diff --git a/.github/workflows/run-commit-hooks.yml b/.github/workflows/run-commit-hooks.yml
new file mode 100644
index 0000000..7c945d8
--- /dev/null
+++ b/.github/workflows/run-commit-hooks.yml
@@ -0,0 +1,35 @@
+name: Check pre-commit hooks
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+  workflow_dispatch:
+
+env:
+  FORCE_COLOR: "1"
+
+jobs:
+  tests-using-pixi:
+    timeout-minutes: 10
+    runs-on: ubuntu-latest
+    steps:
+    - name: Check out the repository
+      uses: actions/checkout@v4
+      with:
+        fetch-depth: 0
+
+    - name: Install Pixi
+      uses: prefix-dev/setup-pixi@v0.8.8
+      with:
+        pixi-version: "latest"
+        run-install: false
+
+    - name: Install pre-commit
+      run: pixi global install pre-commit
+
+    - name: install pre-commit hooks
+      run: pre-commit install
+
+    - name: Run pre-commit hooks
+      run: pre-commit run --all --show-diff-on-failure
diff --git a/.github/workflows/run_tests.yaml b/.github/workflows/run_tests.yaml
deleted file mode 100644
index 3ed7f5c..0000000
--- a/.github/workflows/run_tests.yaml
+++ /dev/null
@@ -1,29 +0,0 @@
-name: bigwig-loader ci
-
-on:
-  push:
-    branches:
-      - main
-    tags:
-      - v*.*.*
-  pull_request: {}
-
-jobs:
-  # RUN MYPY STATIC TYPE ANALYSIS ON BIGWIG-LOADER SOURCE
-  typing:
-    name: mypy type analysis
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.10"
-      - name: Install dependencies
-        run: |
-          sed -i '0,/^ *"cupy",/s///' pyproject.toml
-          python -m pip install --upgrade pip
-          python -m pip install -e .[dev]
-      - name: Run mypy
-        run: |
-          mypy --python-version=3.10 bigwig_loader
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
new file mode 100644
index 0000000..770a702
--- /dev/null
+++ b/.github/workflows/tests.yml
@@ -0,0 +1,38 @@
+name: Tests-With-Pixi
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+  workflow_dispatch:
+
+env:
+  FORCE_COLOR: "1"
+
+jobs:
+  tests-using-pixi:
+    timeout-minutes: 10
+    runs-on: ubuntu-22.04-gpu-t4
+    steps:
+    - name: Check out the repository
+      uses: actions/checkout@v4
+      with:
+        fetch-depth: 0
+
+    - name: run nvidia-smi
+      run: nvidia-smi
+
+    - name: Install Pixi
+      uses: prefix-dev/setup-pixi@v0.8.8
+      with:
+        pixi-version: "latest"
+        run-install: false
+
+    - name: Install pre-commit
+      run: pixi global install pre-commit
+
+    - name: Install dev environment using pixi
+      run: pixi install --environment dev
+
+    - name: Run tests
+      run: pixi run --environment dev test
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index f8cfeaf..fe57a5f 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -29,8 +29,8 @@ repos:
     hooks:
     -   id: mypy
         additional_dependencies:
-        -    pydantic==2.1.1
-        -    pydantic-settings==2.0.3
+        -    pydantic==2.12.4
+        -    pydantic-settings==2.12.0
         exclude: "^(build|docs|tests|benchmark|examples)"
 -   repo: https://github.com/asottile/pyupgrade
     rev: v3.10.1
@@ -38,7 +38,7 @@ repos:
     -   id: pyupgrade
         args: [--py37-plus, --keep-runtime-typing]
 -   repo: https://github.com/PyCQA/bandit
-    rev: '1.7.5'
+    rev: '1.8.6'
     hooks:
     - id: bandit
       args: [ "-c", "pyproject.toml" ]
diff --git a/README.md b/README.md
index dc3b793..123b84e 100644
--- a/README.md
+++ b/README.md
@@ -3,8 +3,33 @@
 Fast batched dataloading of BigWig files containing epigentic track data and corresponding sequences powered by GPU
 for deep learning applications.
 
+> ⚠️ **BREAKING CHANGE (v0.3.0+)**: The output matrix dimensionality has changed from `(n_tracks, batch_size, sequence_length)` to `(batch_size, sequence_length, n_tracks)`. This change was long overdue and eliminates the need for (potentially memory expensive) transpose operations downstream. If you're upgrading from an earlier version, please update your code accordingly (probaby you need to delete one transpose in your code).
+
+> ✨ **NEW FEATURE (v0.3.0+)**: Full `bfloat16` support! You can now specify `dtype="bfloat16"` to get output tensors in bfloat16 format, reducing memory usage by 50%.
+
+
+
+
 ## Quickstart
 
+### Installation with Pixi
+Using [pixi](https://pixi.sh/) to install bigwig-loader is highly recommended.
+Please take a look at the pixi.toml file. If you just want to use bigwig-loader, just
+copy that pixi.toml, add the other libraries you need and use the "prod" environment
+(you don't need to clone this repo, pixi will download bigwig-loader from the
+conda "dataloading" channel):
+
+*   Install pixi, if not installed:
+    ```shell
+    curl -fsSL https://pixi.sh/install.sh | sh
+    ```
+
+* change directory to wherever you put the pixi.toml, and:
+    ```shell
+    pixi run -e prod <my_training_command>
+    ```
+
+
 ### Installation with conda/mamba
 
 Bigwig-loader mainly depends on the rapidsai kvikio library and cupy, both of which are best installed using
@@ -65,16 +90,17 @@ dataset = PytorchBigWigDataset(
     regions_of_interest=train_regions,
     collection=example_bigwigs_directory,
     reference_genome_path=reference_genome_file,
-    sequence_length=1000,
-    center_bin_to_predict=500,
+    sequence_length=1000,000,
+    center_bin_to_predict=500,000,
     window_size=1,
-    batch_size=32,
-    super_batch_size=1024,
-    batches_per_epoch=20,
+    batch_size=1,
+    super_batch_size=4,
+    batches_per_epoch=100,
     maximum_unknown_bases_fraction=0.1,
     sequence_encoder="onehot",
     n_threads=4,
     return_batch_objects=True,
+    dtype="bfloat16"
 )
 
 # Don't use num_workers > 0 in DataLoader. The heavy
@@ -88,7 +114,7 @@ class MyTerribleModel(torch.nn.Module):
         self.linear = torch.nn.Linear(4, 2)
 
     def forward(self, batch):
-        return self.linear(batch).transpose(1, 2)
+        return self.linear(batch)
 
 
 model = MyTerribleModel()
@@ -98,10 +124,10 @@ def poisson_loss(pred, target):
     return (pred - target * torch.log(pred.clamp(min=1e-8))).mean()
 
 for batch in dataloader:
-    # batch.sequences.shape = n_batch (32), sequence_length (1000), onehot encoding (4)
+    # batch.sequences.shape = n_batch x sequence_length x onehot encoding (4)
     pred = model(batch.sequences)
-    # batch.values.shape = n_batch (32), n_tracks (2) center_bin_to_predict (500)
-    loss = poisson_loss(pred[:, :, 250:750], batch.values)
+    # batch.values.shape = n_batch x center_bin_to_predict x n_tracks
+    loss = poisson_loss(pred[:, 250000:750000, :], batch.values)
     print(loss)
     optimizer.zero_grad()
     loss.backward()
@@ -166,19 +192,23 @@ anything is unclear, please open an issue.
 
 ### Environment
 
+The pixi.toml includes a dev environment that has bigwig-loader installed
+as an editable pypi dependency.
+
 1. `git clone git@github.com:pfizer-opensource/bigwig-loader`
 2. `cd bigwig-loader`
-3. create the conda environment" `conda env create -f environment.yml`
-4. `pip install -e '.[dev]'`
-5. run `pre-commit install` to install the pre-commit hooks
+3. optional: `pixi install -e dev`
+4. run `pre-commit install` to install the pre-commit hooks
 
 ### Run Tests
 Tests are in the tests directory. One of the most important tests is
 test_against_pybigwig which makes sure that if there is a mistake in
 pyBigWIg, it is also in bigwig-loader.
 
+In order to run these tests you need gpu.
+
 ```shell
-pytest -vv .
+pixi run -e dev test
 ```
 
 When github runners with GPU's will become available we would also
diff --git a/bigwig_loader/batch_processor.py b/bigwig_loader/batch_processor.py
index 082916d..1d8efa1 100644
--- a/bigwig_loader/batch_processor.py
+++ b/bigwig_loader/batch_processor.py
@@ -2,6 +2,7 @@
 
 # from typing import TYPE_CHECKING
 from typing import Callable
+from typing import Literal
 from typing import Optional
 from typing import Sequence
 from typing import Union
@@ -12,6 +13,7 @@
 
 from bigwig_loader.bigwig import BigWig
 from bigwig_loader.decompressor import Decoder
+from bigwig_loader.default_value import replace_out_tensor_if_needed
 from bigwig_loader.functional import load_decode_search
 from bigwig_loader.intervals_to_values import intervals_to_values
 from bigwig_loader.memory_bank import MemoryBank
@@ -59,19 +61,29 @@ def decoder(self) -> Decoder:
     def memory_bank(self) -> MemoryBank:
         return MemoryBank(elastic=True)
 
-    def _get_out_tensor(self, batch_size: int, sequence_length: int) -> cp.ndarray:
+    def _get_out_tensor(
+        self,
+        batch_size: int,
+        sequence_length: int,
+        dtype: Literal["bfloat16", "float32"] = "float32",
+    ) -> cp.ndarray:
         """Resuses a reserved tensor if possible (when out shape is constant),
         otherwise creates a new one.
         args:
             batch_size: batch size
             sequence_length: length of genomic sequence
+            dtype: output dtype ('float32' or 'bfloat16')
          returns:
-            tensor of shape (number of bigwig files, batch_size, sequence_length)
+            tensor of shape (batch_size, sequence_length, number of bigwig files)
         """
 
