard:
+    rel_path: str
+    hf_name: str
+    size: int | None = None
+    etag: str | None = None
+    last_modified: str | None = None
+
+
+def hf_revision(source: HFSource) -> str:
+    return source.revision or _HF_PARQUET_REVISION
+
+
+def hf_source_payload(source: HFSource, shards: list[HFShard] | None = None) -> dict[str, typing.Any]:
+    payload: dict[str, typing.Any] = {
+        "repo": source.repo,
+        "name": source.name,
+        "split": source.split,
+        "revision": hf_revision(source),
+    }
+    if shards is not None:
+        payload["shards"] = [
+            {
+                "rel_path": shard.rel_path,
+                "hf_name": shard.hf_name,
+                "size": shard.size,
+                "etag": shard.etag,
+                "last_modified": (str(shard.last_modified) if shard.last_modified is not None else None),
+            }
+            for shard in sorted(shards, key=lambda s: s.rel_path)
+        ]
+    return payload
+
+
+def hf_source_cache_key(source: HFSource, shards: list[HFShard] | None = None) -> str:
+    payload = json.dumps(
+        hf_source_payload(source, shards),
+        sort_keys=True,
+        separators=(",", ":"),
+    )
+    return hashlib.sha256(payload.encode("utf-8")).hexdigest()
diff --git a/plugins/huggingface/src/flyteplugins/huggingface/datasets/_transformers.py b/plugins/huggingface/src/flyteplugins/huggingface/datasets/_transformers.py
new file mode 100644
index 000000000..638e726b1
--- /dev/null
+++ b/plugins/huggingface/src/flyteplugins/huggingface/datasets/_transformers.py
@@ -0,0 +1,352 @@
+from __future__ import annotations
+
+import pathlib
+import typing
+from pathlib import Path
+
+import datasets
+import flyte.storage as storage
+import pyarrow as pa
+import pyarrow.parquet as pq
+from flyte._logging import logger
+from flyte.io import PARQUET, DataFrame
+from flyte.io.extend import DataFrameDecoder, DataFrameEncoder
+from flyteidl2.core import literals_pb2, types_pb2
+from fsspec.core import strip_protocol
+
+from ._io import ensure_hf_cached, join_uri_path, list_parquet_files, run_sync_io
+from ._source import HFSource
+
+_ROWS_PER_SHARD = 100_000
+
+
+def _write_dataset(df: datasets.Dataset, path: str, filesystem) -> None:
+    table = df.data.table
+    writer = pq.ParquetWriter(strip_protocol(path), table.schema, filesystem=filesystem)
+
+    try:
+        for batch in table.to_batches(max_chunksize=10_000):
+            writer.write_batch(batch)
+    finally:
+        writer.close()
+
+
+def _read_parquet_files(
+    parquet_files: list[str],
+    columns: list[str] | None,
+    filesystem=None,
+) -> pa.Table:
+    tables = [
+        pq.read_table(
+            strip_protocol(file_path),
+            columns=columns,
+            filesystem=filesystem if storage.is_remote(file_path) else None,
+        )
+        for file_path in parquet_files
+    ]
+    return pa.concat_tables(tables) if len(tables) > 1 else tables[0]
+
+
+def _batch_to_rows(batch: pa.RecordBatch) -> list[dict]:
+    col_lists = {name: col.to_pylist() for name, col in zip(batch.schema.names, batch.columns)}
+    return [{name: col_lists[name][i] for name in batch.schema.names} for i in range(batch.num_rows)]
+
+
+def _iter_parquet_rows(parquet_files: list[str], columns: list[str] | None) -> typing.Iterator[dict]:
+    for file_path in parquet_files:
+        filesystem = storage.get_underlying_filesystem(path=file_path) if storage.is_remote(file_path) else None
+        pf = pq.ParquetFile(
+            strip_protocol(file_path),
+            filesystem=filesystem,
+        )
+        for batch in pf.iter_batches(batch_size=10_000, columns=columns):
+            yield from _batch_to_rows(batch)
+
+
+def _write_iterable_dataset(ds: datasets.IterableDataset, uri: str, filesystem) -> None:
+    file_idx = 0
+    rows_in_shard = 0
+    writer: pq.ParquetWriter | None = None
+
+    try:
+        for batch in ds.iter(batch_size=10_000):
+            table = pa.table(batch)
+
+            if writer is None:
+                shard_path = join_uri_path(uri, f"{file_idx:05}.parquet")
+                writer = pq.ParquetWriter(
+                    strip_protocol(shard_path),
+                    table.schema,
+                    filesystem=filesystem,
+                )
+
+            for arrow_batch in table.to_batches():
+                writer.write_batch(arrow_batch)
+
+            rows_in_shard += len(table)
+
+            if rows_in_shard >= _ROWS_PER_SHARD:
+                writer.close()
+                writer = None
+
+                file_idx += 1
+                rows_in_shard = 0
+    finally:
+        if writer is not None:
+            writer.close()
+
+
+def _requested_columns(
+    current_task_metadata: literals_pb2.StructuredDatasetMetadata,
+) -> list[str] | None:
+    if current_task_metadata.structured_dataset_type and current_task_metadata.structured_dataset_type.columns:
+        return [c.name for c in current_task_metadata.structured_dataset_type.columns]
+    return None
+
+
+async def _localize_parquet_files(parquet_files: list[str]) -> list[str]:
+    """Download remote parquet files before handing them to PyArrow."""
