diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index fc4114d..c6631dc 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -1,5 +1,8 @@
 name: Lint
-on: [push, pull_request]
+on:
+  push:
+    branches: [main]
+  pull_request:
 jobs:
   lint:
     runs-on: ubuntu-latest
diff --git a/examples/simple.py b/examples/simple.py
index e71e48c..a3e44f7 100644
--- a/examples/simple.py
+++ b/examples/simple.py
@@ -23,13 +23,13 @@
 # Import ScratchGPT components
 from scratchgpt import (
     CharTokenizer,
-    FileDataSource,
     ScratchGPTArchitecture,
     ScratchGPTConfig,
     ScratchGPTTraining,
     Trainer,
     TransformerLanguageModel,
 )
+from scratchgpt.data import create_data_source
 
 
 def download_darwin_text(data_file: Path) -> None:
@@ -67,24 +67,21 @@ def create_simple_config() -> ScratchGPTConfig:
         random_seed=1337,
     )
 
-    return ScratchGPTConfig(
-        architecture=architecture,
-        training=training
-    )
+    return ScratchGPTConfig(architecture=architecture, training=training)
 
 
 def prepare_text_for_tokenizer(data_file: Path) -> str:
     """Read the text file for tokenization."""
     print(f"Reading text from: {data_file}")
 
-    with open(data_file, encoding='utf-8') as f:
+    with open(data_file, encoding="utf-8") as f:
         text = f.read()
 
     print(f"Text length: {len(text):,} characters")
     return text
 
 
-def main():
+def main() -> None:
     print("ScratchGPT Simple Training Example")
     print("=" * 50)
 
@@ -104,7 +101,7 @@ def main():
         print(f"Vocabulary size: {tokenizer.vocab_size}")
 
         # Alternative: Use a pre-trained tokenizer like GPT-2
-        # This requires: pip install 'scratchgpt[hf-tokenizers]'
+        # This requires: uv sync --extra hf-tokenizers
         #
         # from scratchgpt import HuggingFaceTokenizer
         # tokenizer = HuggingFaceTokenizer.from_hub("gpt2")
@@ -118,8 +115,10 @@ def main():
         # Step 3: Create configuration
         config = create_simple_config()
         config.architecture.vocab_size = tokenizer.vocab_size
-        print(f"Model configuration: {config.architecture.embedding_size}D embeddings, "
-              f"{config.architecture.num_blocks} blocks, {config.architecture.num_heads} heads")
+        print(
+            f"Model configuration: {config.architecture.embedding_size}D embeddings, "
+            f"{config.architecture.num_blocks} blocks, {config.architecture.num_heads} heads"
+        )
 
         # Step 4: Setup model and training
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -130,7 +129,7 @@ def main():
         print(f"Model parameters: {sum(p.numel() for p in model.parameters()):,}")
 
         optimizer = AdamW(model.parameters(), lr=config.training.learning_rate)
-        data_source = FileDataSource(data_file)
+        data_source = create_data_source(str(data_file))
 
         # Step 5: Create trainer and start training
         trainer = Trainer(
@@ -138,14 +137,14 @@ def main():
             config=config.training,
             optimizer=optimizer,
             experiment_path=experiment_dir,
-            device=device
+            device=device,
         )
 
         print("\nStarting training...")
         print("(Press Ctrl-C to stop training early and see text generation)")
 
         try:
-            trainer.train(data=data_source, tokenizer=tokenizer)
+            trainer.train(data_source=data_source, tokenizer=tokenizer)
             print("\nTraining completed successfully!")
         except KeyboardInterrupt:
             print("\n\nTraining interrupted by user. Moving to text generation with current model state...")
@@ -154,11 +153,7 @@ def main():
         print("\nTesting text generation:")
         model.eval()
 
-        test_prompts = [
-            "Natural selection",
-            "The origin of species",
-            "Darwin observed"
-        ]
+        test_prompts = ["Natural selection", "The origin of species", "Darwin observed"]
 
         for prompt in test_prompts:
             print(f"\nPrompt: '{prompt}'")
diff --git a/pyproject.toml b/pyproject.toml
index c9a09c8..d6ced9a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,6 +24,7 @@ classifiers = [
 license = {file = "LICENSE"}
 
 dependencies = [
+    "datasets>=4.0.0",
     "numpy>=2.3.2",
     "ptflops>=0.7.5",
     "pydantic-settings>=2.10.1",
@@ -69,7 +70,7 @@ strict = true
 exclude = [".venv"]
 
 [[tool.mypy.overrides]]
-module = ["ptflops", "tokenizers.*", "huggingface_hub.*"]
+module = ["ptflops", "tokenizers.*", "huggingface_hub.*", "datasets.*"]
 ignore_missing_imports = true
 
 [tool.ruff]
diff --git a/scratchgpt/__init__.py b/scratchgpt/__init__.py
index 8656c80..85e90b9 100644
--- a/scratchgpt/__init__.py
+++ b/scratchgpt/__init__.py
@@ -7,13 +7,8 @@
     ScratchGPTConfig,
     ScratchGPTTraining,
 )
-from scratchgpt.data.datasource import (
-    ByteSizableDataSource,
-    DataSource,
-    FileDataSource,
-    FolderDataSource,
-    LineByLineFileDataSource,
-)
+from scratchgpt.data.datasource import DataSource
+from scratchgpt.data.hf_datasource import HFDataSource
 from scratchgpt.model.model import TransformerLanguageModel
 from scratchgpt.model_io import (
     ModelLoadFailedError,
@@ -32,7 +27,7 @@
 )
 from scratchgpt.tokenizer.char_tokenizer import CharTokenizer, Utf8Tokenizer
 from scratchgpt.tokenizer.hf_tokenizer import HuggingFaceTokenizer
-from scratchgpt.training.trainer import Trainer, get_dtype_for_vocab_size
+from scratchgpt.training.trainer import Trainer
 
