diff --git a/oxen/Cargo.lock b/oxen/Cargo.lock
index e51864b..0ab5db4 100644
--- a/oxen/Cargo.lock
+++ b/oxen/Cargo.lock
@@ -3065,9 +3065,9 @@ checksum = "4ec2a862134d2a7d32d7983ddcdd1c4923530833c9f2ea1a44fc5fa473989058"
 
 [[package]]
 name = "liboxen"
-version = "0.24.1"
+version = "0.24.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ff3a79a9597b91659b8f54b40449abb45394d997bfaf9b8bb8c8966e81295ad8"
+checksum = "6549e194ea7e6360b9264e47d1375491c54e090a9914d8b8a805990c52472537"
 dependencies = [
  "actix-files",
  "actix-web",
diff --git a/oxen/Cargo.toml b/oxen/Cargo.toml
index 56bcfce..92579f6 100644
--- a/oxen/Cargo.toml
+++ b/oxen/Cargo.toml
@@ -28,7 +28,7 @@ polars = { version = "0.44.0", features = [
 ] }
 serde_json = "1.0.106"
 uuid = { version = "1.4.1", features = ["serde", "v4"] }
-liboxen = "0.24.1"
+liboxen = "0.24.4"
 # liboxen = { path = "../../Oxen/src/lib" }
 
 [build-dependencies]
diff --git a/oxen/pyproject.toml b/oxen/pyproject.toml
index d788baa..e344ca0 100644
--- a/oxen/pyproject.toml
+++ b/oxen/pyproject.toml
@@ -12,16 +12,12 @@ classifiers = [
     "Programming Language :: Python :: Implementation :: PyPy",
 ]
 dependencies = [
-    "numpy",
-    "opencv-python-headless",
     "pandas",
     "polars",
     "pyarrow",
     "requests",
     "toml",
-    "tqdm",
-    "torch",
-    "tensorflow",
+    "tqdm"
 ]
 
 
diff --git a/oxen/python/oxen/__init__.py b/oxen/python/oxen/__init__.py
index 7903c7d..7b3c33c 100644
--- a/oxen/python/oxen/__init__.py
+++ b/oxen/python/oxen/__init__.py
@@ -17,11 +17,8 @@
 from oxen.remote_repo import RemoteRepo
 from oxen.workspace import Workspace
 from oxen.data_frame import DataFrame
-from oxen.dag import DAG
-from oxen.op import Op
 from oxen import auth
 from oxen import datasets
-from oxen import loaders
 from oxen.clone import clone
 from oxen.diff.diff import diff
 from oxen.init import init
@@ -30,7 +27,6 @@
 # Names of public modules we want to expose
 __all__ = [
     "Dataset",
-    "DAG",
     "PyCommit",
     "PyDataset",
     "PyWorkspace",
@@ -38,7 +34,6 @@
     "PyRemoteRepo",
     "PyRepo",
     "PyStagedData",
-    "Op",
     "clone",
     "init",
     "is_configured",
@@ -48,7 +43,6 @@
     "Repo",
     "auth",
     "datasets",
-    "loaders",
     "util",
     "diff",
 ]
diff --git a/oxen/python/oxen/dag.py b/oxen/python/oxen/dag.py
deleted file mode 100644
index 54be36d..0000000
--- a/oxen/python/oxen/dag.py
+++ /dev/null
@@ -1,26 +0,0 @@
-class DAG:
-    def __init__(self, outputs=None):
-        self.outputs = outputs or []
-
-    def evaluate(self):
-        visited = set()
-        outputs = []
-
-        def _dfs(node):
-            if node not in visited:
-                visited.add(node)
-                for parent in node.parents:
-                    _dfs(parent)
-                outputs.append(node)
-
-        for output_node in self.outputs:
-            _dfs(output_node)
-
-        desired_output_ids = [node.id for node in self.outputs]
-        results = [None] * len(self.outputs)
-        for node in outputs:
-            result = node.run()
-            if node.id in desired_output_ids:
-                results[desired_output_ids.index(node.id)] = result
-
-        return results
diff --git a/oxen/python/oxen/loaders/__init__.py b/oxen/python/oxen/loaders/__init__.py
deleted file mode 100644
index 2641a7f..0000000
--- a/oxen/python/oxen/loaders/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-# Loaders
-from .chat import ChatLoader
-from .regression import RegressionLoader
-from .image_classification import ImageClassificationLoader
-
-# Names of public modules we want to expose
-__all__ = ["ChatLoader", "RegressionLoader", "ImageClassificationLoader"]
diff --git a/oxen/python/oxen/loaders/chat.py b/oxen/python/oxen/loaders/chat.py
deleted file mode 100644
index d2f0b11..0000000
--- a/oxen/python/oxen/loaders/chat.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from oxen.dag import DAG
-
-from oxen.ops import (
-    Identity,
-    ReadText,
-    ReadDF,
-    ExtractCol,
-    StrColTemplate,
-    ConcatSeries,
-)
-
-
-class ChatLoader:
-    """
-    Formats / templatizes data from an Oxen repository for use in chatbot training.