+    local_files: list[str] = []
+
+    for file_path in parquet_files:
+        if storage.is_remote(file_path):
+            local_dir = storage.get_random_local_directory()
+            local_name = pathlib.PurePosixPath(file_path.rstrip("/")).name
+            local_path = str(local_dir / local_name)
+            logger.info(f"Downloading remote parquet shard {file_path} to {local_path}")
+            local_files.append(await storage.get(file_path, local_path))
+        else:
+            local_files.append(strip_protocol(file_path))
+    return local_files
+
+
+def _probe_remote_parquet_files(parquet_files: list[str], filesystem) -> None:
+    for file_path in parquet_files:
+        if not storage.is_remote(file_path):
+            continue
+        pq.ParquetFile(strip_protocol(file_path), filesystem=filesystem).metadata
+
+
+class HuggingFaceDatasetToParquetEncodingHandler(DataFrameEncoder):
+    def __init__(self):
+        super().__init__(datasets.Dataset, None, PARQUET)
+
+    async def encode(
+        self,
+        dataframe: DataFrame,
+        structured_dataset_type: types_pb2.StructuredDatasetType,
+    ) -> literals_pb2.StructuredDataset:
+        val = dataframe.val
+
+        if val is None and dataframe.uri:
+            structured_dataset_type.format = PARQUET
+            return literals_pb2.StructuredDataset(
+                uri=typing.cast(str, dataframe.uri),
+                metadata=literals_pb2.StructuredDatasetMetadata(structured_dataset_type=structured_dataset_type),
+            )
+
+        if not dataframe.uri:
+            from flyte._context import internal_ctx
+
+            uri = str(internal_ctx().raw_data.get_random_remote_path())
+        else:
+            uri = typing.cast(str, dataframe.uri)
+
+        if not storage.is_remote(uri):
+            Path(uri).mkdir(parents=True, exist_ok=True)
+
+        path = join_uri_path(uri, f"{0:05}.parquet")
+        df = typing.cast(datasets.Dataset, val)
+
+        filesystem = storage.get_underlying_filesystem(path=path)
+        logger.info(
+            f"Writing Hugging Face Dataset output to "
+            f"{'remote' if storage.is_remote(uri) else 'local'} parquet directory {uri}"
+        )
+        await run_sync_io("write HuggingFace dataset", _write_dataset, df, path, filesystem)
+
+        structured_dataset_type.format = PARQUET
+        return literals_pb2.StructuredDataset(
+            uri=uri,
+            metadata=literals_pb2.StructuredDatasetMetadata(structured_dataset_type=structured_dataset_type),
+        )
+
+
+class ParquetToHuggingFaceDatasetDecodingHandler(DataFrameDecoder):
+    def __init__(self, protocol: str | None = None):
+        super().__init__(datasets.Dataset, protocol, PARQUET)
+
+    async def decode(
+        self,
+        flyte_value: literals_pb2.StructuredDataset,
+        current_task_metadata: literals_pb2.StructuredDatasetMetadata,
+    ) -> datasets.Dataset:
+        uri = flyte_value.uri
+
+        if uri.startswith("hf://"):
+            source_uri = uri
+            try:
+                uri = await ensure_hf_cached(HFSource.from_hf_uri(uri))
+            except Exception as e:
+                raise RuntimeError(
+                    f"Failed to materialize Hugging Face dataset from {uri}: {type(e).__name__}: {e!r}"
+                ) from e
+            logger.info(
+                f"Resolved Hugging Face source {source_uri} to "
+                f"{'remote' if storage.is_remote(uri) else 'local'} parquet directory {uri}"
+            )
+
+        filesystem = storage.get_underlying_filesystem(path=uri)
+        logger.info(
+            f"Reading Hugging Face Dataset parquet from "
+            f"{'remote' if storage.is_remote(uri) else 'local'} directory {uri}"
+        )
+
+        parquet_files = await list_parquet_files(uri, filesystem)
+        columns = _requested_columns(current_task_metadata)
+
+        try:
+            if any(storage.is_remote(file_path) for file_path in parquet_files):
+                logger.info(f"Using direct remote parquet reads for {uri} via Flyte storage filesystem")
+            table = await run_sync_io(
+                "read parquet files",
+                _read_parquet_files,
+                parquet_files,
+                columns,
+                filesystem,
+            )
+        except Exception as exc:
+            if any(storage.is_remote(file_path) for file_path in parquet_files):
+                logger.warning(
+                    f"Direct parquet read failed for {uri}: {type(exc).__name__}: {exc}. "
+                    "Falling back to localizing remote parquet shards."