 __all__ = [
     # Core Model and Config
@@ -42,10 +37,7 @@
     "ScratchGPTTraining",
     # Data Sources
     "DataSource",
-    "ByteSizableDataSource",
-    "FileDataSource",
-    "FolderDataSource",
-    "LineByLineFileDataSource",
+    "HFDataSource",
     # Model I/O
     "load_model",
     "load_tokenizer",
@@ -64,5 +56,4 @@
     "HuggingFaceTokenizer",
     # Training
     "Trainer",
-    "get_dtype_for_vocab_size",
 ]
diff --git a/scratchgpt/config.py b/scratchgpt/config.py
index ae0a592..4683771 100644
--- a/scratchgpt/config.py
+++ b/scratchgpt/config.py
@@ -1,7 +1,7 @@
 import math
-from typing import Annotated
+from typing import Annotated, Self
 
-from pydantic import AfterValidator, Field
+from pydantic import AfterValidator, Field, model_validator
 from pydantic_settings import (
     BaseSettings,
     PydanticBaseSettingsSource,
@@ -18,6 +18,10 @@ def ensure_split_is_valid(v: tuple[float, float]) -> tuple[float, float]:
     is_valid_split = math.isclose(splits_sum, 1.0)
     if not is_valid_split:
         raise ValueError("Invalid data 'split'")
+
+    val_split = v[1]
+    if val_split == 0.0:
+        raise ValueError("You can't have 0 sized validation split.")
     return v
 
 
@@ -36,6 +40,18 @@ class ScratchGPTArchitecture(BaseSettings):
     num_blocks: int = 6
     vocab_size: int | None = None
 
+    @model_validator(mode="after")
+    def validate_embedding_and_heads(self) -> Self:
+        """
+        Ensures that the embedding_size is perfectly divisible by the number of attention heads.
+        """
+        if self.embedding_size % self.num_heads != 0:
+            raise ValueError(
+                f"Incompatible model architecture: embedding_size ({self.embedding_size}) "
+                f"must be divisible by num_heads ({self.num_heads})."
+            )
+        return self
+
     model_config = SettingsConfigDict(
         env_prefix="ARCHITECTURE_",
         extra="allow",
diff --git a/scratchgpt/core/types.py b/scratchgpt/core/types.py
index 32ba9b8..4929412 100644
--- a/scratchgpt/core/types.py
+++ b/scratchgpt/core/types.py
@@ -1,4 +1,4 @@
 from torch import Tensor
 from torch.utils.data import DataLoader
 
-TensorTupleLoader = DataLoader[tuple[Tensor, Tensor]]
+DictTensorLoader = DataLoader[dict[str, Tensor]]
diff --git a/scratchgpt/data/__init__.py b/scratchgpt/data/__init__.py
index e69de29..c1ea931 100644
--- a/scratchgpt/data/__init__.py
+++ b/scratchgpt/data/__init__.py
@@ -0,0 +1,52 @@
+from typing import Any
+
+from scratchgpt.data.datasource import DataSource
+from scratchgpt.data.hf_datasource import HFDataSource
+
+
+def create_data_source(
+    path_or_name: str,
+    split: str = "train",
+    streaming: bool = False,
+    text_column: str = "text",
+    **kwargs: Any,
+) -> DataSource:
+    """
+    Create a data source from a path or dataset name.
+
+    Examples:
+        # HuggingFace Hub dataset
+        >>> ds = create_data_source("wikitext-2-raw-v1")
+
+        # Local text file
+        >>> ds = create_data_source("data.txt")
+
+        # Local CSV file
+        >>> ds = create_data_source("data.csv", text_column="content")
+
+        # Folder of text files
+        >>> ds = create_data_source("./texts/")
+
+        # Streaming large dataset
+        >>> ds = create_data_source("openwebtext", streaming=True)
+
+    Args:
+        path_or_name: HF Hub dataset name or path to local data
+        split: Dataset split to use
+        streaming: Whether to use streaming mode
+        text_column: Column name containing text
+        **kwargs: Additional arguments for HFDataSource
+
+    Returns:
+        DataSource instance
+    """
+    return HFDataSource(
+        path_or_name=path_or_name,
+        split=split,
+        streaming=streaming,
+        text_column=text_column,
+        **kwargs,
+    )
+
+
+__all__ = ["DataSource", "HFDataSource", "create_data_source"]
diff --git a/scratchgpt/data/datasource.py b/scratchgpt/data/datasource.py
index 3bfcedd..2acfe74 100644
--- a/scratchgpt/data/datasource.py
+++ b/scratchgpt/data/datasource.py
@@ -1,90 +1,26 @@
-from collections.abc import Iterator
-from pathlib import Path
-from typing import Protocol, runtime_checkable
+from typing import Protocol
+
+from scratchgpt.core.types import DictTensorLoader
+from scratchgpt.tokenizer.base_tokenizer import Tokenizer
 
 
-@runtime_checkable
 class DataSource(Protocol):
     """
-    An interface for providing raw data to the Trainer.
+    A protocol for classes that can provide training and validation DataLoaders.
 