-        shape = (len(self._bigwigs), batch_size, sequence_length)
-        if self._out.shape != shape:
-            self._out = cp.zeros(shape, dtype=cp.float32)
+        self._out = replace_out_tensor_if_needed(
+            self._out,
+            batch_size=batch_size,
+            sequence_length=sequence_length,
+            number_of_tracks=len(self._bigwigs),
+            dtype=dtype,
+        )
         return self._out
 
     def preprocess(
@@ -105,6 +117,7 @@ def get_batch(
         window_size: int = 1,
         scaling_factors_cupy: Optional[cp.ndarray] = None,
         default_value: float = 0.0,
+        dtype: Literal["float32", "bfloat16"] = "float32",
         out: Optional[cp.ndarray] = None,
     ) -> cp.ndarray:
         (
@@ -139,9 +152,10 @@ def get_batch(
             query_ends=abs_end,
             window_size=window_size,
             default_value=default_value,
+            dtype=dtype,
             out=out,
         )
-        batch = cp.transpose(out, (1, 0, 2))
+        # batch = cp.transpose(out, (1, 0, 2))
         if scaling_factors_cupy is not None:
-            batch *= scaling_factors_cupy
-        return batch
+            out *= scaling_factors_cupy
+        return out
diff --git a/bigwig_loader/bigwig.py b/bigwig_loader/bigwig.py
index cdbab14..f65ad75 100644
--- a/bigwig_loader/bigwig.py
+++ b/bigwig_loader/bigwig.py
@@ -424,7 +424,7 @@ def _guess_max_rows_per_chunk(
         data_offsets = self.rtree_leaf_nodes["data_offset"]
         data_sizes = self.rtree_leaf_nodes["data_size"]
         if len(data_offsets) > sample_size:
-            sample_indices = sample(range(len(data_offsets)), sample_size)
+            sample_indices = sample(range(len(data_offsets)), sample_size)  # nosec
             data_offsets = data_offsets[sample_indices]
             data_sizes = data_sizes[sample_indices]
 
diff --git a/bigwig_loader/collection.py b/bigwig_loader/collection.py
index 6d6fcf0..f117412 100644
--- a/bigwig_loader/collection.py
+++ b/bigwig_loader/collection.py
@@ -97,7 +97,7 @@ def reset_gpu(self) -> None:
         need to be recreated on the new gpu.
         """
 
-        self._out = cp.zeros((len(self), 1, 1), dtype=cp.float32)
+        # self._out = cp.zeros(1, (len(self), 1), dtype=cp.float32)
         if "decoder" in self.__dict__:
             del self.__dict__["decoder"]
         if "memory_bank" in self.__dict__:
@@ -131,7 +131,7 @@ def batch_processor(self) -> BatchProcessor:
     @cached_property
     def scaling_factors_cupy(self) -> cp.ndarray:
         return cp.asarray(self._scaling_factors, dtype=cp.float32).reshape(
-            1, len(self._scaling_factors), 1
+            1, 1, len(self._scaling_factors)
         )
 
     def get_batch(
diff --git a/bigwig_loader/dataset.py b/bigwig_loader/dataset.py
index fa3753e..66533e3 100644
--- a/bigwig_loader/dataset.py
+++ b/bigwig_loader/dataset.py
@@ -87,6 +87,12 @@ class BigWigDataset:
             tracks in case sub_sample_tracks is set. Should be Iterable batches of track indices.
         return_batch_objects: if True, the batches will be returned as instances of
             bigwig_loader.batch.Batch
+        dtype: float32 or bfloat16 output encoding of the target values (not the sequence encoding).
+            Cupy does not support bfloat16 yet, but the cuda kernel that creates the target values
+            does. When bfloat16 is choosen, the cupy array will show to have the data type uint16
+            which can, for example, be converted to a torch.bfloat16 by
+            torch_tensor = torch.as_tensor(out) # torch uint16
+            torch_tensor = torch_tensor.view(torch.bfloat16)    # Reinterpret as bfloat16
     """
 
     def __init__(
@@ -107,7 +113,7 @@ def __init__(
         ] = "onehot",
         file_extensions: Sequence[str] = (".bigWig", ".bw"),
         crawl: bool = True,
-        scale: Optional[dict[Union[str | Path], Any]] = None,
+        scale: Optional[dict[Union[str, Path], Any]] = None,
         default_value: float = 0.0,
         first_n_files: Optional[int] = None,
         position_sampler_buffer_size: int = 100000,
@@ -117,6 +123,7 @@ def __init__(
         custom_position_sampler: Optional[Iterable[tuple[str, int]]] = None,
         custom_track_sampler: Optional[Iterable[list[int]]] = None,
         return_batch_objects: bool = False,
+        dtype: Literal["float32", "bfloat16"] = "float32",
     ):
         super().__init__()
 
@@ -176,6 +183,8 @@ def __init__(
         else:
             self._track_sampler = None
 
+        self._dtype = dtype
+
     def _create_dataloader(self) -> StreamedDataloader:
         sequence_sampler = GenomicSequenceSampler(
             reference_genome_path=self.reference_genome_path,
@@ -199,6 +208,7 @@ def _create_dataloader(self) -> StreamedDataloader:
             slice_size=self.batch_size,
             window_size=self.window_size,
             default_value=self._default_value,
+            dtype=self._dtype,
         )
 
     def __iter__(
diff --git a/bigwig_loader/default_value.py b/bigwig_loader/default_value.py
new file mode 100644
index 0000000..75e6b32
--- /dev/null
+++ b/bigwig_loader/default_value.py
@@ -0,0 +1,118 @@
+from math import isnan
+from typing import Literal
+
+import cupy as cp
+
+
+def get_default_value(value: float, dtype: str) -> cp.uint16 | cp.float32:
+    """bfloat16 is not supported by cupy yet,
+    so we use uint16. This only works for the
+    final output, because we have a custom
+    cuda kernel (which does support bfloat16)
+    populating the final out tensor.
+    """
+    if dtype == "bfloat16":
+        if value == 0:
+            return cp.uint16(0)
+        if isnan(value):
+            return cp.uint16(0x7FC0)
+        else:
+            raise ValueError(
+                "When using bfloat16, please do not use"
+                "any other default value than 0 or NaN."
+            )
+    if dtype == "float32" or dtype == cp.float32:
+        return cp.float32(value)
+    raise ValueError("Only bfloat16 or float32 are supported.")
+
+
+def create_output_tensor(
+    batch_size: int,
+    sequence_length: int,
+    number_of_tracks: int,
+    default_value: float = 0.0,
+    dtype: Literal["bfloat16", "float32"] = "float32",
+) -> cp.ndarray:
+    """Get output tensor in the correct shape: batch_size x sequence_length x n_tracks"""
+    shape = (batch_size, sequence_length, number_of_tracks)
+    if dtype == "bfloat16":
+        out_dtype = cp.uint16
+    elif dtype == "float32":
+        out_dtype = cp.float32
+    else:
+        raise ValueError("Only bfloat16 or float32 are supported.")
+    converted_default_value = get_default_value(value=default_value, dtype=dtype)
+
+    return cp.full(shape, converted_default_value, dtype=out_dtype)
+
+
+def output_tensor_correct_format(
+    tensor: cp.ndarray,
+    batch_size: int,
+    sequence_length: int,
+    number_of_tracks: int,
+    dtype: str,
+) -> bool:
+    if tensor.shape != (batch_size, sequence_length, number_of_tracks):
+        return False
+    if dtype not in ("bfloat16", "float32"):
+        return False
+    if dtype == "bfloat16" and tensor.dtype != cp.dtype("uint16"):
+        return False
+    if (dtype == "float32" or dtype == cp.float32) and tensor.dtype != cp.dtype(
+        "float32"
+    ):
+        return False
+    return True
+
+
+def fill(
+    tensor: cp.ndarray,
+    default_value: float,
+    dtype: Literal["bfloat16", "float32"] = "float32",
+) -> cp.ndarray:
+    tensor.fill(get_default_value(default_value, dtype=dtype))
+    return tensor
+
+
+def replace_out_tensor_if_needed(
+    tensor: cp.ndarray | None,
+    batch_size: int,
+    sequence_length: int,
+    number_of_tracks: int,
+    default_value: float = 0.0,
+    dtype: Literal["bfloat16", "float32"] = "float32",
+    reset_values: bool = False,
+) -> cp.ndarray:
+    """Replace tensor if needed. First check if the given tensor is correct format.
+
+    If the tensor is None or has incorrect shape/dtype, create a new one.
+    Otherwise, return the existing tensor.
+    """
+    if tensor is None:
+        return create_output_tensor(
+            batch_size=batch_size,
+            sequence_length=sequence_length,
+            number_of_tracks=number_of_tracks,
+            default_value=default_value,
+            dtype=dtype,
+        )
+
+    if output_tensor_correct_format(
+        tensor=tensor,
+        batch_size=batch_size,
+        sequence_length=sequence_length,
+        number_of_tracks=number_of_tracks,
+        dtype=dtype,
+    ):
+        if reset_values:
+            tensor = fill(tensor, default_value=default_value, dtype=dtype)
+        return tensor
+
+    return create_output_tensor(
+        batch_size=batch_size,
+        sequence_length=sequence_length,
+        number_of_tracks=number_of_tracks,
+        default_value=default_value,
+        dtype=dtype,
+    )
diff --git a/bigwig_loader/download_example_data.py b/bigwig_loader/download_example_data.py
index 54e5fe1..5e8f046 100644
--- a/bigwig_loader/download_example_data.py
+++ b/bigwig_loader/download_example_data.py
@@ -84,6 +84,10 @@ def unzip_gz_file(compressed_file_path: Path, output_file_path: Path) -> Path:
         "https://www.encodeproject.org/files/ENCFF270ISQ/@@download/ENCFF270ISQ.bigWig",
         "5fef60f9f1e43b9a17075c352650865e",
     ),
+    "mc_Late_Childhood_GLU_BS.bins128.bw": (
+        "https://brainome.ucsd.edu/emukamel/PEC_igv/binc_bigwig/mc_Late_Childhood_GLU_BS.bins128.bw",
+        "c91eee81f11e4dc7cecaae72abd14c66",
+    ),
 }
 
 
diff --git a/bigwig_loader/intervals_to_values.py b/bigwig_loader/intervals_to_values.py
index b379747..72e5808 100644
--- a/bigwig_loader/intervals_to_values.py
+++ b/bigwig_loader/intervals_to_values.py
@@ -2,22 +2,47 @@
 import math
 from math import isnan
 from pathlib import Path
+from typing import Literal
 
 import cupy as cp
 
+from bigwig_loader.default_value import replace_out_tensor_if_needed
 from bigwig_loader.searchsorted import interval_searchsorted
 