+                )
+                parquet_files = await _localize_parquet_files(parquet_files)
+                logger.info(f"Using localized parquet shard reads for {uri}")
+                table = await run_sync_io(
+                    "read localized parquet files",
+                    _read_parquet_files,
+                    parquet_files,
+                    columns,
+                )
+            else:
+                raise
+
+        return datasets.Dataset(table)
+
+
+class HFToHuggingFaceDatasetDecodingHandler(ParquetToHuggingFaceDatasetDecodingHandler):
+    def __init__(self):
+        super().__init__("hf")
+
+
+class HuggingFaceIterableDatasetToParquetEncodingHandler(DataFrameEncoder):
+    def __init__(self):
+        super().__init__(datasets.IterableDataset, None, PARQUET)
+
+    async def encode(
+        self,
+        dataframe: DataFrame,
+        structured_dataset_type: types_pb2.StructuredDatasetType,
+    ) -> literals_pb2.StructuredDataset:
+        val = dataframe.val
+
+        if val is None and dataframe.uri:
+            structured_dataset_type.format = PARQUET
+            return literals_pb2.StructuredDataset(
+                uri=typing.cast(str, dataframe.uri),
+                metadata=literals_pb2.StructuredDatasetMetadata(structured_dataset_type=structured_dataset_type),
+            )
+
+        if not dataframe.uri:
+            from flyte._context import internal_ctx
+
+            uri = str(internal_ctx().raw_data.get_random_remote_path())
+        else:
+            uri = typing.cast(str, dataframe.uri)
+
+        if not storage.is_remote(uri):
+            Path(uri).mkdir(parents=True, exist_ok=True)
+
+        ds = typing.cast(datasets.IterableDataset, val)
+        filesystem = storage.get_underlying_filesystem(path=uri)
+        logger.info(
+            f"Writing Hugging Face IterableDataset output to "
+            f"{'remote' if storage.is_remote(uri) else 'local'} parquet directory {uri}"
+        )
+        await run_sync_io(
+            "write HuggingFace iterable dataset",
+            _write_iterable_dataset,
+            ds,
+            uri,
+            filesystem,
+        )
+
+        structured_dataset_type.format = PARQUET
+        return literals_pb2.StructuredDataset(
+            uri=uri,
+            metadata=literals_pb2.StructuredDatasetMetadata(structured_dataset_type=structured_dataset_type),
+        )
+
+
+class ParquetToHuggingFaceIterableDatasetDecodingHandler(DataFrameDecoder):
+    def __init__(self, protocol: str | None = None):
+        super().__init__(datasets.IterableDataset, protocol, PARQUET)
+
+    async def decode(
+        self,
+        flyte_value: literals_pb2.StructuredDataset,
+        current_task_metadata: literals_pb2.StructuredDatasetMetadata,
+    ) -> datasets.IterableDataset:
+        uri = flyte_value.uri
+
+        if uri.startswith("hf://"):
+            source_uri = uri
+            try:
+                uri = await ensure_hf_cached(HFSource.from_hf_uri(uri))
+            except Exception as e:
+                raise RuntimeError(
+                    f"Failed to materialize Hugging Face dataset from {uri}: {type(e).__name__}: {e!r}"
+                ) from e
+            logger.info(
+                f"Resolved Hugging Face source {source_uri} to "
+                f"{'remote' if storage.is_remote(uri) else 'local'} parquet directory {uri}"
+            )
+
+        filesystem = storage.get_underlying_filesystem(path=uri)
+        logger.info(
+            f"Reading Hugging Face IterableDataset parquet from "
+            f"{'remote' if storage.is_remote(uri) else 'local'} directory {uri}"
+        )
+        parquet_files = await list_parquet_files(uri, filesystem)
+        columns = _requested_columns(current_task_metadata)
+
+        try:
+            if any(storage.is_remote(file_path) for file_path in parquet_files):
+                logger.info(f"Using direct remote iterable parquet reads for {uri} via Flyte storage filesystem")
+            await run_sync_io(
+                "probe parquet files",
+                _probe_remote_parquet_files,
+                parquet_files,
+                filesystem,
+            )
+        except Exception as exc:
+            if any(storage.is_remote(file_path) for file_path in parquet_files):
+                logger.warning(
+                    f"Direct iterable parquet access failed for {uri}: {type(exc).__name__}: {exc}. "
+                    "Falling back to localizing remote parquet shards."
+                )
+                parquet_files = await _localize_parquet_files(parquet_files)
+                logger.info(f"Using localized iterable parquet shard reads for {uri}")
+            else:
+                raise
+
+        return datasets.IterableDataset.from_generator(
+            _iter_parquet_rows,
+            gen_kwargs={"parquet_files": parquet_files, "columns": columns},
+        )
+
+
+class HFToHuggingFaceIterableDatasetDecodingHandler(ParquetToHuggingFaceIterableDatasetDecodingHandler):
+    def __init__(self):
+        super().__init__("hf")
diff --git a/plugins/huggingface/tests/__init__.py b/plugins/huggingface/tests/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/plugins/huggingface/tests/conftest.py b/plugins/huggingface/tests/conftest.py
new file mode 100644
index 000000000..9a7861f0b
--- /dev/null
+++ b/plugins/huggingface/tests/conftest.py
@@ -0,0 +1,53 @@
+import os
+from pathlib import Path
+from unittest.mock import patch
+
+import pytest
+import pytest_asyncio
+from flyte._cache.local_cache import LocalTaskCache
+from flyte._context import RawDataPath, internal_ctx
+from flyte._persistence._db import LocalDB
+from flyte.models import SerializationContext
+
+
+@pytest.fixture
+def ctx_with_test_raw_data_path():
+    """Pytest fixture to set a RawDataPath in the internal_ctx."""
