diff --git a/src/axolotl/core/datasets.py b/src/axolotl/core/datasets.py
new file mode 100644
index 000000000..5feff37c2
--- /dev/null
+++ b/src/axolotl/core/datasets.py
@@ -0,0 +1,144 @@
+import logging
+from dataclasses import dataclass, field
+from enum import Enum
+from pathlib import Path
+from typing import Any, Dict, Generator, List, Optional, Union
+
+from datasets import Dataset as Dataset_ds
+from datasets import DatasetDict, IterableDataset, load_dataset, load_from_disk
+from huggingface_hub import hf_hub_download
+
+logger = logging.getLogger("axolotl")
+
+
+class DsType(Enum):
+    JSON = "json"
+    ARROW = "arrow"
+    PARQUET = "parquet"
+
+
+@dataclass
+class DatasetConfiguration:
+    path: str
+    type: str
+    name: Optional[str] = field(
+        default=None,
+        metadata={"help": "the name of the dataset configuration to load."},
+    )
+    ds_type: Optional[DsType] = None
+    data_files: Optional[Union[str, List[str]]] = None
+    shards: Optional[int] = None
+    test_size: Optional[float] = None
+
+    @staticmethod
+    def from_dict(d: Dict[str, Any]) -> Generator["DatasetConfiguration", None, None]:
+        if "name" in d and isinstance(d["name"], list):
+            name = d.pop("name")
+            for n in name:
+                yield DatasetConfiguration(
+                    **d,
+                    name=n,
+                )
+
+
+def load_dataset_from_local(config: DatasetConfiguration) -> Optional[Dataset_ds]:
+    local_path = Path(config.path)
+    if not local_path.exists():
+        return None
+    ds = None
+    if local_path.is_dir():
+        if config.ds_type:
+            # TODO dirs with arrow or parquet files could be loaded with `load_from_disk`
+            ds = load_from_disk(config.path)
+        else:
+            ds = load_dataset(
+                config.path,
+                name=config.name,
+                data_files=config.data_files,
+                streaming=False,
+                split=None,
+            )
+    elif local_path.is_file():
+        ds_type = "json"
+        if config.ds_type:
+            ds_type = config.ds_type.value
+        elif "parquet" in config.path:
+            ds_type = "parquet"
+        elif "arrow" in config.path:
+            ds_type = "arrow"
+        ds = load_dataset(
+            ds_type,
+            name=config.name,
+            data_files=config.path,
+            streaming=False,
+            split=None,  # is this correct?
+        )
+    if not ds:
+        raise ValueError(
+            "unhandled dataset load: local path exists, but is neither a directory or a file"
+        )
+    return ds
+
+
+# TODO should this be a DatasetDict?
+class Dataset(Dataset_ds):
+    _config: DatasetConfiguration
+
+    def __init__(self, *args, config: DatasetConfiguration = None, **kwargs):
+        self._config = config
+        super().__init__(*args, **kwargs)
+
+    @staticmethod
+    def from_config(
+        config: DatasetConfiguration,
+        token: bool = False,
+        default_test_size: float = 0.1,
+    ):
+        ds = load_dataset_from_local(config)
+        if not ds:
+            try:
+                ds = load_dataset(
+                    config.path,
+                    name=config.name,
+                    data_files=config.data_files,
+                    token=token,
+                )
+            except FileNotFoundError:
+                pass
+        if not ds:
+            fp = hf_hub_download(
+                repo_id=config.path,
+                repo_type="dataset",
+                filename=config.data_files,
+                token=token,
+            )
+            ds = load_dataset(
+                "json", name=config.name, data_files=fp, streaming=False, split=None
+            )
+        if not ds:
+            raise ValueError("unhandled dataset load")
+        test_size = config.test_size if config.test_size else default_test_size
+        # determine if the dataset is pre-tokenized
+        check_ds = ds["train"] if isinstance(ds, DatasetDict) and "train" in ds else ds
+        is_ds_tokenized = False
+        if "input_ids" in check_ds.features:
+            is_ds_tokenized = True
+            if "attention_mask" not in check_ds.features:
+                logger.warning("`attention_mask` missing from pre-tokenized dataset")
+            if "labels" not in check_ds.features:
+                logger.warning("`labels` missing from pre-tokenized dataset")
+        if test_size and (not isinstance(ds, DatasetDict) or "test" not in ds):
+            ds.train_test_split(test_size=test_size, shuffle=False)
+            pass
+
+
+class DatasetCollection:
+    datasets: List[Dataset] = []
+
+    def __init__(self, datasets: Union[Dataset, List[Dataset]]):
+        self.datasets = datasets if isinstance(datasets, list) else [datasets]
+
+    def __iter__(self):
+        for ds in self.datasets:
+            for d in ds:
+                yield d