 CUDA_KERNEL_DIR = Path(__file__).parent.parent / "cuda_kernels"
 
 
-def get_cuda_kernel() -> str:
-    with open(CUDA_KERNEL_DIR / "intervals_to_values.cu") as f:
-        kernel_code = f.read()
-    return kernel_code
+class Kernel:
+    def __init__(
+        self, cudafile: Path = CUDA_KERNEL_DIR / "intervals_to_values_contiguous.cu"
+    ) -> None:
+        with open(cudafile) as f:
+            self.kernel_code = f.read()
 
+        self.kernel_names = {
+            "float32": "intervals_to_values_float32",
+            "bfloat16": "intervals_to_values_bfloat16",
+        }
+        self._kernels: dict[str, cp.RawKernel] = {}
 
-cuda_kernel = cp.RawKernel(get_cuda_kernel(), "intervals_to_values")
-cuda_kernel.compile()
+    def get_kernel_by_dtype(
+        self, dtype: Literal["float32", "bfloat16"]
+    ) -> cp.RawKernel:
+        if dtype in self._kernels:
+            return self._kernels[dtype]
+        kernel = cp.RawKernel(self.kernel_code, self.kernel_names[dtype])
+        kernel.compile()
+        self._kernels[dtype] = kernel
+        return kernel
+
+    def __call__(  # type: ignore[no-untyped-def]
+        self, *args, dtype: Literal["float32", "bfloat16"], **kwargs
+    ) -> cp.ndarray:
+        kernel = self.get_kernel_by_dtype(dtype=dtype)
+        return kernel(*args, **kwargs)
+
+
+cuda_kernel = Kernel()
 
 
 def intervals_to_values(
@@ -32,6 +57,8 @@ def intervals_to_values(
     window_size: int = 1,
     default_value: float = 0.0,
     out: cp.ndarray | None = None,
+    scaling_factors: cp.ndarray | None = None,
+    dtype: Literal["float32", "bfloat16"] = "float32",
 ) -> cp.ndarray:
     """
     This function converts intervals to values. It can do this for multiple tracks at once.
@@ -45,6 +72,7 @@ def intervals_to_values(
     When the sequence length is not a multiple of window_size, the output length will
     be sequence_length // window_size, ignoring the last "incomplete" window.
 
+    Output shape: batch_size x sequence_length x n_tracks (no transpose needed!)
 
     Args:
         array_start: array of length sum(sizes) with the start positions of the intervals
@@ -52,16 +80,17 @@ def intervals_to_values(
         array_value: array of length sum(sizes) with the value for those intervals
         query_starts: array of length batch_size with the (genomic) start positions of each batch element
         query_ends: array of length batch_size with the (genomic) end positions of each batch element
-        out: array of size n_tracks x batch_size x sequence_length to store the output
         found_starts: result of searchsorted (if precalculated). Indices into track_starts.
         found_ends: result of searchsorted (if precalculated). Indices into track_ends.
         sizes: number of elements in track_starts/track_ends/track_values for each track.
             Only needed when found_starts and found_ends are not given.
         window_size: size in basepairs to average over (default: 1)
         default_value: value to use for regions where no data is specified (default: 0.0)
-        out: array of size n_tracks x batch_size x sequence_length to store the output.
+        out: array of size batch_size x sequence_length x n_tracks to store the output.
+        scaling_factors: optional array of shape (n_tracks,) to scale each track's values
+        dtype: output dtype, either 'float32' or 'bfloat16'
     Returns:
-        out: array of size n_tracks x batch_size x sequence_length
+        out: array of size batch_size x sequence_length x n_tracks
 
     """
     if cp.unique(query_ends - query_starts).size != 1:
@@ -72,11 +101,9 @@ def intervals_to_values(
     sequence_length = (query_ends[0] - query_starts[0]).item()
 
     if (found_starts is None or found_ends is None) and sizes is None:
-        # just one size, which is the length of the entire track_starts/tracks_ends/tracks_values
         sizes = cp.asarray([len(array_start)], dtype=array_start.dtype)
 
     if found_starts is None or found_ends is None:
-        # n_subarrays x n_queries
         found_starts, found_ends = interval_searchsorted(
             array_start,
             array_end,
@@ -86,26 +113,23 @@ def intervals_to_values(
             absolute_indices=True,
         )
 
-    if out is None:
-        logging.debug(f"Creating new out tensor with default value {default_value}")
-
-        out = cp.full(
-            (found_starts.shape[0], len(query_starts), sequence_length // window_size),
-            default_value,
-            dtype=cp.float32,
-        )
-        logging.debug(out)
+    reduced_sequence_length = sequence_length // window_size
+    batch_size = len(query_starts)
+    num_tracks = found_starts.shape[0]
 
-    else:
-        logging.debug(f"Setting default value in output tensor to {default_value}")
-        out.fill(default_value)
-        logging.debug(out)
+    out = replace_out_tensor_if_needed(
+        out,
+        batch_size=batch_size,
+        sequence_length=reduced_sequence_length,
+        number_of_tracks=num_tracks,
+        default_value=default_value,
+        dtype=dtype,
+        reset_values=True,
+    )
 
     max_number_intervals = min(
         sequence_length, (found_ends - found_starts).max().item()
     )
-    batch_size = query_starts.shape[0]
-    num_tracks = found_starts.shape[0]
 
     if window_size == 1:
         n_threads_needed = batch_size * max_number_intervals * num_tracks
@@ -127,6 +151,12 @@ def intervals_to_values(
     array_value = cp.ascontiguousarray(array_value)
     default_value_isnan = isnan(default_value)
 
+    # Handle scaling factors - convert to float32 if provided
+    if scaling_factors is not None:
+        scaling_factors = cp.ascontiguousarray(scaling_factors, dtype=cp.float32)
+    else:
+        scaling_factors = cp.asarray([], dtype=cp.float32)
+
     cuda_kernel(
         (grid_size,),
         (block_size,),
@@ -140,13 +170,15 @@ def intervals_to_values(
             array_value,
             num_tracks,
             batch_size,
-            sequence_length,
+            sequence_length,  # should really sequence length, not reduced sequence length
             max_number_intervals,
             window_size,
             cp.float32(default_value),
             default_value_isnan,
+            scaling_factors,
             out,
         ),
+        dtype=dtype,
     )
     return out
 
diff --git a/bigwig_loader/pytorch.py b/bigwig_loader/pytorch.py
index 08750dc..922f7ce 100644
--- a/bigwig_loader/pytorch.py
+++ b/bigwig_loader/pytorch.py
@@ -74,7 +74,9 @@ def __init__(
         self.track_names = track_names
 
     @classmethod
-    def from_batch(cls, batch: Batch) -> "PytorchBatch":
+    def from_batch(
+        cls, batch: Batch, dtype: Literal["float32", "bfloat16"] = "float32"
+    ) -> "PytorchBatch":
         if batch.other_batched is not None:
             other_batched = (
                 [cls._convert_if_possible(tensor) for tensor in batch.other_batched],
@@ -85,7 +87,7 @@ def from_batch(cls, batch: Batch) -> "PytorchBatch":
             chromosomes=cls._convert_if_possible(batch.chromosomes),
             starts=cls._convert_if_possible(batch.starts),
             ends=cls._convert_if_possible(batch.ends),
-            values=cls._convert_if_possible(batch.values),
+            values=cls._convert_track_values(batch.values, dtype=dtype),
             track_indices=cls._convert_if_possible(batch.track_indices),
             sequences=cls._convert_if_possible(batch.sequences),
             other_batched=other_batched,
@@ -99,6 +101,18 @@ def _convert_if_possible(tensor: Any) -> Any:
             return torch.as_tensor(tensor)
         return tensor
 
+    @staticmethod
+    def _convert_track_values(
+        tensor: cp.ndarray, dtype: Literal["float32", "bfloat16"] = "float32"
+    ) -> torch.Tensor:
+        torch_tensor = torch.as_tensor(tensor)
+
+        # Handle bfloat16 conversion from uint16
+        if dtype == "bfloat16" and tensor.dtype == cp.uint16:
+            torch_tensor = torch_tensor.view(torch.bfloat16)
+
+        return torch_tensor
+
 