-    A DataSource is an iterable object that yields individual,
-    untokenized training samples as strings.
+    This uses structural subtyping. Any class that implements a matching
+    `get_dataloaders` method will be considered a valid DataSource.
     """
 
-    def __iter__(self) -> Iterator[str]:
-        """Returns an iterator over the raw text samples."""
+    def get_dataloaders(
+        self,
+        tokenizer: Tokenizer,
+        block_size: int,
+        batch_size: int,
+        splits: tuple[float, float],
+        random_seed: int,
+    ) -> tuple[DictTensorLoader, DictTensorLoader | None]:
+        """
+        Processes data and returns train and validation DataLoaders.
+        """
         ...
-
-
-@runtime_checkable
-class ByteSizableDataSource(DataSource, Protocol):
-    """An optional extension for DataSources that can report their total size in bytes."""
-
-    def total_bytes(self) -> int:
-        """Returns the total size of the data source in bytes."""
-        ...
-
-
-class FileDataSource(ByteSizableDataSource):
-    """Yields the entire content of a single text file as one sample."""
-
-    def __init__(self, file_path: Path):
-        if not file_path.is_file():
-            raise FileNotFoundError(f"Source file not found at: {file_path}")
-        self._file_path = file_path
-
-    def __len__(self) -> int:
-        return 1
-
-    def __iter__(self) -> Iterator[str]:
-        with open(self._file_path, encoding="utf-8", errors="ignore") as f:
-            yield f.read()
-
-    def total_bytes(self) -> int:
-        return self._file_path.stat().st_size
-
-
-class FolderDataSource(ByteSizableDataSource):
-    """Iterates through a directory and yields the content of each file."""
-
-    def __init__(self, folder_path: Path):
-        if not folder_path.is_dir():
-            raise NotADirectoryError(f"Source path is not a directory: {folder_path}")
-
-        self._file_paths = [p for p in folder_path.rglob("*") if p.is_file() and not p.name.startswith(".")]
-        print(f"✅ Found {len(self._file_paths)} files to process in {folder_path}.")
-
-    def __len__(self) -> int:
-        return len(self._file_paths)
-
-    def __iter__(self) -> Iterator[str]:
-        for file_path in self._file_paths:
-            with open(file_path, encoding="utf-8", errors="ignore") as f:
-                yield from f
-
-    def total_bytes(self) -> int:
-        return sum(p.stat().st_size for p in self._file_paths)
-
-
-class LineByLineFileDataSource(ByteSizableDataSource):
-    """Reads a text file and yields each line as a separate sample."""
-
-    def __init__(self, file_path: Path):
-        if not file_path.is_file():
-            raise FileNotFoundError(f"Source file not found at: {file_path}")
-        self._file_path = file_path
-
-        print("Pre-counting lines for progress bar...")
-        with open(self._file_path, encoding="utf-8", errors="ignore") as f:
-            self._line_count = sum(1 for _ in f)
-
-    def __len__(self) -> int:
-        return self._line_count
-
-    def __iter__(self) -> Iterator[str]:
-        with open(self._file_path, encoding="utf-8", errors="ignore") as f:
-            yield from f
-
-    def total_bytes(self) -> int:
-        return self._file_path.stat().st_size
diff --git a/scratchgpt/data/hf_datasource.py b/scratchgpt/data/hf_datasource.py
new file mode 100644
index 0000000..51e8467
--- /dev/null
+++ b/scratchgpt/data/hf_datasource.py
@@ -0,0 +1,149 @@
+from collections.abc import Iterator
+from pathlib import Path
+
+import torch
+from datasets import Dataset, load_dataset
+from datasets import IterableDataset as HFIterableDataset
+from torch import Tensor
+from torch.utils.data import DataLoader
+from torch.utils.data import IterableDataset as TorchIterableDataset
+
+from scratchgpt.core.types import DictTensorLoader
+from scratchgpt.tokenizer.base_tokenizer import Tokenizer
+from scratchgpt.training.tokenize_utils import prepare_dataset_for_training
+
+
+class _StreamingBlockDataset(TorchIterableDataset[dict[str, Tensor]]):
+    """
+    A PyTorch IterableDataset that wraps a Hugging Face IterableDataset,
+    tokenizes it, and yields blocks of a fixed size.
+    """
+
+    def __init__(
+        self,
+        hf_dataset: HFIterableDataset,
+        tokenizer: Tokenizer,
+        block_size: int,
+        text_column: str,
+    ):
+        self._hf_dataset = hf_dataset
+        self._tokenizer = tokenizer
+        self._block_size = block_size
+        self._text_column = text_column
+
+    def __iter__(self) -> Iterator[dict[str, Tensor]]:
+        buffer: list[int] = []
+        chunk_size = self._block_size + 1
+
+        for example in self._hf_dataset:
+            # Safely get text from the example dict.
+            if text := example.get(self._text_column):
+                buffer.extend(self._tokenizer.encode(text))
+
+                # Yield blocks as they become available in the buffer.
+                while len(buffer) >= chunk_size:
+                    block = buffer[:chunk_size]
+                    buffer = buffer[chunk_size:]
+                    yield {
+                        "input_ids": torch.tensor(block[:-1]),
+                        "labels": torch.tensor(block[1:]),
+                    }
+
+
+class HFDataSource:
+    """
+    A DataSource that loads data from the Hugging Face Hub.
+    Handles both standard and streaming datasets.
+    """
+
+    def __init__(self, path_or_name: str, split: str = "train", streaming: bool = False, text_column: str = "text"):
+        self._text_column = text_column
+        self._streaming = streaming
+
+        local_path = Path(path_or_name)
+
+        # Case 1: The path is a local directory.
+        if local_path.is_dir():
+            print(f"Detected local directory. Loading all text files from: {local_path}")
+            self._dataset = load_dataset(
+                "text",
+                data_dir=str(local_path),
+                split=split,
+                streaming=streaming,
+            )
+        # Case 2: The path is a local file.
+        elif local_path.is_file():
+            print(f"Detected local file: {local_path}")
+            is_common_ext = local_path.suffix in {".txt", ".md"}
+
+            if is_common_ext:
+                print("Loading as plain text file.")
+                self._dataset = load_dataset(
+                    "text",
+                    data_files={split: str(local_path)},
+                    split=split,
+                    streaming=streaming,
+                )
+            else:
+                print("Attempting to infer file type...")
+                self._dataset = load_dataset(
+                    str(local_path),
+                    split=split,
+                    streaming=streaming,
+                )
+        # Case 3: The path is not local, assume it's a Hub dataset ID.
+        else:
+            print(f"Assuming '{path_or_name}' is a Hugging Face Hub dataset.")
+            self._dataset = load_dataset(
+                path_or_name,
+                split=split,
+                streaming=streaming,
+            )
+
+    def get_dataloaders(
+        self,
+        tokenizer: Tokenizer,
+        block_size: int,
+        batch_size: int,
+        splits: tuple[float, float],
+        random_seed: int,
+    ) -> tuple[DictTensorLoader, DictTensorLoader | None]:
+        cpu_count = torch.multiprocessing.cpu_count() or 1
+        num_proc = max(1, cpu_count // 2)
+
+        match self._dataset:
+            case Dataset():
+                prepared_dataset = prepare_dataset_for_training(
+                    self._dataset, tokenizer, block_size, self._text_column, num_proc
+                )
+                split_datasets = prepared_dataset.train_test_split(test_size=splits[1], seed=random_seed)
+                train_loader = DataLoader(
+                    split_datasets["train"],
+                    batch_size=batch_size,
+                    shuffle=True,
+                    pin_memory=True,
+                    num_workers=num_proc,
+                )
+                val_loader = DataLoader(
+                    split_datasets["test"],
+                    batch_size=batch_size,
+                    shuffle=False,
+                    pin_memory=True,
+                    num_workers=num_proc,
+                )
+                return train_loader, val_loader
+
+            case HFIterableDataset():
+                print(
+                    "⚠️  Note: Validation splitting is not supported for streaming datasets. "
+                    "Validation loader will be None."
+                )
+
+                streaming_dataset = _StreamingBlockDataset(self._dataset, tokenizer, block_size, self._text_column)
+                # shuffle=True is not supported for IterableDatasets in DataLoader
+                train_loader = DataLoader(streaming_dataset, batch_size=batch_size)
+
+                return train_loader, None
+
+            case _:
+                raise TypeError(f"Unsupported dataset type: {type(self._dataset)}")
diff --git a/scratchgpt/dataloader.py b/scratchgpt/dataloader.py
deleted file mode 100644
index dd80565..0000000
--- a/scratchgpt/dataloader.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from pathlib import Path
-
-import numpy as np
-import torch
-from torch import Tensor
-from torch.utils.data import Dataset
-
-
-class PretokenizedDataset(Dataset[tuple[Tensor, Tensor]]):
-    def __init__(
-        self,
-        token_file: Path,
-        block_size: int,
-        dtype: np.dtype,
-    ) -> None:
-        super().__init__()
-        self.block_size = block_size
-
-        all_tokens = np.memmap(token_file, dtype=dtype, mode="c")
-        self.data = torch.from_numpy(all_tokens)
-
-    def __len__(self) -> int:
-        return max(0, len(self.data) - self.block_size)
-
-    def __getitem__(self, idx: int) -> tuple[Tensor, Tensor]:
-        block = self.data[idx : idx + self.block_size]
-        target = self.data[idx + 1 : idx + self.block_size + 1]
-
-        return block.long(), target.long()
diff --git a/scratchgpt/model/model.py b/scratchgpt/model/model.py
index 8a8f148..4ee95ae 100644
--- a/scratchgpt/model/model.py
+++ b/scratchgpt/model/model.py
@@ -68,16 +68,15 @@ def forward(self, context: Tensor) -> Tensor:
 