-    """
-
-    def __init__(self, prompt_file, data_file):
-        """
-        Creates a new ChatLoader.
-
-        Parameters
-        ----------
-        prompt_file : str
-            Path to a text file containing a prompt template for the chatbot.
-        data_file: str
-            Path to a tabular file containing the chatbot training data,
-            with "prompt" and "response" columns
-        """
-        # Define input nodes
-        prompt_name = Identity(input="prompt")
-        column_name = Identity(input="response")
-        prompt = ReadText(input=prompt_file)
-        data_frame = ReadDF(input=data_file)
-
-        # Define intermediate nodes
-        extract_prompt = ExtractCol(name="extract_prompt")(data_frame, prompt_name)
-        extract_response = ExtractCol(name="extract_response")(data_frame, column_name)
-        templatize = StrColTemplate(name="templatize")(prompt, extract_prompt)
-        output = ConcatSeries(name="concat_output")(templatize, extract_response)
-
-        # Create and compile the graph
-        self.graph = DAG(outputs=[output])
-
-    def run(self):
-        """
-        Returns
-        --------
-        outputs[0] : pl.DataFrame
-            DataFrame with columns containing the templatized prompt ("prompt")
-            and response ("response")
-        """
-        # Run the graph to get the outputs
-        result = self.graph.evaluate()
-
-        print("\n\nResult:")
-        print(result)
-        return result
diff --git a/oxen/python/oxen/loaders/image_classification.py b/oxen/python/oxen/loaders/image_classification.py
deleted file mode 100644
index a84f139..0000000
--- a/oxen/python/oxen/loaders/image_classification.py
+++ /dev/null
@@ -1,98 +0,0 @@
-from oxen.dag import DAG
-
-from oxen.ops import (
-    Identity,
-    ReadDF,
-    ExtractCol,
-    CreateLabelMap,
-    EncodeLabels,
-    ReadImageDir,
-    ReadText,
-    ResizeImages,
-)
-
-
-class ImageClassificationLoader:
-    """
-    Prepares data from an Oxen repository for use
-    in supervised image classification tasks.
-    """
-
-    def __init__(
-        self,
-        imagery_root_dir,
-        label_file,
-        df_file,
-        path_name="path",
-        label_name="label",
-        resize_to=None,
-        resize_method="crop",
-    ):
-        """
-        Creates a new ImageClassificationLoader.
-
-        Parameters
-        ----------
-        imagery_root_dir : str
-            Directory relative to which the image paths
-            in the DataFrame file are specified.
-        label_file: str
-            Path to a text file containing a line-separated
-            list of canonical labels for the dataset.
-        df_file : str
-            Path to a tabular file containing the image paths
-            and associate labels (and any additional metadata).
-        path_name : str
-            Column name in df_file containing the image paths
-        label_name : str
-            Column name in df_file containing the image labels
-        resize_to : int | None
-            Size to which images should be resized (square, in pixels)
-        resize_method : str
-            Method to use for resizing images. One of "crop", "pad", or "squash".
-                crop : resize (preserving aspect) such
-                       that smaller size = target size, then center crop
-                pad: resize (prserving aspect) such that larger size = target size,
-                     then pad with zeros equally on all sides
-                squash: resize (not presercing aspect)
-        """
-        # Define input nodes
-        data_frame = ReadDF(input=df_file)
-        label_list = ReadText(input=label_file)
-        path_name = Identity(input=path_name)
-        label_name = Identity(input=label_name)
-        imagery_root_dir = Identity(input=imagery_root_dir)
-        resize_to = Identity(input=resize_to)
-        resize_method = Identity(input=resize_method)
-
-        # Define intermediate nodes
-        paths = ExtractCol()(data_frame, path_name)
-        label_text = ExtractCol()(data_frame, label_name)
-        image_list = ReadImageDir()(imagery_root_dir, paths)
-
-        # Define output nodes
-        images = ResizeImages()(image_list, resize_to, resize_method)
-
-        label_map = CreateLabelMap()(label_list, label_text)
-        labels = EncodeLabels()(label_text, label_map)
-
-        # Create and compile the graph
-        self.graph = DAG(outputs=[images, labels, label_map])
-
-    def run(self):
-        """
-        Returns
-        -------
-        outputs[0] (images) : np.ndarray
-            All images found in the dataset, as a numpy array of shape (n, h, w, c)
-        outputs[1] (labels) : np.nadarray
-            Encoded labels for training, index-matched to the images array
-        outputs[2] (mapper) : dict
-            A dictionary mapping the encoded labels to their canonical names
-        """
-        # Run the graph to get the outputs
-        result = self.graph.evaluate()
-
-        print("\n\nResult:")
-        print(result)
-        return result
diff --git a/oxen/python/oxen/loaders/regression.py b/oxen/python/oxen/loaders/regression.py
deleted file mode 100644
index 1ef420d..0000000
--- a/oxen/python/oxen/loaders/regression.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from oxen.dag import DAG
-
-from oxen.ops import Identity, ReadDF, ExtractCol, ConcatSeries
-
-
-class RegressionLoader:
-    def __init__(self, data_file, pred_name, f_names):
-        """
-        Extracts and formats relevant features and labels
-        from a tabular dataset for use in regression tasks.