 GENOMIC_SEQUENCE_TYPE = Union[torch.Tensor, list[str], None]
 BATCH_TYPE = Union[
@@ -172,6 +186,7 @@ class PytorchBigWigDataset(IterableDataset[BATCH_TYPE]):
         custom_track_sampler: if specified, this sampler will be used to sample tracks. When not
             specified, each batch simply contains all tracks, or a randomly sellected subset of
             tracks in case sub_sample_tracks is set. Should be Iterable batches of track indices.
+        dtype: float32 or bfloat16 output encoding of the target values (not the sequence encoding).
     """
 
     def __init__(
@@ -202,6 +217,7 @@ def __init__(
         custom_position_sampler: Optional[Iterable[tuple[str, int]]] = None,
         custom_track_sampler: Optional[Iterable[list[int]]] = None,
         return_batch_objects: bool = False,
+        dtype: Literal["float32", "bfloat16"] = "float32",
     ):
         super().__init__()
         self._dataset = BigWigDataset(
@@ -229,6 +245,7 @@ def __init__(
             custom_position_sampler=custom_position_sampler,
             custom_track_sampler=custom_track_sampler,
             return_batch_objects=True,
+            dtype=dtype,
         )
         self._return_batch_objects = return_batch_objects
 
@@ -236,7 +253,7 @@ def __iter__(
         self,
     ) -> Iterator[BATCH_TYPE]:
         for batch in self._dataset:
-            pytorch_batch = PytorchBatch.from_batch(batch)  # type: ignore
+            pytorch_batch = PytorchBatch.from_batch(batch, dtype=self._dataset._dtype)  # type: ignore
             if self._return_batch_objects:
                 yield pytorch_batch
             elif pytorch_batch.track_indices is None:
diff --git a/bigwig_loader/sampler/track_sampler.py b/bigwig_loader/sampler/track_sampler.py
index c21f379..68854aa 100644
--- a/bigwig_loader/sampler/track_sampler.py
+++ b/bigwig_loader/sampler/track_sampler.py
@@ -9,4 +9,6 @@ def __init__(self, total_number_of_tracks: int, sample_size: int):
 
     def __iter__(self) -> Iterator[list[int]]:
         while True:
-            yield sorted(sample(range(self.total_number_of_tracks), self.sample_size))
+            yield sorted(
+                sample(range(self.total_number_of_tracks), self.sample_size)  # nosec
+            )
diff --git a/bigwig_loader/streamed_dataset.py b/bigwig_loader/streamed_dataset.py
index 314058a..1ddbb33 100644
--- a/bigwig_loader/streamed_dataset.py
+++ b/bigwig_loader/streamed_dataset.py
@@ -4,6 +4,7 @@
 from typing import Generator
 from typing import Iterable
 from typing import Iterator
+from typing import Literal
 
 import cupy as cp
 
@@ -12,6 +13,7 @@
 from bigwig_loader.batch_processor import BatchProcessor
 from bigwig_loader.batch_processor import PreprocessedReturnType
 from bigwig_loader.collection import BigWigCollection
+from bigwig_loader.default_value import replace_out_tensor_if_needed
 from bigwig_loader.intervals_to_values import intervals_to_values
 
 InputBatchType = Batch | IntervalType
@@ -109,6 +111,7 @@ def __init__(
         slice_size: int | None = None,
         window_size: int = 1,
         default_value: float = 0.0,
+        dtype: Literal["float32", "bfloat16"] = "float32",
     ):
         self.input_generator = input_generator
         self.collection = collection
@@ -129,6 +132,7 @@ def __init__(
         self._entered = False
         self._out = None
         self._default_value = default_value
+        self._dtype = dtype
 
     def __enter__(self) -> "StreamedDataloader":
         self._entered = True
@@ -197,17 +201,26 @@ def _generate_batches(self) -> Generator[Batch, None, None]:
                 slice_size = self._determine_slice_size(n_samples=n_samples)
 
                 out = self._get_out_tensor(
+                    batch_size=slice_size,
                     sequence_length=(abs_end[0] - abs_start[0]).item()
                     // self.window_size,
                     number_of_tracks=found_starts.shape[0],
-                    batch_size=slice_size,
                 )
 
+                # Prepare scaling factors if needed
+                scaling_factors = None
+                if self.collection.scaling_factors_cupy is not None:
+                    scaling_factors = (
+                        self.collection.scaling_factors_cupy.squeeze()
+                    )  # Shape: (n_tracks,)
+                    if batch.track_indices is not None:
+                        scaling_factors = scaling_factors[batch.track_indices]
+
                 for select in self._slices_objects(n_samples, slice_size):
                     with self.main_stream as stream:
                         stream.synchronize()
 
-                        value_matrix = intervals_to_values(
+                        values = intervals_to_values(
                             array_start=start_data,
                             array_end=end_data,
                             array_value=value_data,
@@ -218,21 +231,15 @@ def _generate_batches(self) -> Generator[Batch, None, None]:
                             window_size=self.window_size,
                             default_value=self._default_value,
                             out=out,
+                            dtype=self._dtype,
+                            scaling_factors=scaling_factors,  # NEW: pass scaling factors to kernel
                         )
 
-                        values = cp.transpose(value_matrix, (1, 0, 2))
-                        if self.collection.scaling_factors_cupy is not None:
-                            scaling_factors = self.collection.scaling_factors_cupy
-                            if batch.track_indices is not None:
-                                scaling_factors = scaling_factors[
-                                    :, batch.track_indices, :
-                                ]
-
-                            values *= scaling_factors
-                        stream.synchronize()
+                        # Removed the old scaling multiplication code - now handled in kernel!
 
                         sliced_query = batch[select]
                         sliced_query.values = values
+                        stream.synchronize()
 
                     yield sliced_query
 
@@ -246,12 +253,16 @@ def _generate_batches(self) -> Generator[Batch, None, None]:
             raise e
 
     def _get_out_tensor(
-        self, number_of_tracks: int, batch_size: int, sequence_length: int
+        self, batch_size: int, sequence_length: int, number_of_tracks: int
     ) -> cp.ndarray:
-        shape = (number_of_tracks, batch_size, sequence_length)
-
-        if self._out is None or self._out.shape != shape:
-            self._out = cp.full(shape, self._default_value, dtype=cp.float32)
+        self._out = replace_out_tensor_if_needed(
+            tensor=self._out,
+            batch_size=batch_size,
+            sequence_length=sequence_length,
+            number_of_tracks=number_of_tracks,
+            default_value=self._default_value,
+            dtype=self._dtype,
+        )
         return self._out
 
     def _determine_slice_size(self, n_samples: int) -> int:
diff --git a/conda-forge-pypi-map.json b/conda-forge-pypi-map.json
new file mode 100644
index 0000000..f6ef1dd
--- /dev/null
+++ b/conda-forge-pypi-map.json
@@ -0,0 +1,5 @@
+{
+  "pytorch": "torch",
+  "pytorch-gpu": "torch",
+  "pytorch-cpu": "torch"
+}
diff --git a/cuda_kernels/intervals_to_values.cu b/cuda_kernels/intervals_to_values.cu
index 2d1299d..3e27fba 100644
--- a/cuda_kernels/intervals_to_values.cu
+++ b/cuda_kernels/intervals_to_values.cu
@@ -21,7 +21,7 @@ void intervals_to_values(
 
     int thread = blockIdx.x * blockDim.x + threadIdx.x;
 
-//     # out is a 1D array of size batch_size x n_tracks x sequence_length
+//     # out is a 1D array of n_tracks x batch_size x sequence_length
 //
 //     # n_tracks x n_batch
 