 
 class FeedFoward(nn.Module):
-    def __init__(self, embedding_size: int) -> None:
+    def __init__(self, embedding_size: int, dropout_rate: float) -> None:
         super().__init__()
         self._ffwd_multipler = 4
-        self._dropout = 0.2
 
         self._net = nn.Sequential(
             nn.Linear(embedding_size, embedding_size * self._ffwd_multipler),
             nn.ReLU(),
             nn.Linear(self._ffwd_multipler * embedding_size, embedding_size),
-            nn.Dropout(self._dropout),
+            nn.Dropout(dropout_rate),
         )
 
     def forward(self, tensor: Tensor) -> Tensor:
@@ -102,7 +101,7 @@ def __init__(
             head_size,
             dropout_rate,
         )
-        self._ffwd = FeedFoward(embedding_size)
+        self._ffwd = FeedFoward(embedding_size, dropout_rate)
         self._layer_norm_attention = nn.LayerNorm(embedding_size)
         self._layer_norm_ffwd = nn.LayerNorm(embedding_size)
 
@@ -158,14 +157,24 @@ def forward(self, context: Tensor) -> Tensor:
         logits: Tensor = self._lm_head(x)  # (B, T, vocab_size)
         return logits
 
-    def generate(self, context: Tensor, max_new_tokens: int) -> Tensor:
+    def generate(
+        self,
+        context: Tensor,
+        max_new_tokens: int,
+        stop_token: int | None = None,
+        temperature: float = 1.0,
+    ) -> Tensor:
         for _ in range(max_new_tokens):
             cropped_context = context[:, -self._block_size :]
             logits = self(cropped_context)
-            logits = logits[:, -1, :]  # becomes (B, C)
+            logits = logits[:, -1, :] / temperature  # becomes (B, C)
             probs = F.softmax(logits, dim=-1)
             idx_next = torch.multinomial(probs, num_samples=1)
             context = torch.cat((context, idx_next), dim=1)
+
+            if stop_token is not None and idx_next == stop_token:
+                return context
+
         return context
 
 
diff --git a/scratchgpt/train.py b/scratchgpt/train.py
index 6ee25e1..b9ee229 100644
--- a/scratchgpt/train.py
+++ b/scratchgpt/train.py
@@ -1,25 +1,15 @@
 import argparse
-import sys
 from pathlib import Path
 
 import torch
 from pydantic_yaml import parse_yaml_file_as, to_yaml_file
 from torch.optim import AdamW
 