-
-        Parameters
-        ----------
-        data_file : str
-            Path to a tabular file containing the input features
-            and prediction target for a regression task
-        pred_nam : str
-            Column name in data_file containing the prediction target
-        f_names : list
-            List of column names in data_file containing the input features
-        """
-        # Define input nodes
-        pred_name = Identity(input=pred_name)
-        data_frame = ReadDF(input=data_file)
-
-        # Define intermediate nodes
-        prediction = ExtractCol()(data_frame, pred_name)
-        extracts = [ExtractCol()(data_frame, Identity(input=col)) for col in f_names]
-        features = ConcatSeries(name="concat")(*extracts)
-
-        # Create and compile the graph
-        self.graph = DAG(outputs=[features, prediction])
-
-    def run(self):
-        # Run the graph to get the outputs
-        """
-        Returns
-        ---------
-        outputs[0] (features) : pl.DataFrame
-            DataFrame containing only the specified input features
-        outputs[1] (prediction) : pl.Series
-            Series containing the prediction target
-        """
-        result = self.graph.evaluate()
-
-        print("\n\nResult:")
-        print(result)
-        return result
diff --git a/oxen/python/oxen/op.py b/oxen/python/oxen/op.py
deleted file mode 100644
index 1c27e54..0000000
--- a/oxen/python/oxen/op.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import uuid
-
-
-class Op:
-    def __init__(self, *args, **kwargs):
-        self.args = args
-        self.kwargs = kwargs
-        self.id = uuid.uuid4()
-        self.input = None
-        self.name = self.__class__.__name__
-
-        if "name" in kwargs:
-            self.name = kwargs["name"]
-
-        if "input" in kwargs:
-            self.input = kwargs["input"]
-
-        # print(f"Creating op {self.name}(args={self.args}, kwargs={self.kwargs})")
-
-        self.parents = []
-
-    def __repr__(self):
-        return f"{self.name}({self.args})"
-
-    # Links to the parent Operations that need to run first
-    def __call__(self, *args):
-        for arg in args:
-            # print(f"  {self} --parent--> {arg}")
-            self.parents.append(arg)
-        return self
-
-    # For the child op to implement
-    def call(self, _args):
-        raise NotImplementedError()
-
-    # Combines the data, the args, and the parent inputs, and computes the output
-    def run(self):
-        # print("=" * 5)
-        # print(f"Running {self}")
-        # print(f"parents {self.parents}")
-
-        # these will be the inputs to the subsequent node call
-        inputs = []
-        if self.input:
-            inputs.append(self.input)
-        if self.args:
-            inputs.append(self.args)
-        if self.parents:
-            inputs.extend([node.input for node in self.parents])
-
-        # transform the inputs into the output
-        self.input = self.call(inputs)
-        # print(f"output {self.input}")
-        # print("=" * 5)
-        return self.input
diff --git a/oxen/python/oxen/ops/__init__.py b/oxen/python/oxen/ops/__init__.py
deleted file mode 100644
index 59279c0..0000000
--- a/oxen/python/oxen/ops/__init__.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# Operations
-from .concat_series import ConcatSeries
-from .extract_col import ExtractCol
-from .identity import Identity
-from .read_df import ReadDF
-from .read_text import ReadText
-from .str_col_template import StrColTemplate
-from .read_image_dir import ReadImageDir
-from .encode_labels import EncodeLabels
-from .create_label_map import CreateLabelMap
-from .resize_images import ResizeImages
-
-# Names of public modules we want to expose
-__all__ = [
-    "ConcatSeries",
-    "ExtractCol",
-    "Identity",
-    "ReadDF",
-    "ReadText",
-    "StrColTemplate",
-    "ReadImageDir",
-    "EncodeLabels",
-    "CreateLabelMap",
-    "ResizeImages",
-]
diff --git a/oxen/python/oxen/ops/concat_series.py b/oxen/python/oxen/ops/concat_series.py
deleted file mode 100644
index 2dc271e..0000000
--- a/oxen/python/oxen/ops/concat_series.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import oxen
-import polars as pl
-
-
-class ConcatSeries(oxen.Op):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def call(self, args):
-        return pl.DataFrame(args)
diff --git a/oxen/python/oxen/ops/create_label_map.py b/oxen/python/oxen/ops/create_label_map.py
deleted file mode 100644
index 0ef5266..0000000
--- a/oxen/python/oxen/ops/create_label_map.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import oxen
-
-
-class CreateLabelMap(oxen.Op):
-    """
-    Creates a dictionary mapping string labels to integers,
-    based on a canonical line-separated text file of labels.