diff --git a/cuda_kernels/intervals_to_values_contiguous.cu b/cuda_kernels/intervals_to_values_contiguous.cu
new file mode 100644
index 0000000..c4e699f
--- /dev/null
+++ b/cuda_kernels/intervals_to_values_contiguous.cu
@@ -0,0 +1,215 @@
+#include <math_constants.h>
+#include <cuda_bf16.h>
+
+template<typename T>
+__device__ __forceinline__ T cast_value(float value);
+
+template<>
+__device__ __forceinline__ float cast_value<float>(float value) {
+    return value;
+}
+
+template<>
+__device__ __forceinline__ __nv_bfloat16 cast_value<__nv_bfloat16>(float value) {
+    return __float2bfloat16(value);
+}
+
+template<typename T>
+__global__
+void intervals_to_values_kernel(
+        const unsigned int* query_starts,
+        const unsigned int* query_ends,
+        const unsigned int* found_starts,
+        const unsigned int* found_ends,
+        const unsigned int* track_starts,
+        const unsigned int* track_ends,
+        const float* track_values,
+        const int n_tracks,
+        const int batch_size,
+        const int sequence_length,
+        const int max_number_intervals,
+        const int window_size,
+        const float default_value,
+        const bool default_value_isnan,
+        const float* scaling_factors,  // NEW: scaling factors array of length n_tracks
+        T* out
+) {
+    int thread = blockIdx.x * blockDim.x + threadIdx.x;
+
+    // Same thread indexing as before
+    int batch_index = thread % batch_size;
+    int i = thread % (batch_size * n_tracks);
+    int track_index = i / batch_size;
+
+    if (window_size == 1) {
+        int j = (thread / (batch_size * n_tracks)) % max_number_intervals;
+
+        int found_start_index = found_starts[i];
+        int found_end_index = found_ends[i];
+        int query_start = query_starts[batch_index];
+        int query_end = query_ends[batch_index];
+
+        int cursor = found_start_index + j;
+
+        if (cursor < found_end_index) {
+            int interval_start = track_starts[cursor];
+            int interval_end = track_ends[cursor];
+            int start_index = max(interval_start - query_start, 0);
+            int end_index = min(interval_end, query_end) - query_start;
+
+            float value = track_values[cursor];
+
+            // Apply scaling factor here
+            if (scaling_factors != nullptr) {
+                value *= scaling_factors[track_index];
+            }
+
+            T typed_value = cast_value<T>(value);
+
+            int base_offset = batch_index * (sequence_length * n_tracks) + track_index;
+
+            for (int pos = start_index; pos < end_index; pos++) {
+                out[base_offset + pos * n_tracks] = typed_value;
+            }
+        }
+    } else {
+        int found_start_index = found_starts[i];
+        int found_end_index = found_ends[i];
+        int query_start = query_starts[batch_index];
+        int query_end = query_ends[batch_index];
+
+        int cursor = found_start_index;
+        int window_index = 0;
+        float summation = 0.0f;
+        int valid_count = 0;
+
+        int reduced_dim = sequence_length / window_size;
+
+        while (cursor < found_end_index && window_index < reduced_dim) {
+            int window_start = window_index * window_size;
+            int window_end = window_start + window_size;
+
+            int interval_start = track_starts[cursor];
+            int interval_end = track_ends[cursor];
+
+            int start_index = max(interval_start - query_start, 0);
+            int end_index = min(interval_end, query_end) - query_start;
+
+            if (start_index >= window_end) {
+                float final_value;
+                if (default_value_isnan) {
+                    final_value = (valid_count > 0) ? (summation / valid_count) : CUDART_NAN_F;
+                } else {
+                    summation = summation + (window_size - valid_count) * default_value;
+                    final_value = summation / window_size;
+                }
+
+                // Apply scaling factor
+                if (scaling_factors != nullptr) {
+                    final_value *= scaling_factors[track_index];
+                }
+
+                T out_value = cast_value<T>(final_value);
+                int out_idx = batch_index * (reduced_dim * n_tracks) + window_index * n_tracks + track_index;
+                out[out_idx] = out_value;
+
+                summation = 0.0f;
+                valid_count = 0;
+                window_index += 1;
+                continue;
+            }
+
+            int number = min(window_end, end_index) - max(window_start, start_index);
+
+            if (number > 0) {
+                summation += number * track_values[cursor];
+                valid_count += number;
+            }
+
+            if (end_index >= window_end || cursor + 1 >= found_end_index) {
+                float final_value;
+                if (default_value_isnan) {
+                    final_value = (valid_count > 0) ? (summation / valid_count) : CUDART_NAN_F;
+                } else {
+                    summation = summation + (window_size - valid_count) * default_value;
+                    final_value = summation / window_size;
+                }
+
+                // Apply scaling factor
+                if (scaling_factors != nullptr) {
+                    final_value *= scaling_factors[track_index];
+                }
+
+                T out_value = cast_value<T>(final_value);
+                int out_idx = batch_index * (reduced_dim * n_tracks) + window_index * n_tracks + track_index;
+                out[out_idx] = out_value;
+
+                summation = 0.0f;
+                valid_count = 0;
+                window_index += 1;
+            }
+
+            if (end_index < window_end) {
+                cursor += 1;
+            }
+        }
+    }
+}
+
+// Update wrapper functions to include scaling_factors
+
+extern "C" __global__
+void intervals_to_values_float32(
+        const unsigned int* query_starts,
+        const unsigned int* query_ends,
+        const unsigned int* found_starts,
+        const unsigned int* found_ends,
+        const unsigned int* track_starts,
+        const unsigned int* track_ends,
+        const float* track_values,
+        const int n_tracks,
+        const int batch_size,
+        const int sequence_length,
+        const int max_number_intervals,
+        const int window_size,
+        const float default_value,
+        const bool default_value_isnan,
+        const float* scaling_factors,
+        float* out
+) {
+    intervals_to_values_kernel<float>(
+        query_starts, query_ends, found_starts, found_ends,
+        track_starts, track_ends, track_values,
+        n_tracks, batch_size, sequence_length, max_number_intervals,
+        window_size, default_value, default_value_isnan,
+        scaling_factors, out
+    );
+}
+
+extern "C" __global__
+void intervals_to_values_bfloat16(
+        const unsigned int* query_starts,
+        const unsigned int* query_ends,
+        const unsigned int* found_starts,
+        const unsigned int* found_ends,
+        const unsigned int* track_starts,
+        const unsigned int* track_ends,
+        const float* track_values,
+        const int n_tracks,
+        const int batch_size,
+        const int sequence_length,
+        const int max_number_intervals,
+        const int window_size,
+        const float default_value,
+        const bool default_value_isnan,
+        const float* scaling_factors,
+        __nv_bfloat16* out
+) {
+    intervals_to_values_kernel<__nv_bfloat16>(
+        query_starts, query_ends, found_starts, found_ends,
+        track_starts, track_ends, track_values,
+        n_tracks, batch_size, sequence_length, max_number_intervals,
+        window_size, default_value, default_value_isnan,
+        scaling_factors, out
+    );
+}
diff --git a/pixi.lock b/pixi.lock
new file mode 100644
index 0000000..c69d9da
--- /dev/null
+++ b/pixi.lock
@@ -0,0 +1,5713 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/rapidsai/
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/nvidia/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/dataloading/
+    indexes:
+    - https://pypi.org/simple
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.10.5-hbd8a1cb_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.44-h1aa0949_5.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-38_h4a7cf45_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-38_h0358290_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h9ec8514_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-h767d61c_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-hcd61629_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-h767d61c_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-38_h47877c9_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-devel-5.8.1-hb9d3cd8_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hb9d3cd8_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.30-pthreads_h94d23a6_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.51.0-hee844dc_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h8f9b012_7.conda
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+  sha256: 4c52a6e78aef5cf47a98ef8e934755abf53953379b7d53e68b15ff4420e6683d
+  requires_dist:
+  - idna>=2.0
+  - multidict>=4.0
+  - propcache>=0.2.1
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/c1/da/8da9f6a53f67b5106ffe902c6fa0164e10398d4e150d85838b82f424072a/yarl-1.22.0-cp311-cp311-macosx_11_0_arm64.whl
+  name: yarl
+  version: 1.22.0
+  sha256: 792a2af6d58177ef7c19cbf0097aba92ca1b9cb3ffdd9c7470e156c8f9b5e028
+  requires_dist:
+  - idna>=2.0
+  - multidict>=4.0
+  - propcache>=0.2.1
+  requires_python: '>=3.9'
+- conda: https://conda.anaconda.org/conda-forge/noarch/zarr-3.1.3-pyhcf101f3_0.conda
+  sha256: 3d494f058f1e8aef41c5fac202c45de26fdc7b169ed886522497a8b83615e8db
+  md5: 540ed093cbc10711485a385a58f34b00
+  depends:
+  - python >=3.11
+  - packaging >=22.0
+  - numpy >=1.26
+  - numcodecs >=0.14
+  - typing_extensions >=4.9
+  - donfig >=0.8
+  - crc32c
+  - python
+  constrains:
+  - fsspec >=2023.10.0
+  - obstore >=0.5.1
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/zarr
+  size: 290974
+  timestamp: 1758255670714
+- conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhd8ed1ab_0.conda
+  sha256: 7560d21e1b021fd40b65bfb72f67945a3fcb83d78ad7ccf37b8b3165ec3b68ad
+  md5: df5e78d904988eb55042c0c97446079f
+  depends:
+  - python >=3.9
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/zipp
+  size: 22963
+  timestamp: 1749421737203
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda
+  sha256: a4166e3d8ff4e35932510aaff7aa90772f84b4d07e9f6f83c614cba7ceefe0eb
+  md5: 6432cb5d4ac0046c3ac0a8a0f95842f9
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libstdcxx >=13
+  - libzlib >=1.3.1,<2.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/zstd
+  size: 567578
+  timestamp: 1742433379869
diff --git a/pixi.toml b/pixi.toml
new file mode 100644
index 0000000..6f3dee9
--- /dev/null
+++ b/pixi.toml
@@ -0,0 +1,65 @@
+[workspace]
+channels = ["rapidsai", "conda-forge", "nvidia", "bioconda", "dataloading"]
+name = "nucleotides-diffusion"
+platforms = ["linux-64", "osx-arm64"]
+version = "0.1.0"
+conda-pypi-map = { "conda-forge" = "conda-forge-pypi-map.json" }
+
+
+[tasks]
+download-example-data = { cmd = "python -m bigwig_loader.download_example_data"}
+test = { cmd = "pytest -vv" }
+pre-commit-hooks = { cmd = "pre-commit run --all-files --show-diff-on-failure" }
+
+[feature.gpu.system-requirements]
+cuda = "12"
+
+[feature.gpu.target.linux-64.dependencies]
+cuda-version = "12.8.*"
+pytorch-gpu = ">=2.6"
+cuda-nvcc = "*"
+kvikio = "<=25.08.00"
+
+[feature.released.target.linux-64.dependencies]
+bigwig-loader = "*"
+
+[feature.dev.target.linux-64.pypi-dependencies]
+bigwig-loader = { path = ".", editable = true }
+
+[feature.cpu.target.osx-arm64.dependencies]
+pytorch-cpu = "*"
+
+[feature.cpu.target.linux-64.dependencies]
+pytorch-cpu = "*"
+
+[dependencies]
+python = "==3.11"
+pip = "*"
+numpy = "*"
+pandas = "*"
+
+[pypi-dependencies]
+python-dotenv = "*"
+pydantic = "*"
+pydantic-settings = "*"
+universal-pathlib = "*"
+fsspec = { version = "*" }
+s3fs = "*"
+pyfaidx = "*"
+numcodecs ="*"
+
+[feature.test.pypi-dependencies]
+pytest = "*"
+pytest-sugar = "*"
+pytest-cov = "*"
+pytest-mock = "*"
+mypy = "*"
+aim = "==3.28.0"
+pytest-asyncio = "*"
+asgi-lifespan = "*"
+pyBigWig = "*"
+
+[environments]
+prod = {features = ["gpu", "released"]}
+dev = {features = ["gpu", "dev", "test"]}
+dev-cpu = {features = ["cpu", "test"] }
diff --git a/tests/conftest.py b/tests/conftest.py
index 539d1af..65b1501 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,8 +1,10 @@
 import logging
 import random
 