-try:
-    from scratchgpt.tokenizer.hf_tokenizer import HuggingFaceTokenizer
-except ImportError:
-    print(
-        "HuggingFaceTokenizer not available. Please install the hf-tokenizers extras:\n"
-        "pip install 'scratchgpt[hf-tokenizers]'",
-        file=sys.stderr,
-    )
-    sys.exit(1)
-
 from scratchgpt.config import ScratchGPTConfig
-from scratchgpt.data.datasource import DataSource, FileDataSource, FolderDataSource
+from scratchgpt.data import create_data_source
 from scratchgpt.model.model import TransformerLanguageModel
 from scratchgpt.model_io import load_model, save_tokenizer
+from scratchgpt.tokenizer.hf_tokenizer import HuggingFaceTokenizer
 from scratchgpt.training.trainer import Trainer
 
 
@@ -36,16 +26,34 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument(
         "-d",
         "--data_source",
-        type=Path,
+        type=str,
         required=True,
-        help="The path to the training data source (file or folder).",
+        help="Dataset name from HF Hub or path to local data (file/folder).",
     )
     parser.add_argument(
         "-t",
         "--tokenizer",
         type=str,
         default="gpt2",
-        help="The name of the Hugging Face Hub tokenizer to use (e.g., 'gpt2', 'bert-base-uncased').",
+        help="The name of the Hugging Face Hub tokenizer to use (e.g., 'gpt2').",
+    )
+    parser.add_argument(
+        "-s",
+        "--split",
+        type=str,
+        default="train",
+        help="The dataset split to use for training (e.g., 'train', 'validation').",
+    )
+    parser.add_argument(
+        "--text-column",
+        type=str,
+        default="text",
+        help="Name of the column containing text data.",
+    )
+    parser.add_argument(
+        "--streaming",
+        action="store_true",
+        help="Use streaming mode for large datasets that don't fit in memory.",
     )
     parser.add_argument(
         "-dv",
@@ -58,17 +66,6 @@ def parse_args() -> argparse.Namespace:
     return parser.parse_args()
 
 
-def get_data_source(path: Path) -> DataSource:
-    """Instantiates the correct DataSource based on the path."""
-    if path.is_file():
-        print(f"Using single file data source: {path}")
-        return FileDataSource(path)
-    if path.is_dir():
-        print(f"Using folder data source: {path}")
-        return FolderDataSource(path)
-    raise FileNotFoundError(f"Data source path not found or is not a file/directory: {path}")
-
-
 def main() -> None:
     """Main script to configure and run the training process."""
     args = parse_args()
@@ -86,18 +83,27 @@ def main() -> None:
     torch.manual_seed(config.training.random_seed)
 
     # 2. Get the tokenizer from the Hugging Face Hub
+    print(f"Loading tokenizer: {args.tokenizer}")
     tokenizer = HuggingFaceTokenizer.from_hub(repo_id=args.tokenizer)
     config.architecture.vocab_size = tokenizer.vocab_size
 
-    # 3. Instantiate the data sources
-    data_source = get_data_source(args.data_source)
+    # 3. Create the data source
+    print(f"Loading dataset: {args.data_source} (split: {args.split})")
+    if args.streaming:
+        print("Using streaming mode for data loading")
+
+    data_source = create_data_source(
+        path_or_name=args.data_source,
+        split=args.split,
+        streaming=args.streaming,
+        text_column=args.text_column,
+    )
 
     # 4. Set up the model and optimizer
     device = torch.device(args.device)
     print(f"Using device: {device}")
     model = TransformerLanguageModel(config)
 
-    # Load existing model weights if they exist in the experiment folder
     best_model_path = args.experiment / "best_model_weights.pth"
     model = load_model(best_model_path, model, device)
 
@@ -118,7 +124,7 @@ def main() -> None:
     save_tokenizer(args.experiment, tokenizer)
 
     print("\nStarting training...")
-    trainer.train(data=data_source, tokenizer=tokenizer)
+    trainer.train(data_source=data_source, tokenizer=tokenizer)
     print("\n✅ Training complete.")
 
 
diff --git a/scratchgpt/training/tokenize_utils.py b/scratchgpt/training/tokenize_utils.py
new file mode 100644
index 0000000..9c12c8d
--- /dev/null
+++ b/scratchgpt/training/tokenize_utils.py
@@ -0,0 +1,82 @@
+from collections.abc import Callable
+from typing import Any
+
+from datasets import Dataset
+
+from scratchgpt.tokenizer.base_tokenizer import Tokenizer
+
+TokenizeFunc = Callable[[dict[str, list[Any]]], dict[str, list[list[int]]]]
+
+
+def create_tokenize_function(
+    tokenizer: Tokenizer,
+    block_size: int,
+    text_column: str,
+) -> TokenizeFunc:
+    """
+    Creates a tokenization function for use with `datasets.map`.
+
+    This function tokenizes all texts, concatenates them, and then splits
+    the result into chunks of `block_size`.
+    """
+
+    def tokenize_and_chunk(examples: dict[str, list[Any]]) -> dict[str, list[list[int]]]:
+        """Tokenizes a batch of texts and forms fixed-size blocks."""
+        # Ensure the specified text column exists.
+        if text_column not in examples:
+            raise KeyError(
+                f"Text column '{text_column}' not found in dataset. Available columns: {list(examples.keys())}"
+            )
+
+        # 1. Concatenate all tokens from all documents in the batch.
+        all_tokens: list[int] = []
+        for text in examples[text_column]:
+            all_tokens.extend(tokenizer.encode(text))
+
+        # 2. Create chunks of `block_size + 1` to get both inputs and labels.
+        chunk_size = block_size + 1
+        total_tokens = len(all_tokens)
+
+        # If we can't form at least one full chunk, there's nothing to do.
+        if total_tokens < chunk_size:
+            return {"input_ids": [], "labels": []}
+
+        # 3. Drop the last partial chunk to ensure all sequences are full length.
+        num_to_drop = total_tokens % chunk_size
+        if num_to_drop > 0:
+            all_tokens = all_tokens[:-num_to_drop]
+
+        # 4. Create input_ids and labels from the chunks.
+        input_ids = [all_tokens[i : i + block_size] for i in range(0, len(all_tokens), chunk_size)]
+        labels = [all_tokens[i + 1 : i + chunk_size] for i in range(0, len(all_tokens), chunk_size)]
+
+        return {"input_ids": input_ids, "labels": labels}
+
+    return tokenize_and_chunk
+
+
+def prepare_dataset_for_training(
+    dataset: Dataset,
+    tokenizer: Tokenizer,
+    block_size: int,
+    text_column: str,
+    num_proc: int | None = None,
+) -> Dataset:
+    """
+    Prepares a dataset for training by tokenizing and chunking it.
+    """
+    tokenize_fn = create_tokenize_function(tokenizer=tokenizer, block_size=block_size, text_column=text_column)
+
+    columns_to_remove = list(dataset.features)
+
+    tokenized_dataset = dataset.map(
+        tokenize_fn,
+        batched=True,
+        num_proc=num_proc,
+        remove_columns=columns_to_remove,
+    )
+
+    # Set the dataset format for PyTorch for direct use in DataLoaders.
+    tokenized_dataset.set_format("torch", columns=["input_ids", "labels"])
+
+    return tokenized_dataset
diff --git a/scratchgpt/training/trainer.py b/scratchgpt/training/trainer.py
index 5e3444d..61b0090 100644
--- a/scratchgpt/training/trainer.py
+++ b/scratchgpt/training/trainer.py
@@ -1,34 +1,20 @@
 import sys
 from pathlib import Path
 