-
-    Args:
-        args[0]: str
-            String of line-separated labels
-        args[1]: List[str]
-            Iterable of labels in data, as a check against unexpected data values
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def call(self, args):
-        lines = args[0].split("\n")
-        canonical_labels = set(lines)
-        data_labels = set(args[1])
-        missing_labels = data_labels.difference(canonical_labels)
-        if len(missing_labels) != 0:
-            raise ValueError(
-                f"Some label(s) in data missing from labels file: {missing_labels}"
-            )
-        return {line: i for i, line in enumerate(lines)}
diff --git a/oxen/python/oxen/ops/encode_labels.py b/oxen/python/oxen/ops/encode_labels.py
deleted file mode 100644
index 5f86194..0000000
--- a/oxen/python/oxen/ops/encode_labels.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import oxen
-import numpy as np
-
-
-class EncodeLabels(oxen.Op):
-    """
-    Maps a column of labels to a column of integers according to a dictionary.
-
-    Args:
-        args[0] : polars.Series
-            Polars series of labels to be mapped
-        args[1] : Dict[str, int]
-            Dictionary mapping labels to integers
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def call(self, args):
-        return np.array(args[0].replace(args[1]))
diff --git a/oxen/python/oxen/ops/extract_col.py b/oxen/python/oxen/ops/extract_col.py
deleted file mode 100644
index 9ec2b25..0000000
--- a/oxen/python/oxen/ops/extract_col.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import oxen
-
-
-class ExtractCol(oxen.Op):
-    def __init__(self, *args, **kwargs):
-        """
-        Extracts a column from a DataFrame.
-
-        Args:
-            args[0]: polars.DataFrame
-                DataFrame to extract column from
-            args[1]: str
-                Name of column to extract
-        """
-        super().__init__(*args, **kwargs)
-
-    def call(self, args):
-        df, column = args
-        return df[column]
diff --git a/oxen/python/oxen/ops/identity.py b/oxen/python/oxen/ops/identity.py
deleted file mode 100644
index 00b3acb..0000000
--- a/oxen/python/oxen/ops/identity.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import oxen
-
-
-class Identity(oxen.Op):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def call(self, args):
-        # print(f"identity_fn {args}")
-        return self.input
diff --git a/oxen/python/oxen/ops/read_df.py b/oxen/python/oxen/ops/read_df.py
deleted file mode 100644
index 25d6f3c..0000000
--- a/oxen/python/oxen/ops/read_df.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import oxen
-
-
-class ReadDF(oxen.Op):
-    """
-    Reads a polars DataFrame from a file.
-
-    Args:
-        args[0] : str
-            File path to read DataFrame from
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def call(self, args):
-        # print(f"read_df {args}")
-        return oxen.util.read_df(args[0])
diff --git a/oxen/python/oxen/ops/read_image_dir.py b/oxen/python/oxen/ops/read_image_dir.py
deleted file mode 100644
index 2bd6fe9..0000000
--- a/oxen/python/oxen/ops/read_image_dir.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import oxen
-from tqdm import tqdm
-import cv2
-
-
-class ReadImageDir(oxen.Op):
-    """
-    Reads in imagery as specified by a DataFrame column of paths.
-
-    Args:
-        args[0] : str
-            Root imagery directory, is prefixed to DataFrame paths
-        args[1] : List[str]
-            Column of paths to imagery (relative to root directory specified in args[0])
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def call(self, args):
-        image_data = []
-        prefix = args[0]
-        print("Reading images...")
-        for path in tqdm(args[1]):
-            img = cv2.imread(f"{prefix}/{path}", cv2.IMREAD_UNCHANGED)
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-            image_data.append(img)
-        return image_data
diff --git a/oxen/python/oxen/ops/read_text.py b/oxen/python/oxen/ops/read_text.py
deleted file mode 100644
index 3481469..0000000
--- a/oxen/python/oxen/ops/read_text.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import oxen
-from pathlib import Path
-
-
-class ReadText(oxen.Op):
-    """
-    Reads a text file
-
-    Args:
-        args[0] : str
-            File path to read text from
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def call(self, args):
-        file = args[0]
-        return Path(file).read_text()
diff --git a/oxen/python/oxen/ops/resize_images.py b/oxen/python/oxen/ops/resize_images.py
deleted file mode 100644
index 73d455f..0000000
--- a/oxen/python/oxen/ops/resize_images.py
+++ /dev/null
@@ -1,114 +0,0 @@
-import oxen
-import numpy as np
-import cv2
-from tqdm import tqdm
-
-
-class ResizeImages(oxen.Op):
-    """
-    Resizes a list of images to a common size for use in computer vision tasks.