+import cupy as cp
 import pandas as pd
 import pytest
+import torch
 
 from bigwig_loader import config
 from bigwig_loader.download_example_data import get_example_bigwigs_files
@@ -62,3 +64,53 @@ def example_intervals(collection) -> tuple[list[str], list[int], list[int]]:
     start = [start[i] for i in shuffle_index]
     end = [end[i] for i in shuffle_index]
     return chrom, start, end
+
+
+def all_close(expected, values, dtype):
+    """Compare arrays/tensors with dtype-appropriate tolerances.
+
+    For bfloat16, uses looser tolerances due to lower precision.
+    For float32, uses standard tolerances.
+    """
+    if dtype == "float32":
+        return cp.allclose(expected, values, equal_nan=True)
+    elif dtype == "bfloat16":
+        expected = convert_to_torch_bfloat16(expected)
+        values = convert_to_torch_bfloat16(values)
+        return torch.allclose(expected, values, rtol=1e-2, atol=1e-2, equal_nan=True)
+    raise ValueError(f"Unsupported data type: {dtype}")
+
+
+def convert_to_torch_bfloat16(tensor):
+    """Convert a CuPy array to PyTorch bfloat16 tensor.
+
+    For uint16 tensors (bfloat16 bits), reinterprets bits as bfloat16.
+    For float32 tensors, converts to bfloat16.
+    """
+
+    torch_tensor = torch.as_tensor(tensor)
+
+    if tensor.dtype == cp.uint16:
+        # Reinterpret uint16 bits as bfloat16 using view
+        # This treats the uint16 bit pattern as if it were bfloat16
+        torch_tensor = torch_tensor.view(torch.bfloat16)
+    elif tensor.dtype == cp.float32:
+        # Convert float32 to bfloat16
+        torch_tensor = torch_tensor.to(torch.bfloat16)
+    else:
+        # Convert other types to float32 first, then to bfloat16
+        torch_tensor = torch_tensor.to(torch.float32).to(torch.bfloat16)
+    return torch_tensor
+
+
+def uint16_to_float32(tensor):
+    """Convert uint16 array (containing bfloat16 bits) to float32 values.
+
+    This interprets the uint16 bit pattern as bfloat16, then converts to float32.
+    Keeps data on GPU throughout the operation.
+    """
+    torch_tensor = convert_to_torch_bfloat16(tensor)
+    # Convert to float32 to get actual values (stays on GPU)
+    float32_tensor = torch_tensor.to(torch.float32)
+    # Convert back to CuPy (stays on GPU)
+    return cp.asarray(float32_tensor)
diff --git a/tests/test_against_pybigwig.py b/tests/test_against_pybigwig.py
index a17307b..d5d4e50 100644
--- a/tests/test_against_pybigwig.py
+++ b/tests/test_against_pybigwig.py
@@ -40,6 +40,8 @@ def test_same_output(bigwig_path):
 
     pybigwig_batch = pybigwig_collection.get_batch(chromosomes, starts, ends)
     np.nan_to_num(pybigwig_batch, copy=False, nan=0.0)
+    pybigwig_batch = pybigwig_batch.transpose(0, 2, 1)
+
     this_batch = collection.get_batch(chromosomes, starts, ends).get()
     print("PyBigWig:")
     print(pybigwig_batch)
@@ -64,7 +66,9 @@ def test_same_output_with_nans(bigwig_path):
         list(df["center"] + 1000),
     )
 
-    pybigwig_batch = pybigwig_collection.get_batch(chromosomes, starts, ends)
+    pybigwig_batch = pybigwig_collection.get_batch(chromosomes, starts, ends).transpose(
+        0, 2, 1
+    )
 
     this_batch = collection.get_batch(
         chromosomes, starts, ends, default_value=np.nan
@@ -116,6 +120,8 @@ def test_windowed_output_against_pybigwig(
     # And take mean over the window
     pybigwig_batch = np.nanmean(pybigwig_batch, axis=-1)
 
+    pybigwig_batch = pybigwig_batch.transpose(0, 2, 1)
+
     print("PyBigWig (with window function applied afterwards):")
     print(pybigwig_batch)
     print("bigwig-loader:")
diff --git a/tests/test_collection.py b/tests/test_collection.py
index 1e9893e..ab7ff44 100644
--- a/tests/test_collection.py
+++ b/tests/test_collection.py
@@ -43,7 +43,7 @@ def bigwig_dataset(bigwig_path, reference_genome_path, merged_intervals):
 
 def test_dataset(bigwig_dataset):
     for sequence, target in bigwig_dataset:
-        assert target.shape == (256, 2, 1000)
+        assert target.shape == (256, 1000, 2)
 
 
 def test_get_batch(collection):
@@ -54,7 +54,7 @@ def test_get_batch(collection):
         intervals["start"].values,
         intervals["end"].values,
     )
-    assert batch.shape == (256, n_files, 1000)
+    assert batch.shape == (256, 1000, n_files)
 
 
 def test_get_batch_with_nans(collection):
@@ -76,7 +76,7 @@ def test_get_batch_with_nans(collection):
     )
 
     print(batch)
-    assert batch.shape == (3, n_files, 1000)
+    assert batch.shape == (3, 1000, n_files)
     assert cp.any(cp.isnan(batch))
 
 
@@ -144,5 +144,5 @@ def test_scaling(bigwig_path, reference_genome_path):
     )
 
     assert cp.allclose(
-        scaled_batch, unscaled_batch * cp.asarray([3, 10]).reshape(1, 2, 1)
+        scaled_batch, unscaled_batch * cp.asarray([3, 10]).reshape(1, 1, 2)
     )
diff --git a/tests/test_dataset.py b/tests/test_dataset.py
index dd30fc1..2398288 100644
--- a/tests/test_dataset.py
+++ b/tests/test_dataset.py
@@ -41,7 +41,8 @@ def dataset_with_track_sampling(bigwig_path, reference_genome_path, merged_inter
 def test_output_shape(dataset):
     for i, (sequence, values) in enumerate(dataset):
         print(i, "---", flush=True)
-        assert values.shape == (265, 2, 250)
+        assert values.shape == (265, 250, 2)
+        assert sequence.shape == (265, 2000, 4)
 
 
 def test_output_shape_sub_sampled_tracks(dataset_with_track_sampling):
@@ -50,7 +51,7 @@ def test_output_shape_sub_sampled_tracks(dataset_with_track_sampling):
     ):
         print(i, "---", flush=True)
         assert len(track_indices) == 1
-        assert values.shape == (265, 1, 250)
+        assert values.shape == (265, 250, 1)
 
 
 def test_batch_return_type(bigwig_path, reference_genome_path, merged_intervals):
diff --git a/tests/test_intervals_to_values.py b/tests/test_intervals_to_values.py
index 8098b33..661c5db 100644
--- a/tests/test_intervals_to_values.py
+++ b/tests/test_intervals_to_values.py
@@ -1,5 +1,6 @@
 import cupy as cp
 import pytest
+from conftest import all_close
 
 from bigwig_loader.intervals_to_values import intervals_to_values
 
@@ -29,7 +30,7 @@ def test_get_values_from_intervals(default_value) -> None:
     track_values = cp.asarray([20.0, 15.0, 30.0, 40.0, 50.0], dtype=cp.dtype("f4"))
     query_starts = cp.asarray([2], dtype=cp.int32)
     query_ends = cp.asarray([17], dtype=cp.int32)
-    reserved = cp.zeros((1, 15), dtype=cp.float32)
+    reserved = cp.zeros((1, 15, 1), dtype=cp.float32)
     values = intervals_to_values(
         track_starts,
         track_ends,
@@ -41,7 +42,12 @@ def test_get_values_from_intervals(default_value) -> None:
     )
     expected = cp.asarray(
         [[20, 15, 15, 15, 15, 15, 15, 15, 30, 30, 40, 40, 40, 40, 50]]
-    )
+    ).transpose(1, 0)
+    print("expected", expected.shape)
+    print(expected)
+    print("actual", values.shape)
+    print(values)
+
     assert cp.allclose(expected, values, equal_nan=True)
 
 
@@ -64,14 +70,16 @@ def test_get_values_from_intervals_edge_case_1(default_value) -> None:
         out=reserved,
     )
     x = default_value
-    expected = cp.asarray([[x, x, x, x, 30, 30, 40, 40, 40, 40, 50, 50]])
+    expected = cp.asarray([[[x, x, x, x, 30, 30, 40, 40, 40, 40, 50, 50]]]).transpose(
+        0, 2, 1
+    )
 
     print(expected)
     print(values)
 
     assert (
         cp.allclose(values, expected, equal_nan=True)
-        and expected.shape[-1] == query_ends[0] - query_starts[0]
+        and expected.shape[1] == query_ends[0] - query_starts[0]
     )
 
 
@@ -93,10 +101,10 @@ def test_get_values_from_intervals_edge_case_2(default_value) -> None:
         default_value=default_value,
         out=reserved,
     )
-    expected = cp.asarray([[30, 30, 40, 40, 40, 40, 50, 50]])
+    expected = cp.asarray([[[30, 30, 40, 40, 40, 40, 50, 50]]]).transpose(0, 2, 1)
     assert (
         cp.allclose(values, expected, equal_nan=True)
-        and expected.shape[-1] == query_ends[0] - query_starts[0]
+        and expected.shape[1] == query_ends[0] - query_starts[0]
     )
 