-import numpy as np
 import torch
 from torch import Tensor
 from torch.nn import functional as F
 from torch.optim.optimizer import Optimizer
-from torch.utils.data import DataLoader, random_split
+from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
 from scratchgpt.config import ScratchGPTTraining
-from scratchgpt.core.types import TensorTupleLoader
-from scratchgpt.data.datasource import ByteSizableDataSource, DataSource
-from scratchgpt.dataloader import PretokenizedDataset
+from scratchgpt.data.datasource import DataSource
 from scratchgpt.metering import AverageValueMeter
 from scratchgpt.model.model import TransformerLanguageModel
 from scratchgpt.tokenizer.base_tokenizer import Tokenizer
 
 
-def get_dtype_for_vocab_size(vocab_size: int) -> np.dtype:
-    """Determine the smallest possible uint dtype for a given vocabulary size."""
-    if vocab_size < 2**8:
-        return np.dtype(np.uint8)
-    if vocab_size < 2**16:
-        return np.dtype(np.uint16)
-    if vocab_size < 2**32:
-        return np.dtype(np.uint32)
-    return np.dtype(np.uint64)
-
-
 class Trainer:
     """Orchestrates the model training, validation, and checkpointing."""
 
@@ -47,75 +33,7 @@ def __init__(
         self.device = device
         self.experiment_path.mkdir(exist_ok=True, parents=True)
 
-    def _pretokenize(
-        self,
-        data_source: DataSource,
-        tokenizer: Tokenizer,
-        output_path: Path,
-        dtype: np.dtype,
-    ) -> None:
-        """Iterates through a DataSource, tokenizes it, and saves to a binary file."""
-        total_size = None
-        unit = "samples"
-        # Check if we can provide a more detailed byte-level progress bar
-        if isinstance(data_source, ByteSizableDataSource):
-            total_size = data_source.total_bytes()
-            unit = "B"
-
-        with (
-            open(output_path, "wb") as f,
-            tqdm(total=total_size, unit=unit, unit_scale=True, desc="Tokenizing") as pbar,
-        ):
-            for text_sample in data_source:
-                tokens = tokenizer.encode(text_sample)
-                f.write(np.array(tokens, dtype=dtype).tobytes())
-
-                if total_size:
-                    pbar.update(len(text_sample.encode("utf-8", errors="ignore")))
-                else:
-                    pbar.update(1)
-
-    def _get_dataloader(
-        self, data_source: DataSource, tokenizer: Tokenizer, cache_file: Path
-    ) -> tuple[TensorTupleLoader, TensorTupleLoader]:
-        """Handles DataLoader creation, using a pre-tokenized cache if it exists."""
-        dtype = get_dtype_for_vocab_size(tokenizer.vocab_size)
-
-        if not cache_file.exists():
-            print(f"⏳ Cache file not found. Pre-tokenizing data to '{cache_file}'...")
-            self._pretokenize(data_source, tokenizer, cache_file, dtype)
-
-        print(f"✅ Loading pre-tokenized data from '{cache_file}'")
-        dataset = PretokenizedDataset(
-            token_file=cache_file,
-            block_size=self.model._block_size,
-            dtype=dtype,
-        )
-
-        train_dataset, val_dataset = random_split(dataset, self.config.splits)
-        cpu_count = torch.multiprocessing.cpu_count()
-        num_workers = max(1, int(cpu_count / 2))
-        train_loader = DataLoader(
-            train_dataset,
-            batch_size=self.config.batch_size,
-            shuffle=True,
-            pin_memory=True,
-            num_workers=num_workers,
-            drop_last=False,
-        )
-
-        val_loader = DataLoader(
-            val_dataset,
-            batch_size=self.config.batch_size,
-            shuffle=False,
-            pin_memory=True,
-            num_workers=num_workers,
-            drop_last=False,
-        )
-
-        return train_loader, val_loader
-
-    def _run_epoch(self, dataloader: DataLoader[tuple[Tensor, Tensor]], stage: str) -> float:
+    def _run_epoch(self, dataloader: DataLoader[dict[str, Tensor]], stage: str) -> float:
         """Runs a single epoch of training or validation."""
         is_train = stage == "train"
         self.model.train(is_train)
@@ -123,15 +41,17 @@ def _run_epoch(self, dataloader: DataLoader[tuple[Tensor, Tensor]], stage: str)
 
         pbar = tqdm(dataloader, desc=stage.capitalize(), file=sys.stdout)
         with torch.set_grad_enabled(is_train):
-            for batch, targets in pbar:
-                batch, targets = batch.to(self.device), targets.to(self.device)
+            for batch in pbar:
+                # IMPORTANT: implicit strong relationship to tokenize_utils.py
+                input_ids = batch["input_ids"].to(self.device)
+                labels = batch["labels"].to(self.device)
 
                 if is_train:
                     self.optimizer.zero_grad(set_to_none=True)
 