+    raw_data_path = RawDataPath.from_local_folder()
+    ctx = internal_ctx()
+    new_context = ctx.new_raw_data_path(raw_data_path=raw_data_path)
+    with new_context as ctx:
+        yield ctx
+
+
+@pytest.fixture
+def dummy_serialization_context():
+    yield SerializationContext(
+        code_bundle=None,
+        version="abc123",
+        input_path="s3://bucket/test/run/inputs.pb",
+        output_path="s3://bucket/outputs/0/jfkljfa/0",
+        root_dir=Path.cwd(),
+    )
+
+
+@pytest_asyncio.fixture(autouse=True)
+async def isolate_local_cache(tmp_path):
+    """
+    Global fixture to isolate LocalTaskCache for each test.
+    Uses temporary directory to avoid polluting local development cache.
+    """
+    with patch.object(LocalDB, "_get_db_path", staticmethod(lambda: str(tmp_path / "test_cache.db"))):
+        LocalDB._initialized = False
+        LocalDB._conn = None
+        LocalDB._conn_sync = None
+        yield
+        await LocalTaskCache.close()
+
+
+@pytest.fixture(autouse=True)
+def patch_os_exit(monkeypatch):
+    def mock_exit(code):
+        raise SystemExit(code)
+
+    monkeypatch.setattr(os, "_exit", mock_exit)
diff --git a/plugins/huggingface/tests/datasets/__init__.py b/plugins/huggingface/tests/datasets/__init__.py
new file mode 100644
index 000000000..8b1378917
--- /dev/null
+++ b/plugins/huggingface/tests/datasets/__init__.py
@@ -0,0 +1 @@
+
diff --git a/plugins/huggingface/tests/datasets/test_transformers.py b/plugins/huggingface/tests/datasets/test_transformers.py
new file mode 100644
index 000000000..4a1fe04a1
--- /dev/null
+++ b/plugins/huggingface/tests/datasets/test_transformers.py
@@ -0,0 +1,594 @@
+import typing
+from collections import OrderedDict
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import flyte
+import pytest
+from flyte.io import DataFrame
+from flyte.io._dataframe.dataframe import PARQUET, DataFrameTransformerEngine
+from flyte.types import TypeEngine
+from flyteidl2.core import literals_pb2
+
+from flyteplugins.huggingface.datasets import HFSource, from_hf, register_huggingface_dataset_transformers
+from flyteplugins.huggingface.datasets._io import (
+    HFParquetError,
+    _resolve_hf_config,
+    ensure_hf_cached,
+    get_hf_cache_path,
+    hf_cache_manifest,
+    join_uri_path,
+    list_parquet_files,
+)
+from flyteplugins.huggingface.datasets._source import HFShard, hf_source_cache_key
+from flyteplugins.huggingface.datasets._transformers import (
+    _ROWS_PER_SHARD,
+    HFToHuggingFaceDatasetDecodingHandler,
+    HFToHuggingFaceIterableDatasetDecodingHandler,
+    HuggingFaceDatasetToParquetEncodingHandler,
+    HuggingFaceIterableDatasetToParquetEncodingHandler,
+    ParquetToHuggingFaceDatasetDecodingHandler,
+    ParquetToHuggingFaceIterableDatasetDecodingHandler,
+    _iter_parquet_rows,
+    _read_parquet_files,
+)
+
+datasets = pytest.importorskip("datasets")
+register_huggingface_dataset_transformers()
+
+TEST_DATA = {
+    "text": ["hello world", "flyte rocks", "hugging face datasets"],
+    "label": [0, 1, 0],
+}
+
+
+@pytest.fixture
+def sample_dataset():
+    return datasets.Dataset.from_dict(TEST_DATA)
+
+
+# ============================================================================
+# HF source references
+# ============================================================================
+
+
+def test_from_hf_returns_dataframe_reference():
+    ref = from_hf("stanfordnlp/imdb", split="train")
+
+    assert isinstance(ref, DataFrame)
+    assert ref.uri == "hf://stanfordnlp/imdb?split=train"
+    assert ref.format == PARQUET
+    assert ref.hash is not None
+
+
+def test_hfsource_to_hf_uri_with_all_fields():
+    source = HFSource(
+        repo="stanfordnlp/imdb",
+        name="plain_text",
+        split="train",
+        revision="refs/convert/parquet",
+        cache_root="s3://bucket/flyte-hf-cache",
+    )
+    assert (
+        source.to_hf_uri() == "hf://stanfordnlp/imdb?name=plain_text&split=train"
+        "&cache_root=s3://bucket/flyte-hf-cache&revision=refs/convert/parquet"
+    )
+
+
+def test_hfsource_to_hf_uri_omits_name_and_split():
+    source = HFSource(repo="stanfordnlp/imdb")
+    assert source.to_hf_uri() == "hf://stanfordnlp/imdb"
+
+
+def test_hfsource_uri_roundtrip():
+    for source in [
+        HFSource(repo="stanfordnlp/imdb"),
+        HFSource(repo="stanfordnlp/imdb", split="train"),
+        HFSource(
+            repo="stanfordnlp/imdb",
+            name="plain_text",
+            split="test",
+            cache_root="s3://bucket/cache",
+            revision="refs/convert/parquet",
+        ),
+        HFSource(repo="glue", name="mrpc", split="train"),
+    ]:
+        assert HFSource.from_hf_uri(source.to_hf_uri()) == source
+
+
+def test_hfsource_rejects_blank_fields():
+    with pytest.raises(ValueError, match="repo must not be empty"):
+        HFSource(repo="  ")
+
+    with pytest.raises(ValueError, match="name must not be blank"):
+        HFSource(repo="stanfordnlp/imdb", name=" ")
+