 
@@ -119,10 +127,10 @@ def test_get_values_from_intervals_edge_case_3(default_value) -> None:
         out=reserved,
     )
     x = default_value
-    expected = cp.asarray([[20, 30, 30, 40, 40, x, x]])
+    expected = cp.asarray([[[20, 30, 30, 40, 40, x, x]]]).transpose(0, 2, 1)
     assert (
         cp.allclose(values, expected, equal_nan=True)
-        and expected.shape[-1] == query_ends[0] - query_starts[0]
+        and expected.shape[1] == query_ends[0] - query_starts[0]
     )
 
 
@@ -144,12 +152,12 @@ def test_get_values_from_intervals_edge_case_4(default_value) -> None:
         default_value=default_value,
         out=reserved,
     )
-    expected = cp.asarray([[20, 30, 30, 40, 40]])
+    expected = cp.asarray([[[20, 30, 30, 40, 40]]]).transpose(0, 2, 1)
 
     print(expected)
     print(values)
 
-    assert (values == expected).all() and expected.shape[-1] == query_ends[
+    assert (values == expected).all() and expected.shape[1] == query_ends[
         0
     ] - query_starts[0]
 
@@ -173,14 +181,16 @@ def test_get_values_from_intervals_edge_case_5(default_value) -> None:
         out=reserved,
     )
     x = default_value
-    expected = cp.asarray([[20, 30, 30, 40, 40, x, x, x, x, 50, 50]])
+    expected = cp.asarray([[[20, 30, 30, 40, 40, x, x, x, x, 50, 50]]]).transpose(
+        0, 2, 1
+    )
 
     print(expected)
     print(values)
 
     assert (
         cp.allclose(expected, values, equal_nan=True)
-        and expected.shape[-1] == query_ends[0] - query_starts[0]
+        and expected.shape[1] == query_ends[0] - query_starts[0]
     )
 
 
@@ -207,17 +217,23 @@ def test_get_values_from_intervals_batch_of_2(default_value) -> None:
 
     expected = cp.asarray(
         [
-            [20.0, 20.0, 20.0, 30.0, 30.0, 40.0, 40.0, x, x, x, x],
-            [20.0, 30.0, 30.0, 40.0, 40.0, x, x, x, x, 50.0, 50.0],
+            [
+                [20.0, 20.0, 20.0, 30.0, 30.0, 40.0, 40.0, x, x, x, x],
+                [20.0, 30.0, 30.0, 40.0, 40.0, x, x, x, x, 50.0, 50.0],
+            ]
         ]
-    )
+    ).transpose(1, 2, 0)
+
+    print("expected", expected.shape)
     print(expected)
-    print(values)
+    print("actual", values.shape)
+    print(values, values.shape)
     assert cp.allclose(expected, values, equal_nan=True)
 
 
+@pytest.mark.parametrize("dtype", ["bfloat16", "float32"])
 @pytest.mark.parametrize("default_value", [0.0, cp.nan])
-def test_get_values_from_intervals_batch_multiple_tracks(default_value) -> None:
+def test_get_values_from_intervals_batch_multiple_tracks(dtype, default_value) -> None:
     """Query end is exactly at end index before "gap"."""
     track_starts = cp.asarray(
         [5, 10, 12, 18, 8, 9, 10, 18, 25, 10, 100, 1000], dtype=cp.int32
@@ -231,7 +247,8 @@ def test_get_values_from_intervals_batch_multiple_tracks(default_value) -> None:
     )
     query_starts = cp.asarray([7, 9, 20, 99], dtype=cp.int32)
     query_ends = cp.asarray([18, 20, 31, 110], dtype=cp.int32)
-    reserved = cp.zeros([3, 4, 11], dtype=cp.dtype("<f4"))
+    # reserved = cp.zeros([3, 4, 11], dtype=cp.dtype("<f4"))
+    # reserved = cp.zeros([4, 11, 3], dtype=cp.dtype("<f4"))
     values = intervals_to_values(
         track_starts,
         track_ends,
@@ -239,8 +256,9 @@ def test_get_values_from_intervals_batch_multiple_tracks(default_value) -> None:
         query_starts,
         query_ends,
         default_value=default_value,
-        out=reserved,
+        # out=reserved,
         sizes=cp.asarray([4, 5, 3], dtype=cp.int32),
+        dtype=dtype,
     )
     x = default_value
     expected = cp.asarray(
@@ -289,6 +307,11 @@ def test_get_values_from_intervals_batch_multiple_tracks(default_value) -> None:
             ],
         ]
     )
+
+    expected = expected.transpose(1, 2, 0)
+
+    print("Expected:", expected.shape)
     print(expected)
+    print("Actual Values:", values.shape)
     print(values)
-    assert cp.allclose(expected, values, equal_nan=True)
+    assert all_close(expected, values, dtype)
diff --git a/tests/test_intervals_to_values_window_function.py b/tests/test_intervals_to_values_window_function.py
index 4020fdc..f964190 100644
--- a/tests/test_intervals_to_values_window_function.py
+++ b/tests/test_intervals_to_values_window_function.py
@@ -4,19 +4,62 @@
 import cupy as cp
 import numpy as np
 import pytest
+from conftest import all_close
 
 from bigwig_loader.intervals_to_values import intervals_to_values
 
 
+def reshape_expected(
+    expected: cp.ndarray, batch_size: int, n_tracks: int
+) -> cp.ndarray:
+    """
+    Reshape expected output to match new shape (batch_size, sequence_length, n_tracks).
+
+    Handles cases where expected might be:
+    - 1D: sequence_length → reshape to (batch_size, sequence_length, n_tracks)
+    - 2D: (batch_size, sequence_length) or (n_tracks, sequence_length) → reshape appropriately
+    - 3D: already correct or needs transposing
+    """
+    if expected.ndim == 1:
+        # 1D array: expand to (batch_size, sequence_length, n_tracks)
+        seq_length = len(expected)
+        expected = expected.reshape(1, seq_length, 1)
+        if batch_size > 1 or n_tracks > 1:
+            expected = cp.broadcast_to(expected, (batch_size, seq_length, n_tracks))
+    elif expected.ndim == 2:
+        # 2D array: assume (batch_size, sequence_length), add track dimension
+        expected = expected.reshape(expected.shape[0], expected.shape[1], 1)
+        if n_tracks > 1:
+            expected = cp.broadcast_to(
+                expected, (batch_size, expected.shape[1], n_tracks)
+            )
+    elif expected.ndim == 3:
+        # Already 3D: check if it needs transposing or reshaping
+        # Case 1: shape is (n_tracks, batch_size, sequence_length), transpose to (batch_size, sequence_length, n_tracks)
+        if expected.shape[0] == n_tracks and expected.shape[1] == batch_size:
+            expected = expected.transpose(1, 2, 0)
+        # Case 2: shape is (batch_size, 1, sequence_length), reshape to (batch_size, sequence_length, n_tracks)
+        elif expected.shape[0] == batch_size and expected.shape[1] == 1:
+            seq_length = expected.shape[2]
+            expected = expected.reshape(batch_size, seq_length, 1)
+            if n_tracks > 1:
+                expected = cp.broadcast_to(expected, (batch_size, seq_length, n_tracks))
+
+    return expected
+
+
+@pytest.mark.parametrize("dtype", ["float32", "bfloat16"])
 @pytest.mark.parametrize("default_value", [0.0, cp.nan])
-def test_get_values_from_intervals_window(default_value) -> None:
+def test_get_values_from_intervals_window(default_value, dtype) -> None:
     """."""
     track_starts = cp.asarray([1, 3, 10, 12, 16], dtype=cp.int32)
     track_ends = cp.asarray([3, 10, 12, 16, 20], dtype=cp.int32)
     track_values = cp.asarray([20.0, 15.0, 30.0, 40.0, 50.0], dtype=cp.dtype("f4"))
     query_starts = cp.asarray([2], dtype=cp.int32)
     query_ends = cp.asarray([17], dtype=cp.int32)
-    reserved = cp.zeros((1, 3), dtype=cp.float32)
+    batch_size = len(query_starts)
+    n_tracks = 1
+    reserved = cp.zeros((batch_size, 3, n_tracks), dtype=cp.float32)
     values = intervals_to_values(
         track_starts,
         track_ends,
@@ -26,26 +69,30 @@ def test_get_values_from_intervals_window(default_value) -> None:
         window_size=5,
         default_value=default_value,
         out=reserved,
+        dtype=dtype,
     )
 
     expected = cp.asarray([[16.0, 21.0, 42.0]])
+    expected = reshape_expected(expected, batch_size, n_tracks)
 
     print("expected:")
     print(expected)
     print("actual:")
     print(values)
-    assert (values == expected).all()
+    assert all_close(values, expected, dtype)
 
 
-@pytest.mark.parametrize("default_value", [0.0, cp.nan, 5.6, 10.0, 7565])
-def test_get_values_from_intervals_edge_case_1(default_value) -> None:
+@pytest.mark.parametrize("dtype", ["float32", "bfloat16"])
+@pytest.mark.parametrize("default_value", [0.0, cp.nan])
+def test_get_values_from_intervals_edge_case_1(default_value, dtype) -> None:
     """Query start is somewhere in a "gap"."""
     track_starts = cp.asarray([1, 10, 12, 16], dtype=cp.int32)
     track_ends = cp.asarray([3, 12, 16, 20], dtype=cp.int32)
     track_values = cp.asarray([20.0, 30.0, 40.0, 50.0], dtype=cp.dtype("f4"))
     query_starts = cp.asarray([6], dtype=cp.int32)
     query_ends = cp.asarray([18], dtype=cp.int32)
-    # reserved = cp.zeros((1, 4), dtype=cp.dtype("<f4"))
+    batch_size = len(query_starts)
+    n_tracks = 1
     values = intervals_to_values(
         track_starts,
         track_ends,
@@ -54,6 +101,7 @@ def test_get_values_from_intervals_edge_case_1(default_value) -> None:
         query_ends,
         default_value=default_value,
         window_size=3,
+        dtype=dtype,
     )
     x = default_value
     if isnan(default_value):
@@ -63,26 +111,27 @@ def test_get_values_from_intervals_edge_case_1(default_value) -> None:
     else:
         expected = cp.asarray([[x, 20.0, 40.0, 46.666668]])
 