-
-    Args:
-        args[0]: List[np.ndarray]
-            List of images to resize (height, width, channels)
-        args[1]: int | None
-            Height and width dimension for cropping square images
-        args[2]: str
-            Method for resizing images to square size. Options are
-            "crop", "pad", and "squeeze".
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def crop(self, image, size):
-        # Resize such that shortest side is target size
-        if image.shape[0] < image.shape[1]:
-            resized = self._resize_same_aspect(image, height=size)
-        else:
-            resized = self._resize_same_aspect(image, width=size)
-        result = self._center_crop(resized, size, size)
-        return result.astype(image.dtype)
-
-    def pad(self, image, size, inter=cv2.INTER_LINEAR):
-        # Resize largest dimension to target size
-        if image.shape[0] < image.shape[1]:
-            resized = self._resize_same_aspect(image, width=size, inter=inter)
-        else:
-            resized = self._resize_same_aspect(image, height=size, inter=inter)
-
-        color = (0, 0, 0)
-        result = np.full((size, size, image.shape[2]), color)
-
-        old_height, old_width = resized.shape[:2]
-        x_center = (size - old_width) // 2
-        y_center = (size - old_height) // 2
-
-        # copy img image into center of result image
-        result[y_center : y_center + old_height, x_center : x_center + old_width] = (
-            resized
-        )
-
-        return result.astype(image.dtype)
-
-    def squeeze(self, image, size, inter=cv2.INTER_LINEAR):
-        result = cv2.resize(image, (size, size), interpolation=inter)
-        return result.astype(image.dtype)
-
-    def _resize_same_aspect(
-        self, image, height=None, width=None, inter=cv2.INTER_LINEAR
-    ):
-        dim = None
-        (h, w) = image.shape[:2]
-
-        if width is None and height is None:
-            return image
-
-        if width is None:
-            r = height / float(h)
-            dim = (int(w * r), height)
-        else:
-            r = width / float(w)
-            dim = (width, int(h * r))
-
-        result = cv2.resize(image, dim, interpolation=inter)
-        return result
-
-    def _center_crop(self, image, out_height, out_width):
-        height, width = image.shape[:2]
-        startx = width // 2 - out_width // 2
-        starty = height // 2 - out_height // 2
-
-        if len(image.shape) > 2:
-            return image[starty : starty + out_height, startx : startx + out_width, :]
-        else:
-            return image[starty : starty + out_height, startx : startx + out_width]
-
-    def call(self, args):
-        if args[1] is None:
-            return np.array(args[0])
-
-        n_channels = args[0][0].shape[2]
-        out_dtype = args[0][0].dtype
-        result = np.zeros(
-            (
-                len(args[0]),
-                args[1],
-                args[1],
-                n_channels,
-            ),
-            dtype=out_dtype,
-        )
-
-        print("Resizing images...")
-        for i in tqdm(range(len(args[0]))):
-            if args[2] == "crop":
-                modified = self.crop(args[0][i], args[1])
-            elif args[2] == "pad":
-                modified = self.pad(args[0][i], args[1])
-            elif args[2] == "squeeze":
-                modified = self.squeeze(args[0][i], args[1])
-            else:
-                raise ValueError(f"Invalid argument {args[2]} for resize_method")
-            result[i] = modified
-        return result
diff --git a/oxen/python/oxen/ops/str_col_template.py b/oxen/python/oxen/ops/str_col_template.py
deleted file mode 100644
index 2cd7608..0000000
--- a/oxen/python/oxen/ops/str_col_template.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import oxen
-
-
-class StrColTemplate(oxen.Op):
-    def __init__(self, *args, **kwargs):
-        self.search = kwargs["search"] if "search" in kwargs else "{prompt}"
-        super().__init__(*args, **kwargs)
-
-    def call(self, args):
-        value, column = args
-        result = column.map_elements(lambda x: value.replace(self.search, x))
-        return result
diff --git a/oxen/python/oxen/repo.py b/oxen/python/oxen/repo.py
index 344f8b9..a6e8d45 100644
--- a/oxen/python/oxen/repo.py
+++ b/oxen/python/oxen/repo.py
@@ -213,3 +213,9 @@ def current_branch(self):
         Returns the current branch.
         """
         return self._repo.current_branch()
+
+    def merge(self, branch: str):
+        """
+        Merge a branch into the current branch.