-                logits = self.model(batch)
+                logits = self.model(input_ids)
                 B, T, C = logits.shape
-                loss: Tensor = F.cross_entropy(logits.view(B * T, C), targets.view(B * T))
+                loss: Tensor = F.cross_entropy(logits.view(B * T, C), labels.view(B * T))
 
                 if is_train:
                     loss.backward()  # type: ignore[no-untyped-call]
@@ -148,18 +68,22 @@ def _run_epoch(self, dataloader: DataLoader[tuple[Tensor, Tensor]], stage: str)
 
     def train(
         self,
-        data: DataSource,
+        data_source: DataSource,
         tokenizer: Tokenizer,
     ) -> None:
         """
         Trains the model.
 
-        This method orchestrates the entire training pipeline, including optional
-        data pre-tokenization, executing training and validation epochs, and
-        saving model checkpoints.
+        This method orchestrates the entire training pipeline, using HF datasets
+        for efficient tokenization caching and data loading.
         """
-        train_cache = self.experiment_path / "train_data.bin"
-        train_loader, val_loader = self._get_dataloader(data, tokenizer, train_cache)
+        train_loader, val_loader = data_source.get_dataloaders(
+            tokenizer=tokenizer,
+            block_size=self.model._block_size,
+            batch_size=self.config.batch_size,
+            splits=self.config.splits,
+            random_seed=self.config.random_seed,
+        )
 
         best_val_loss = float("inf")
         latest_model_path = self.experiment_path / "latest_model_weights.pth"
@@ -170,8 +94,12 @@ def train(
             self._run_epoch(train_loader, "train")
             torch.save(self.model.state_dict(), latest_model_path)
 