+    with pytest.raises(ValueError, match="split must not be blank"):
+        HFSource(repo="stanfordnlp/imdb", split=" ")
+
+
+# ============================================================================
+# Config resolution
+# ============================================================================
+
+
+def test_resolve_hf_config_prefers_explicit_name():
+    hfs = MagicMock()
+    hfs.ls.side_effect = lambda path, revision=None, detail=True: {
+        "datasets/glue/mrpc": [
+            {"type": "directory", "name": "datasets/glue/mrpc/train"},
+        ]
+    }[path]
+
+    name, path, entries = _resolve_hf_config(hfs, HFSource(repo="glue", name="mrpc"), "refs/convert/parquet")
+
+    assert name == "mrpc"
+    assert path == "datasets/glue/mrpc"
+    assert entries == [{"type": "directory", "name": "datasets/glue/mrpc/train"}]
+
+
+def test_resolve_hf_config_uses_default_when_available():
+    hfs = MagicMock()
+
+    def ls(path, revision=None, detail=True):
+        if path == "datasets/org/ds/default":
+            return [{"type": "directory", "name": "datasets/org/ds/default/train"}]
+        raise FileNotFoundError(path)
+
+    hfs.ls.side_effect = ls
+
+    name, path, entries = _resolve_hf_config(hfs, HFSource(repo="org/ds"), "refs/convert/parquet")
+
+    assert name == "default"
+    assert path == "datasets/org/ds/default"
+    assert entries == [{"type": "directory", "name": "datasets/org/ds/default/train"}]
+
+
+def test_resolve_hf_config_falls_back_to_only_available_config():
+    hfs = MagicMock()
+
+    def ls(path, revision=None, detail=True):
+        if path == "datasets/stanfordnlp/imdb/default":
+            raise FileNotFoundError(path)
+        if path == "datasets/stanfordnlp/imdb":
+            return [{"type": "directory", "name": "datasets/stanfordnlp/imdb/plain_text"}]
+        if path == "datasets/stanfordnlp/imdb/plain_text":
+            return [
+                {
+                    "type": "directory",
+                    "name": "datasets/stanfordnlp/imdb/plain_text/train",
+                }
+            ]
+        raise FileNotFoundError(path)
+
+    hfs.ls.side_effect = ls
+
+    name, path, entries = _resolve_hf_config(hfs, HFSource(repo="stanfordnlp/imdb"), "refs/convert/parquet")
+
+    assert name == "plain_text"
+    assert path == "datasets/stanfordnlp/imdb/plain_text"
+    assert entries == [{"type": "directory", "name": "datasets/stanfordnlp/imdb/plain_text/train"}]
+
+
+def test_resolve_hf_config_requires_name_when_multiple_configs_exist():
+    hfs = MagicMock()
+
+    def ls(path, revision=None, detail=True):
+        if path == "datasets/glue/default":
+            raise FileNotFoundError(path)
+        if path == "datasets/glue":
+            return [
+                {"type": "directory", "name": "datasets/glue/mrpc"},
+                {"type": "directory", "name": "datasets/glue/sst2"},
+            ]
+        raise FileNotFoundError(path)
+
+    hfs.ls.side_effect = ls
+
+    with pytest.raises(HFParquetError, match="multiple parquet configs"):
+        _resolve_hf_config(hfs, HFSource(repo="glue"), "refs/convert/parquet")
+
+
+# ============================================================================
+# Registration and handler properties
+# ============================================================================
+
+
+def test_registered_dataset_handlers():
+    assert isinstance(
+        DataFrameTransformerEngine.get_encoder(datasets.Dataset, "file", PARQUET),
+        HuggingFaceDatasetToParquetEncodingHandler,
+    )
+    assert isinstance(
+        DataFrameTransformerEngine.get_decoder(datasets.Dataset, "file", PARQUET),
+        ParquetToHuggingFaceDatasetDecodingHandler,
+    )
+    assert isinstance(
+        DataFrameTransformerEngine.get_decoder(datasets.Dataset, "hf", PARQUET),
+        HFToHuggingFaceDatasetDecodingHandler,
+    )
+
+
+def test_registered_iterable_handlers():
+    assert isinstance(
+        DataFrameTransformerEngine.get_encoder(datasets.IterableDataset, "file", PARQUET),
+        HuggingFaceIterableDatasetToParquetEncodingHandler,
+    )
+    assert isinstance(
+        DataFrameTransformerEngine.get_decoder(datasets.IterableDataset, "file", PARQUET),
+        ParquetToHuggingFaceIterableDatasetDecodingHandler,
+    )
+    assert isinstance(
+        DataFrameTransformerEngine.get_decoder(datasets.IterableDataset, "hf", PARQUET),
+        HFToHuggingFaceIterableDatasetDecodingHandler,
+    )
+
+
+# ============================================================================
+# datasets.Dataset roundtrip
+# ============================================================================
+
+
+@pytest.mark.asyncio
+async def test_dataset_roundtrip(ctx_with_test_raw_data_path, sample_dataset):
+    fdt = DataFrameTransformerEngine()
+    lt = TypeEngine.to_literal_type(datasets.Dataset)
+
+    lit = await fdt.to_literal(sample_dataset, python_type=datasets.Dataset, expected=lt)