+        """
+        return self._repo.merge(branch)
diff --git a/oxen/requirements.txt b/oxen/requirements.txt
index 1cc3549..6089845 100644
--- a/oxen/requirements.txt
+++ b/oxen/requirements.txt
@@ -1,14 +1,10 @@
 maturin
-numpy>=2.0.0
-opencv-python-headless==4.10.0.84
 pandas>=2.0.1
-polars>=1.15.0
+polars>=1.20.0
 pyarrow>=18.0.0
 pytest-datadir==1.4.1
 pytest==8.3.4
 requests>=2.32.3
 ruff>=0.5.0
-tensorflow>=2.16.1
 toml==0.10.2
-torch>=2.2.0
 tqdm>=4.67.1
diff --git a/oxen/src/py_repo.rs b/oxen/src/py_repo.rs
index f445a05..c7df01a 100644
--- a/oxen/src/py_repo.rs
+++ b/oxen/src/py_repo.rs
@@ -202,13 +202,21 @@ impl PyRepo {
                 branch: branch.to_string(),
                 subtree_paths: None,
                 depth: None,
-                all
+                all,
             };
             repositories::pull_remote_branch(&repo, &fetch_opts).await
         })?;
         Ok(())
     }
 
+    pub fn merge(&self, branch: &str) -> Result<Option<PyCommit>, PyOxenError> {
+        let repo = LocalRepository::from_dir(&self.path)?;
+        match repositories::merge::merge(&repo, branch)? {
+            Some(commit) => Ok(Some(PyCommit { commit })),
+            None => Ok(None),
+        }
+    }
+
     // pub fn diff(&self, path: &str) -> Result<PyDiff, PyOxenError> {
     //     let repo = LocalRepository::from_dir(&self.path)?;
     //     let diff =
diff --git a/oxen/tests/conftest.py b/oxen/tests/conftest.py
index a4d932b..d302d08 100644
--- a/oxen/tests/conftest.py
+++ b/oxen/tests/conftest.py
@@ -114,6 +114,16 @@ def house_prices_local_repo_no_commits(shared_datadir):
     yield repo
 
 
+@pytest.fixture
+def house_prices_local_repo_fully_committed(house_prices_local_repo_no_commits):
+    repo = house_prices_local_repo_no_commits
+
+    repo.add(os.path.join(repo.path, "prices.csv"))
+    repo.commit("Add prices.csv")
+
+    yield repo
+
+
 @pytest.fixture
 def empty_remote_repo():
     repo_name = f"py-ox/test_repo_{str(uuid.uuid4())}"
diff --git a/oxen/tests/test_chat_loader.py b/oxen/tests/test_chat_loader.py
deleted file mode 100644
index 9f6b925..0000000
--- a/oxen/tests/test_chat_loader.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from oxen.loaders import ChatLoader
-import os
-
-
-def test_chat_loader(chat_bot_local_repo_no_commits):
-    repo = chat_bot_local_repo_no_commits
-    prompt_file = os.path.join(repo.path, "prompt.txt")
-    data_file = os.path.join(repo.path, "examples.tsv")
-    loader = ChatLoader(prompt_file, data_file)
-    result = loader.run()[0]
-    assert result.height == 6
diff --git a/oxen/tests/test_dataloader_pytorch.py b/oxen/tests/test_dataloader_pytorch.py
deleted file mode 100644
index 78b25cc..0000000
--- a/oxen/tests/test_dataloader_pytorch.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# from torch.utils.data import DataLoader
-
-# from oxen import Dataset, DataEntry, Features
-
-
-# def test_dataset_load_celeba_train_download(
-#     celeba_remote_repo_fully_pushed, empty_local_dir
-# ):
-# _local_repo is the original local repo
-# remote_repo is the remote repo we pushed to
-# _local_repo, remote_repo = celeba_remote_repo_fully_pushed
-
-# download the remote dataframe, and load the data into a dataloader
-# cache_dir = empty_local_dir
-
-#  "images",   "annotations/train.csv"   "labels.txt"
-#         |                  /                |
-#         |                 /                 |
-#
-
-# Image Classification Loader Graph
-
-# csv_input = oxen.DataFrameLoader("annotations/train.csv")
-# images_input = oxen.DirLoader("images")
-# labels_input = oxen.FileLoader("labels.txt")
-
-# image_column = oxen.ColumnExtractor(["image"])(csv_input)
-# label_column = oxen.ColumnExtractor(["label"])(csv_input)
-
-# line_to_idx = oxen.LineToIdx()(label_column, labels_input)
-
-# image_output = oxen.ImageTensor()(image_column, images_input)
-# label_output = oxen.LabelTensor()(label_column, line_to_idx)
-
-# lag = oxen.LoaderGraph(
-#     inputs=[csv_input, images_input, labels_input],
-#     outputs=[image_output, label_output],
-# )
-
-# LLM Loader Graph
-
-# csv_input = oxen.DataFrameLoader("train.csv")
-# prompt_input = oxen.FileLoader("prompt.txt")
-
-# prompt_column = oxen.ColumnExtractor(["prompt"])(csv_input)
-# response_column = oxen.ColumnExtractor(["response"])(csv_input)
-
-# prompt_column = oxen.PromptTemplate()(prompt_input, prompt_column)
-
-# prompt_output = oxen.TextTokenizer()(prompt_column)
-# response_output = oxen.TextTokenizer()(response_column)
-
-# lag = oxen.LoaderGraph(