-            val_loss = self._run_epoch(val_loader, "validation")
-            if val_loss < best_val_loss:
-                best_val_loss = val_loss
-                print(f"🎉 New best validation loss: {best_val_loss:.4f}. Saving model...")
+            if val_loader:
+                val_loss = self._run_epoch(val_loader, "validation")
+                if val_loss < best_val_loss:
+                    best_val_loss = val_loss
+                    print(f"🎉 New best validation loss: {best_val_loss:.4f}. Saving model...")
+                    torch.save(self.model.state_dict(), best_model_path)
+            else:
+                print("🎉 Saving latest model as best because we have no validation dataset")
                 torch.save(self.model.state_dict(), best_model_path)
diff --git a/tests/data/test_datasource.py b/tests/data/test_datasource.py
new file mode 100644
index 0000000..23400b3
--- /dev/null
+++ b/tests/data/test_datasource.py
@@ -0,0 +1,102 @@
+from pathlib import Path
+
+import pytest
+import torch
+from torch.utils.data import DataLoader
+
+from scratchgpt.data.hf_datasource import HFDataSource
+from scratchgpt.tokenizer.char_tokenizer import CharTokenizer
+
+
+@pytest.fixture
+def simple_tokenizer() -> CharTokenizer:
+    """A simple tokenizer where token ID matches the digit."""
+    return CharTokenizer(text="0123456789abcdef")
+
+
+@pytest.fixture
+def dummy_text_file(tmp_path: Path) -> Path:
+    """Creates a dummy text file with 32 chars."""
+    data_path = tmp_path / "data.txt"
+    data_path.write_text("0123456789abcdef0123456789abcdef")
+    return data_path
+
+
+@pytest.fixture
+def dummy_text_dir(tmp_path: Path) -> Path:
+    """Creates a directory with two text files for a total of 32 chars."""
+    data_dir = tmp_path / "data_dir"
+    data_dir.mkdir()
+    (data_dir / "a.txt").write_text("0123456789abcdef")
+    (data_dir / "b.txt").write_text("0123456789abcdef")
+    return data_dir
+
+
+def test_hf_datasource_from_file(dummy_text_file, simple_tokenizer):
+    """Tests loading a standard dataset from a single .txt file."""
+    block_size = 7
+    batch_size = 2
+
+    data_source = HFDataSource(path_or_name=str(dummy_text_file))
+
+    train_loader, val_loader = data_source.get_dataloaders(
+        tokenizer=simple_tokenizer, block_size=block_size, batch_size=batch_size, splits=(0.5, 0.5), random_seed=42
+    )
+
+    assert isinstance(train_loader, DataLoader)
+    assert isinstance(val_loader, DataLoader)
+
+    # 32 chars // (7+1) chunk size = 4 total samples. Split 50/50 -> 2 train, 2 val.
+    # With batch_size=2, there should be 1 batch in each loader.
+    assert len(train_loader) == 1
+    assert len(val_loader) == 1
+
+    train_batch = next(iter(train_loader))
+    assert train_batch["input_ids"].shape == (batch_size, block_size)
+    assert torch.equal(train_batch["input_ids"][0, 1:], train_batch["labels"][0, :-1])
+
+
+@pytest.mark.skip
+def test_hf_datasource_from_directory(dummy_text_dir, simple_tokenizer):
+    """Tests loading a standard dataset from a directory of text files."""
+    block_size = 7
+    batch_size = 4  # Use all data in one batch
+
+    data_source = HFDataSource(path_or_name=str(dummy_text_dir))
+
+    train_loader, _ = data_source.get_dataloaders(
+        tokenizer=simple_tokenizer,
+        block_size=block_size,
+        batch_size=batch_size,
+        splits=(1.0, 0.0),  # Use all data for training
+        random_seed=42,
+    )
+
+    # 32 chars // (7+1) chunk size = 4 total samples.
+    assert len(train_loader) == 1
+    batch = next(iter(train_loader))
+    assert batch["input_ids"].shape == (batch_size, block_size)
+
+
+def test_hf_datasource_streaming_from_file(dummy_text_file, simple_tokenizer):
+    """Tests loading a streaming dataset from a single .txt file."""
+    block_size = 10
+    batch_size = 2
+
+    data_source = HFDataSource(path_or_name=str(dummy_text_file), streaming=True)
+
+    train_loader, val_loader = data_source.get_dataloaders(
+        tokenizer=simple_tokenizer,
+        block_size=block_size,
+        batch_size=batch_size,
+        splits=(0.8, 0.2),  # Splits are ignored for streaming
+        random_seed=42,
+    )
+
+    assert isinstance(train_loader, DataLoader)
+    assert val_loader is None
+
+    # 32 chars // (10+1) chunk size = 2 total samples. batch_size=2 -> 1 batch.
+    train_batch = next(iter(train_loader))
+    assert train_batch["input_ids"].shape == (batch_size, block_size)
+    assert torch.equal(train_batch["input_ids"][0, 1:], train_batch["labels"][0, :-1])
diff --git a/tests/training/test_tokenize_utils.py b/tests/training/test_tokenize_utils.py
new file mode 100644
index 0000000..4fbfeb9
--- /dev/null
+++ b/tests/training/test_tokenize_utils.py
@@ -0,0 +1,74 @@
+import pytest
+from datasets import Dataset
+
+from scratchgpt.tokenizer.char_tokenizer import CharTokenizer
+from scratchgpt.training.tokenize_utils import (
+    create_tokenize_function,
+    prepare_dataset_for_training,
+)
+
+
+@pytest.fixture
+def simple_tokenizer() -> CharTokenizer:
+    """A simple character tokenizer. Includes a space, so 'a' is token 1."""
+    return CharTokenizer(text="abcdefghijklmnopqrstuvwxyz ")
+
+
+def test_tokenize_and_chunk_logic(simple_tokenizer):
+    """Tests the core logic of the tokenize_and_chunk inner function."""
+    block_size = 8
+    text_column = "text"
+    # This text has 26 chars. With block_size=8, chunk_size=9. 26 // 9 = 2.
+    # We should get 2 full blocks.
+    sample_data = {"text": ["abcdefghijklmnopqrstuvwxyz"]}
+
+    tokenize_fn = create_tokenize_function(simple_tokenizer, block_size, text_column)
+    processed = tokenize_fn(sample_data)
+
+    assert "input_ids" in processed
+    assert "labels" in processed
+
+    assert len(processed["input_ids"]) == 2
+    assert len(processed["labels"]) == 2
+
+    # Check block content. With a space in the vocab, 'a' is 1, 'b' is 2, etc.
+    assert processed["input_ids"][0] == list(range(1, 9))  # a-h
+    assert processed["labels"][0] == list(range(2, 10))  # b-i
+
+    # Second block starts after the first 9-token chunk (a-i).
+    # It begins with 'j', which is token 10.
+    assert processed["input_ids"][1] == list(range(10, 18))  # j-q
+    assert processed["labels"][1] == list(range(11, 19))  # k-r
+
+
+def test_tokenize_and_chunk_edge_cases(simple_tokenizer):
+    """Tests scenarios with insufficient text."""
+    block_size = 10
+    text_column = "text"
+    tokenize_fn = create_tokenize_function(simple_tokenizer, block_size, text_column)
+
+    # Case 1: Text is too short to form any blocks
+    short_text = {"text": ["abcdefghij"]}  # 10 chars, needs 11 for one block
+    processed_short = tokenize_fn(short_text)
+    assert len(processed_short["input_ids"]) == 0
+    assert len(processed_short["labels"]) == 0
+
+    # Case 2: Text is just long enough for one block
+    exact_text = {"text": ["abcdefghijk"]}  # 11 chars
+    processed_exact = tokenize_fn(exact_text)
+    assert len(processed_exact["input_ids"]) == 1
+    assert processed_exact["input_ids"][0] == list(range(1, 11))  # a-j
+
+
+def test_prepare_dataset_for_training(simple_tokenizer):
+    """Ensures the full preparation pipeline works as expected."""
+    block_size = 5
+    text_column = "content"
+    # Concatenated text is 18 chars long. 18 // (5+1) = 3 blocks
+    dataset = Dataset.from_dict({"content": ["abcdefghijkl", "mnopqr"]})
+
+    processed_dataset = prepare_dataset_for_training(dataset, simple_tokenizer, block_size, text_column, num_proc=1)
+
+    assert processed_dataset.column_names == ["input_ids", "labels"]
+    assert processed_dataset.format["type"] == "torch"
+    assert len(processed_dataset) == 3
diff --git a/uv.lock b/uv.lock
index 5fb8b46..64a574c 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,6 +2,79 @@ version = 1
 revision = 3
 requires-python = ">=3.12"
 
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.6.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
+]
+
+[[package]]
+name = "aiohttp"
+version = "3.12.15"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aiohappyeyeballs" },
+    { name = "aiosignal" },
+    { name = "attrs" },
+    { name = "frozenlist" },
+    { name = "multidict" },
+    { name = "propcache" },
+    { name = "yarl" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9b/e7/d92a237d8802ca88483906c388f7c201bbe96cd80a165ffd0ac2f6a8d59f/aiohttp-3.12.15.tar.gz", hash = "sha256:4fc61385e9c98d72fcdf47e6dd81833f47b2f77c114c29cd64a361be57a763a2", size = 7823716, upload-time = "2025-07-29T05:52:32.215Z" }
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