+    assert lit.scalar.structured_dataset.metadata.structured_dataset_type.format == PARQUET
+
+    restored = await fdt.to_python_value(lit, expected_python_type=datasets.Dataset)
+    assert isinstance(restored, datasets.Dataset)
+    assert len(restored) == len(sample_dataset)
+    assert restored.column_names == sample_dataset.column_names
+    assert restored["text"] == sample_dataset["text"]
+
+
+@pytest.mark.asyncio
+async def test_dataset_column_projection(ctx_with_test_raw_data_path, sample_dataset):
+    fdt = DataFrameTransformerEngine()
+    lt = TypeEngine.to_literal_type(datasets.Dataset)
+    lit = await fdt.to_literal(sample_dataset, python_type=datasets.Dataset, expected=lt)
+
+    columns = OrderedDict(text=str)
+    restored = await fdt.to_python_value(
+        lit,
+        expected_python_type=typing.Annotated[datasets.Dataset, columns],
+    )
+
+    assert isinstance(restored, datasets.Dataset)
+    assert restored.column_names == ["text"]
+
+
+@pytest.mark.asyncio
+async def test_dataset_passthrough_task(sample_dataset):
+    flyte.init()
+    env = flyte.TaskEnvironment(name="hf-dataset-test")
+
+    @env.task
+    async def select_two(ds: datasets.Dataset) -> datasets.Dataset:
+        return ds.select(range(2))
+
+    run = flyte.with_runcontext("local").run(select_two, sample_dataset)
+    result = run.outputs()[0]
+
+    assert isinstance(result, datasets.Dataset)
+    assert len(result) == 2
+
+
+# ============================================================================
+# hf:// source literals
+# ============================================================================
+
+
+@pytest.mark.asyncio
+async def test_encode_from_hf_reference_as_dataset_emits_hf_uri(
+    ctx_with_test_raw_data_path,
+):
+    fdt = DataFrameTransformerEngine()
+    lt = TypeEngine.to_literal_type(datasets.Dataset)
+
+    ref = from_hf(
+        "stanfordnlp/imdb",
+        name="plain_text",
+        split="train",
+        cache_root="s3://bucket/cache",
+    )
+    lit = await fdt.to_literal(ref, python_type=datasets.Dataset, expected=lt)
+
+    assert (
+        lit.scalar.structured_dataset.uri
+        == "hf://stanfordnlp/imdb?name=plain_text&split=train&cache_root=s3://bucket/cache"
+    )
+    assert lit.scalar.structured_dataset.metadata.structured_dataset_type.format == PARQUET
+
+
+@pytest.mark.asyncio
+async def test_encode_from_hf_reference_as_iterable_dataset_emits_hf_uri(
+    ctx_with_test_raw_data_path,
+):
+    fdt = DataFrameTransformerEngine()
+    lt = TypeEngine.to_literal_type(datasets.IterableDataset)
+
+    ref = from_hf("stanfordnlp/imdb", split="train")
+    lit = await fdt.to_literal(ref, python_type=datasets.IterableDataset, expected=lt)
+
+    assert lit.scalar.structured_dataset.uri == "hf://stanfordnlp/imdb?split=train"
+
+
+@pytest.mark.asyncio
+async def test_decode_hf_uri_as_dataset_uses_cache_materialization(ctx_with_test_raw_data_path, sample_dataset):
+    fdt = DataFrameTransformerEngine()
+    lt = TypeEngine.to_literal_type(datasets.Dataset)
+    cached_lit = await fdt.to_literal(sample_dataset, python_type=datasets.Dataset, expected=lt)
+    cached_uri = cached_lit.scalar.structured_dataset.uri
+
+    hf_lit = literals_pb2.Literal(
+        scalar=literals_pb2.Scalar(
+            structured_dataset=literals_pb2.StructuredDataset(
+                uri="hf://stanfordnlp/imdb?split=train",
+                metadata=cached_lit.scalar.structured_dataset.metadata,
+            )
+        )
+    )
+
+    with patch(
+        "flyteplugins.huggingface.datasets._transformers.ensure_hf_cached",
+        new=AsyncMock(return_value=cached_uri),
+    ) as ensure_cached:
+        restored = await fdt.to_python_value(hf_lit, expected_python_type=datasets.Dataset)
+
+    ensure_cached.assert_awaited_once_with(HFSource(repo="stanfordnlp/imdb", split="train"))
+    assert isinstance(restored, datasets.Dataset)
+    assert len(restored) == len(sample_dataset)
+
+
+@pytest.mark.asyncio
+async def test_decode_hf_uri_as_iterable_dataset_uses_cache_materialization(
+    ctx_with_test_raw_data_path, sample_dataset
+):
+    fdt = DataFrameTransformerEngine()
+    cached_lit = await fdt.to_literal(
+        sample_dataset,
+        python_type=datasets.Dataset,
+        expected=TypeEngine.to_literal_type(datasets.Dataset),
+    )
+    cached_uri = cached_lit.scalar.structured_dataset.uri
+
+    hf_lit = literals_pb2.Literal(
+        scalar=literals_pb2.Scalar(
+            structured_dataset=literals_pb2.StructuredDataset(
+                uri="hf://stanfordnlp/imdb?name=plain_text&split=test",
+                metadata=cached_lit.scalar.structured_dataset.metadata,
+            )
+        )
+    )
+
+    with patch(
+        "flyteplugins.huggingface.datasets._transformers.ensure_hf_cached",
+        new=AsyncMock(return_value=cached_uri),
+    ) as ensure_cached:
+        restored = await fdt.to_python_value(hf_lit, expected_python_type=datasets.IterableDataset)
+
+    ensure_cached.assert_awaited_once_with(HFSource(repo="stanfordnlp/imdb", name="plain_text", split="test"))