-#     inputs=[csv_input, prompt_input],
-#     outputs=[prompt_output, response_output],
-# )
-
-# House Price Regression
-
-# csv_input = oxen.DataFrameLoader("housing.csv")
-# price = oxen.ColumnExtractor(["price"])(csv_input)
-# features = oxen.ColumnExtractor(["sqft", "num_bed", "num_bath"])(csv_input)
-
-# lag = oxen.LoaderGraph(
-#     inputs=[csv_input],
-#     outputs=[price, features],
-# )
-
-# dataset = Dataset(remote_repo, lag, cache_dir=cache_dir, download=True)
-
-# # train_files = ["annotations/train.csv", "images"]
-# # dataset.load(train_files, download=True)
-
-# dataloader = DataLoader(dataset, batch_size=4, shuffle=False)
-
-# for i, data in enumerate(dataloader, 0):
-#     # get the inputs; data is a list of [inputs, labels]
-#     inputs, labels = data
diff --git a/oxen/tests/test_image_classification_loader.py b/oxen/tests/test_image_classification_loader.py
deleted file mode 100644
index 78b7a1f..0000000
--- a/oxen/tests/test_image_classification_loader.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import os
-import pytest
-import tensorflow as tf
-import numpy as np
-from oxen.loaders import ImageClassificationLoader
-
-
-def test_image_classification_dataloader_local(
-    celeba_local_repo_fully_committed, empty_local_dir
-):
-    repo = celeba_local_repo_fully_committed
-
-    train_file = os.path.join(repo.path, "annotations", "train.csv")
-    label_file = os.path.join(repo.path, "annotations", "labels.txt")
-
-    loader = ImageClassificationLoader(
-        imagery_root_dir=repo.path,
-        label_file=label_file,
-        df_file=train_file,
-        path_name="file",
-        label_name="hair_color",
-    )
-    data, labels, mapper = loader.run()
-
-    assert data.dtype == np.uint8
-
-    assert data.shape == (5, 218, 178, 3), "Data not returned in expected shape"
-    assert labels.shape == (5,)
-    assert len(mapper.items()) == 3
-
-    # Test ease of use with pytorch
-    # torch_data = TensorDataset(torch.from_numpy(data), torch.from_numpy(labels))
-    # torch_dl = DataLoader(torch_data, batch_size=1)
-    # assert len(torch_dl) == 5
-
-    # Test ease of use with tensorflow
-    dataset = tf.data.Dataset.from_tensor_slices((data, labels))
-
-    dataset = dataset.shuffle(buffer_size=len(data))
-    dataset = dataset.batch(1)
-
-    assert len(dataset) == 5
-
-
-def test_image_loader_missing_unique_label(
-    celeba_local_repo_fully_committed, empty_local_dir
-):
-    repo = celeba_local_repo_fully_committed
-    train_file = os.path.join(repo.path, "annotations", "test.csv")
-    label_file = os.path.join(repo.path, "annotations", "labels.txt")
-
-    loader = ImageClassificationLoader(
-        imagery_root_dir=repo.path,
-        label_file=label_file,
-        df_file=train_file,
-        path_name="file",
-        label_name="hair_color",
-    )
-    with pytest.raises(ValueError) as e:
-        data, labels, mapper = loader.run()
-
-    assert "label(s) in data missing" in str(e.value)
-
-
-def test_image_loader_resize_crop(
-    celeba_local_repo_fully_committed, empty_local_dir, tmp_path
-):
-    repo = celeba_local_repo_fully_committed
-    train_file = os.path.join(repo.path, "annotations", "train.csv")
-    label_file = os.path.join(repo.path, "annotations", "labels.txt")
-
-    loader = ImageClassificationLoader(
-        imagery_root_dir=repo.path,
-        label_file=label_file,
-        df_file=train_file,
-        path_name="file",
-        label_name="hair_color",
-        resize_to=512,
-        resize_method="crop",
-    )
-
-    data, labels, mapper = loader.run()
-
-    assert data.dtype == np.uint8
-
-    np.save(tmp_path / "imgs.npy", data)
-    assert data.shape == (5, 512, 512, 3), "Data not returned in expected shape"
-
-
-def test_image_loader_resize_squeeze(
-    celeba_local_repo_fully_committed, empty_local_dir, tmp_path
-):
-    repo = celeba_local_repo_fully_committed
-    train_file = os.path.join(repo.path, "annotations", "train.csv")
-    label_file = os.path.join(repo.path, "annotations", "labels.txt")
-
-    loader = ImageClassificationLoader(
-        imagery_root_dir=repo.path,
-        label_file=label_file,
-        df_file=train_file,
-        path_name="file",
-        label_name="hair_color",
-        resize_to=252,
-        resize_method="squeeze",
-    )
-    data, labels, mapper = loader.run()
-    np.save(tmp_path / "imgs.npy", data)
-    print("this this dtype is", data.dtype)
-    assert data.shape == (5, 252, 252, 3), "Data not returned in expected shape"
-
-
-def test_image_loader_resize_pad(