+    assert isinstance(restored, datasets.IterableDataset)
+    assert len(list(restored)) == len(sample_dataset)
+
+
+@pytest.mark.asyncio
+async def test_dataset_decode_remote_prefers_direct_filesystem_read(sample_dataset):
+    handler = ParquetToHuggingFaceDatasetDecodingHandler()
+    fs = object()
+    parquet_files = ["s3://bucket/path/00000.parquet"]
+    lit = literals_pb2.StructuredDataset(uri="s3://bucket/path")
+    metadata = literals_pb2.StructuredDatasetMetadata()
+
+    with (
+        patch(
+            "flyteplugins.huggingface.datasets._transformers.storage.get_underlying_filesystem",
+            return_value=fs,
+        ),
+        patch(
+            "flyteplugins.huggingface.datasets._transformers.list_parquet_files",
+            new=AsyncMock(return_value=parquet_files),
+        ),
+        patch(
+            "flyteplugins.huggingface.datasets._transformers.run_sync_io",
+            new=AsyncMock(return_value=sample_dataset.data.table),
+        ) as run_sync,
+        patch(
+            "flyteplugins.huggingface.datasets._transformers._localize_parquet_files",
+            new=AsyncMock(),
+        ) as localize,
+        patch("flyteplugins.huggingface.datasets._transformers.logger.info") as log_info,
+    ):
+        restored = await handler.decode(lit, metadata)
+
+    localize.assert_not_awaited()
+    run_sync.assert_awaited_once_with("read parquet files", _read_parquet_files, parquet_files, None, fs)
+    assert isinstance(restored, datasets.Dataset)
+    log_info.assert_any_call("Using direct remote parquet reads for s3://bucket/path via Flyte storage filesystem")
+
+
+@pytest.mark.asyncio
+async def test_dataset_decode_remote_falls_back_to_localized_reads(sample_dataset):
+    handler = ParquetToHuggingFaceDatasetDecodingHandler()
+    fs = object()
+    remote_files = ["s3://bucket/path/00000.parquet"]
+    local_files = ["/tmp/00000.parquet"]
+    lit = literals_pb2.StructuredDataset(uri="s3://bucket/path")
+    metadata = literals_pb2.StructuredDatasetMetadata()
+
+    with (
+        patch(
+            "flyteplugins.huggingface.datasets._transformers.storage.get_underlying_filesystem",
+            return_value=fs,
+        ),
+        patch(
+            "flyteplugins.huggingface.datasets._transformers.list_parquet_files",
+            new=AsyncMock(return_value=remote_files),
+        ),
+        patch(
+            "flyteplugins.huggingface.datasets._transformers.run_sync_io",
+            new=AsyncMock(side_effect=[RuntimeError("boom"), sample_dataset.data.table]),
+        ) as run_sync,
+        patch(
+            "flyteplugins.huggingface.datasets._transformers._localize_parquet_files",
+            new=AsyncMock(return_value=local_files),
+        ) as localize,
+        patch("flyteplugins.huggingface.datasets._transformers.logger.info") as log_info,
+        patch("flyteplugins.huggingface.datasets._transformers.logger.warning") as log_warning,
+    ):
+        restored = await handler.decode(lit, metadata)
+
+    localize.assert_awaited_once_with(remote_files)
+    assert run_sync.await_args_list[0].args == ("read parquet files", _read_parquet_files, remote_files, None, fs)
+    assert run_sync.await_args_list[1].args == ("read localized parquet files", _read_parquet_files, local_files, None)
+    assert isinstance(restored, datasets.Dataset)
+    log_warning.assert_called_once()
+    log_info.assert_any_call("Using localized parquet shard reads for s3://bucket/path")
+
+
+@pytest.mark.asyncio
+async def test_iterable_decode_remote_uses_picklable_generator_args():
+    handler = ParquetToHuggingFaceIterableDatasetDecodingHandler()
+    fs = object()
+    parquet_files = ["s3://bucket/path/00000.parquet"]
+    lit = literals_pb2.StructuredDataset(uri="s3://bucket/path")
+    metadata = literals_pb2.StructuredDatasetMetadata()
+
+    sentinel = object()
+    with (
+        patch(
+            "flyteplugins.huggingface.datasets._transformers.storage.get_underlying_filesystem",
+            return_value=fs,
+        ),
+        patch(
+            "flyteplugins.huggingface.datasets._transformers.list_parquet_files",
+            new=AsyncMock(return_value=parquet_files),
+        ),
+        patch(
+            "flyteplugins.huggingface.datasets._transformers.run_sync_io",
+            new=AsyncMock(return_value=None),
+        ),
+        patch(
+            "flyteplugins.huggingface.datasets._transformers.datasets.IterableDataset.from_generator",
+            return_value=sentinel,
+        ) as from_generator,
+    ):
+        restored = await handler.decode(lit, metadata)
+
+    from_generator.assert_called_once_with(
+        _iter_parquet_rows,
+        gen_kwargs={"parquet_files": parquet_files, "columns": None},
+    )
+    assert restored is sentinel
+
+
+# ============================================================================
+# datasets.IterableDataset roundtrip
+# ============================================================================
+
+
+@pytest.mark.asyncio
+async def test_iterable_dataset_roundtrip(ctx_with_test_raw_data_path, sample_dataset):
+    iterable_ds = sample_dataset.to_iterable_dataset()
+    fdt = DataFrameTransformerEngine()
+    lt = TypeEngine.to_literal_type(datasets.IterableDataset)
+
+    lit = await fdt.to_literal(iterable_ds, python_type=datasets.IterableDataset, expected=lt)
+    restored = await fdt.to_python_value(lit, expected_python_type=datasets.IterableDataset)
+
+    assert isinstance(restored, datasets.IterableDataset)
+    rows = list(restored)
+    assert len(rows) == len(sample_dataset)
+    assert set(rows[0].keys()) == set(sample_dataset.column_names)
+
+
+@pytest.mark.asyncio
+async def test_iterable_dataset_large_sharding(ctx_with_test_raw_data_path):
+    big_ds = datasets.Dataset.from_dict({"x": list(range(_ROWS_PER_SHARD + 1))})
+    iterable_ds = big_ds.to_iterable_dataset()
+
+    fdt = DataFrameTransformerEngine()
+    lt = TypeEngine.to_literal_type(datasets.IterableDataset)
+
+    lit = await fdt.to_literal(iterable_ds, python_type=datasets.IterableDataset, expected=lt)
+    uri = lit.scalar.structured_dataset.uri
+
+    import flyte.storage as storage
+
+    fs = storage.get_underlying_filesystem(path=uri)
+    files = await list_parquet_files(uri, fs)
+
+    assert len(files) == 2
+
+    restored = await fdt.to_python_value(lit, expected_python_type=datasets.IterableDataset)
+    assert len(list(restored)) == _ROWS_PER_SHARD + 1
+
+
+@pytest.mark.asyncio
+async def test_iterable_dataset_passthrough_task(sample_dataset):
+    flyte.init()
+    env = flyte.TaskEnvironment(name="hf-iterable-test")
+
+    @env.task
+    async def passthrough(ds: datasets.IterableDataset) -> datasets.IterableDataset:
+        return ds
+
+    iterable_ds = sample_dataset.to_iterable_dataset()
+    run = flyte.with_runcontext("local").run(passthrough, iterable_ds)
+    result = run.outputs()[0]
+
+    assert isinstance(result, datasets.IterableDataset)
+    assert len(list(result)) == len(sample_dataset)
+
+
+# ============================================================================
+# Helpers
+# ============================================================================
+
+
+def test_join_uri_path_preserves_uri_scheme():
+    assert join_uri_path("s3://bucket/root/", "/a/", "b") == "s3://bucket/root/a/b"
+    assert join_uri_path("hf://stanfordnlp/imdb", "train") == "hf://stanfordnlp/imdb/train"
+    assert join_uri_path("datasets", "stanfordnlp/imdb", "plain_text") == "datasets/stanfordnlp/imdb/plain_text"
+
+
+@pytest.mark.asyncio
+async def test_ensure_hf_cached_ignores_registry_artifact_uri_and_uses_canonical_path():
+    source = HFSource(
+        repo="stanfordnlp/imdb",
+        name="plain_text",
+        split="train",
+        cache_root="s3://bucket/flyte-hf-cache",
+    )
+    shards = [
+        HFShard(
+            rel_path="00000.parquet",
+            hf_name="datasets/stanfordnlp/imdb/plain_text/train/00000.parquet",
+            size=123,
+            etag="etag-1",
+        )
+    ]
+    cache_key = hf_source_cache_key(source, shards)
+    expected_manifest = hf_cache_manifest(source, shards, cache_key)
+    default_remote_path = get_hf_cache_path(source, cache_key)
+
+    with (
+        patch(
+            "flyteplugins.huggingface.datasets._io.run_sync_io",
+            new=AsyncMock(return_value=shards),
+        ),
+        patch(
+            "flyteplugins.huggingface.datasets._io.read_registry_record",
+            new=AsyncMock(return_value={"artifact_uri": "s3://bucket/old-layout/location"}),
+        ),
+        patch(
+            "flyteplugins.huggingface.datasets._io.read_cache_manifest",
+            new=AsyncMock(return_value=expected_manifest),
+        ) as read_manifest,
+    ):
+        remote_path = await ensure_hf_cached(source)
+
+    read_manifest.assert_awaited_once_with(default_remote_path)
+    assert remote_path == default_remote_path
diff --git a/plugins/huggingface/uv.lock b/plugins/huggingface/uv.lock
new file mode 100644
index 000000000..58902d8b4
--- /dev/null
+++ b/plugins/huggingface/uv.lock
@@ -0,0 +1,2966 @@
+version = 1
+revision = 3
+requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version >= '3.15' and sys_platform == 'win32'",
+    "python_full_version >= '3.15' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.15' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.14.*' and sys_platform == 'win32'",
+    "python_full_version == '3.14.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.14.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.12.*' and sys_platform == 'win32'",
+    "python_full_version == '3.12.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.11.*' and sys_platform == 'win32'",
+    "python_full_version == '3.11.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.11'",
+]
+
+[[package]]
+name = "aiofiles"
+version = "25.1.0"
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