-    celeba_local_repo_fully_committed, empty_local_dir, tmp_path
-):
-    repo = celeba_local_repo_fully_committed
-    train_file = os.path.join(repo.path, "annotations", "train.csv")
-    label_file = os.path.join(repo.path, "annotations", "labels.txt")
-
-    loader = ImageClassificationLoader(
-        imagery_root_dir=repo.path,
-        label_file=label_file,
-        df_file=train_file,
-        path_name="file",
-        label_name="hair_color",
-        resize_to=111,
-        resize_method="pad",
-    )
-    data, labels, mapper = loader.run()
-    np.save(tmp_path / "imgs.npy", data)
-    print("this dtype is", data.dtype)
-    assert data.shape == (5, 111, 111, 3), "Data not returned in expected shape"
-
-
-def test_bad_resize_method(
-    celeba_local_repo_fully_committed, empty_local_dir, tmp_path
-):
-    repo = celeba_local_repo_fully_committed
-    train_file = os.path.join(repo.path, "annotations", "train.csv")
-    label_file = os.path.join(repo.path, "annotations", "labels.txt")
-
-    with pytest.raises(ValueError) as e:
-        print(e)
-        loader = ImageClassificationLoader(
-            imagery_root_dir=repo.path,
-            label_file=label_file,
-            df_file=train_file,
-            path_name="file",
-            label_name="hair_color",
-            resize_to=111,
-            resize_method="bad_method",
-        )
-        data, labels, mapper = loader.run()
diff --git a/oxen/tests/test_merge.py b/oxen/tests/test_merge.py
new file mode 100644
index 0000000..6ce90ab
--- /dev/null
+++ b/oxen/tests/test_merge.py
@@ -0,0 +1,45 @@
+import os
+
+
+def test_merge(house_prices_local_repo_fully_committed):
+    repo = house_prices_local_repo_fully_committed
+
+    prices_file = "prices.csv"
+    full_path = os.path.join(repo.path, prices_file)
+    initial_branch = repo.current_branch.name
+
+    # read initial prices.csv contents
+    with open(full_path, "r") as f:
+        initial_contents = f.read()
+
+    # oxen checkout -b new_branch
+    new_branch = "new_branch"
+    repo.checkout(new_branch, create=True)
+    assert repo.current_branch.name == "new_branch"
+
+    # update prices.csv
+    new_line = "6000000,6000,7,7,2015"
+    with open(full_path, "a") as f:
+        f.write(new_line)
+
+    # oxen add prices.csv
+    repo.add(full_path)
+
+    # oxen commit
+    repo.commit("Add new price")
+    with open(full_path, "r") as f:
+        updated_contents = f.read()
+
+    # oxen checkout main
+    repo.checkout(initial_branch)
+    assert repo.current_branch.name == initial_branch
+    with open(full_path, "r") as f:
+        contents = f.read()
+        assert contents == initial_contents
+
+    # oxen merge new_branch
+    repo.merge(new_branch)
+    assert repo.current_branch.name == initial_branch
+    with open(full_path, "r") as f:
+        contents = f.read()
+        assert contents == updated_contents
diff --git a/oxen/tests/test_regression_loader.py b/oxen/tests/test_regression_loader.py
deleted file mode 100644
index 70c1bbf..0000000
--- a/oxen/tests/test_regression_loader.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from oxen.loaders import RegressionLoader
-import os
-
-
-def test_regression_loader(house_prices_local_repo_no_commits):
-    repo = house_prices_local_repo_no_commits
-    data_file = os.path.join(repo.path, "prices.csv")
-
-    loader = RegressionLoader(data_file, "price", ["sqft", "num_bed", "num_bath"])
-    result = loader.run()
-    assert len(result) == 2
-
-    # is a dataframe
-    assert result[0].height == 5
-    assert result[0].width == 3
-
-    # is a series
-    assert result[1].len() == 5
-
-    print(result[0])
-    print(result[1])
diff --git a/oxen/tests/test_workspace_commit.py b/oxen/tests/test_workspace_commit.py
index a01e8d9..9f37488 100644
--- a/oxen/tests/test_workspace_commit.py
+++ b/oxen/tests/test_workspace_commit.py
@@ -1,4 +1,5 @@
 import os
+import pytest
 from oxen import RemoteRepo, Workspace
 
 
@@ -18,5 +19,11 @@ def test_commit_one_file(
 def test_commit_empty(celeba_remote_repo_one_image_pushed: RemoteRepo, shared_datadir):
     _, remote_repo = celeba_remote_repo_one_image_pushed
     workspace = Workspace(remote_repo, "main")
-    workspace.commit("a commit message")
-    assert len(remote_repo.log()) == 2
+
+    with pytest.raises(ValueError) as e:
+        # empty commits in workspace should raise an error
+        workspace.commit("a commit message")
+        assert "No changes to commit" in str(e)
+
+    # should still be 1 commit
+    assert len(remote_repo.log()) == 1