+    expected = reshape_expected(expected, batch_size, n_tracks)
     print("expected:")
     print(expected)
     print("actual:")
     print(values)
 
-    assert (
-        cp.allclose(values, expected, equal_nan=True)
-        and expected.shape[-1] == (query_ends[0] - query_starts[0]) / 3
-    )
+    assert all_close(values, expected, dtype)
 
 
+@pytest.mark.parametrize("dtype", ["float32", "bfloat16"])
 @pytest.mark.parametrize("default_value", [0.0, cp.nan])
-def test_get_values_from_intervals_edge_case_2(default_value) -> None:
+def test_get_values_from_intervals_edge_case_2(default_value, dtype) -> None:
     """Query start is exactly at start index after "gap"."""
     track_starts = cp.asarray([1, 10, 12, 16], dtype=cp.int32)
     track_ends = cp.asarray([3, 12, 16, 20], dtype=cp.int32)
     track_values = cp.asarray([20.0, 30.0, 40.0, 50.0], dtype=cp.dtype("f4"))
     query_starts = cp.asarray([10], dtype=cp.int32)
     query_ends = cp.asarray([18], dtype=cp.int32)
-    reserved = cp.zeros((1, 2), dtype=cp.dtype("<f4"))
+    batch_size = len(query_starts)
+    n_tracks = 1
+    reserved = cp.zeros((batch_size, 2, n_tracks), dtype=cp.dtype("<f4"))
     values = intervals_to_values(
         track_starts,
         track_ends,
@@ -92,25 +141,30 @@ def test_get_values_from_intervals_edge_case_2(default_value) -> None:
         window_size=4,
         default_value=default_value,
         out=reserved,
+        dtype=dtype,
     )
     expected = cp.asarray([[35.0, 45.0]])
+    expected = reshape_expected(expected, batch_size, n_tracks)
     print(expected)
     print(values)
     assert (
-        cp.allclose(values, expected, equal_nan=True)
-        and expected.shape[-1] == (query_ends[0] - query_starts[0]) / 4
+        all_close(values, expected, dtype)
+        and expected.shape[1] == (query_ends[0] - query_starts[0]) / 4
     )
 
 
+@pytest.mark.parametrize("dtype", ["float32", "bfloat16"])
 @pytest.mark.parametrize("default_value", [0.0, cp.nan])
-def test_get_values_from_intervals_edge_case_3(default_value) -> None:
+def test_get_values_from_intervals_edge_case_3(default_value, dtype) -> None:
     """Query end is somewhere in a "gap"."""
     track_starts = cp.asarray([5, 10, 12, 18], dtype=cp.int32)
     track_ends = cp.asarray([10, 12, 14, 20], dtype=cp.int32)
     track_values = cp.asarray([20.0, 30.0, 40.0, 50.0], dtype=cp.dtype("f4"))
     query_starts = cp.asarray([8], dtype=cp.int32)
     query_ends = cp.asarray([16], dtype=cp.int32)
-    reserved = cp.zeros((1, 2), dtype=cp.dtype("<f4"))
+    batch_size = len(query_starts)
+    n_tracks = 1
+    reserved = cp.zeros((batch_size, 2, n_tracks), dtype=cp.dtype("<f4"))
     values = intervals_to_values(
         track_starts,
         track_ends,
@@ -120,29 +174,30 @@ def test_get_values_from_intervals_edge_case_3(default_value) -> None:
         window_size=4,
         default_value=default_value,
         out=reserved,
+        dtype=dtype,
     )
-    # expected = cp.asarray([[20, 20, 30, 30, 40, 40, 0, 0]])
     if isnan(default_value):
         expected = cp.asarray([[25.0, 40.0]])
     else:
         expected = cp.asarray([[25.0, 20.0]])
+    expected = reshape_expected(expected, batch_size, n_tracks)
     print(expected)
     print(values)
-    assert (
-        cp.allclose(expected, values, equal_nan=True)
-        and expected.shape[-1] == (query_ends[0] - query_starts[0]) / 4
-    )
+    assert all_close(expected, values, dtype)
 
 
+@pytest.mark.parametrize("dtype", ["float32", "bfloat16"])
 @pytest.mark.parametrize("default_value", [0.0, cp.nan])
-def test_get_values_from_intervals_edge_case_4(default_value) -> None:
+def test_get_values_from_intervals_edge_case_4(default_value, dtype) -> None:
     """Query end is exactly at end index before "gap"."""
     track_starts = cp.asarray([5, 10, 12, 18], dtype=cp.int32)
     track_ends = cp.asarray([10, 12, 14, 20], dtype=cp.int32)
     track_values = cp.asarray([20.0, 30.0, 40.0, 50.0], dtype=cp.dtype("f4"))
     query_starts = cp.asarray([8], dtype=cp.int32)
     query_ends = cp.asarray([14], dtype=cp.int32)
-    reserved = cp.zeros((1, 2), dtype=cp.dtype("<f4"))
+    batch_size = len(query_starts)
+    n_tracks = 1
+    reserved = cp.zeros((batch_size, 2, n_tracks), dtype=cp.dtype("<f4"))
     values = intervals_to_values(
         track_starts,
         track_ends,
@@ -152,28 +207,29 @@ def test_get_values_from_intervals_edge_case_4(default_value) -> None:
         window_size=3,
         default_value=default_value,
         out=reserved,
+        dtype=dtype,
     )
-    # without window function:[[20, 20, 30, 30, 40, 40]]
     expected = cp.asarray([[23.333334, 36.666668]])
+    expected = reshape_expected(expected, batch_size, n_tracks)
 
     print(expected)
     print(values)
 
-    assert (
-        cp.allclose(values, expected, equal_nan=True)
-        and expected.shape[-1] == (query_ends[0] - query_starts[0]) / 3
-    )
+    assert all_close(values, expected, dtype)
 
 
+@pytest.mark.parametrize("dtype", ["float32", "bfloat16"])
 @pytest.mark.parametrize("default_value", [0.0, cp.nan])
-def test_get_values_from_intervals_edge_case_5(default_value) -> None:
+def test_get_values_from_intervals_edge_case_5(default_value, dtype) -> None:
     """Query end is exactly at end index before "gap"."""
     track_starts = cp.asarray([5, 10, 12, 18], dtype=cp.uint32)
     track_ends = cp.asarray([10, 12, 14, 20], dtype=cp.uint32)
     track_values = cp.asarray([20.0, 30.0, 40.0, 50.0], dtype=cp.dtype("f4"))
     query_starts = cp.asarray([8], dtype=cp.uint32)
     query_ends = cp.asarray([20], dtype=cp.uint32)
-    reserved = cp.zeros((1, 4), dtype=cp.dtype("<f4"))
+    batch_size = len(query_starts)
+    n_tracks = 1
+    reserved = cp.zeros((batch_size, 4, n_tracks), dtype=cp.dtype("<f4"))
     values = intervals_to_values(
         track_starts,
         track_ends,
@@ -183,6 +239,7 @@ def test_get_values_from_intervals_edge_case_5(default_value) -> None:
         window_size=3,
         default_value=default_value,
         out=reserved,
+        dtype=dtype,
     )
     x = default_value
     if isnan(default_value):
@@ -190,24 +247,25 @@ def test_get_values_from_intervals_edge_case_5(default_value) -> None:
     else:
         expected = cp.asarray([[23.333334, 36.666668, x, 33.333332]])
 
+    expected = reshape_expected(expected, batch_size, n_tracks)
     print(expected)
     print(values)
 
-    assert (
-        cp.allclose(values, expected, equal_nan=True)
-        and expected.shape[-1] == (query_ends[0] - query_starts[0]) / 3
-    )
+    assert all_close(values, expected, dtype)
 
 
+@pytest.mark.parametrize("dtype", ["float32", "bfloat16"])
 @pytest.mark.parametrize("default_value", [0.0, cp.nan])
-def test_get_values_from_intervals_batch_of_2(default_value) -> None:
+def test_get_values_from_intervals_batch_of_2(default_value, dtype) -> None:
     """Query end is exactly at end index before "gap"."""
     track_starts = cp.asarray([5, 10, 12, 18], dtype=cp.int32)
     track_ends = cp.asarray([10, 12, 14, 20], dtype=cp.int32)
     track_values = cp.asarray([20.0, 30.0, 40.0, 50.0], dtype=cp.dtype("f4"))
     query_starts = cp.asarray([6, 8], dtype=cp.int32)
     query_ends = cp.asarray([18, 20], dtype=cp.int32)
-    reserved = cp.zeros([2, 4], dtype=cp.dtype("<f4"))
+    batch_size = len(query_starts)
+    n_tracks = 1
+    reserved = cp.zeros([batch_size, 4, n_tracks], dtype=cp.dtype("<f4"))
     values = intervals_to_values(
         track_starts,
         track_ends,
@@ -217,28 +275,32 @@ def test_get_values_from_intervals_batch_of_2(default_value) -> None:
         window_size=3,
         default_value=default_value,
         out=reserved,
+        dtype=dtype,
     )
-    # expected without window function:
-    #    [20.0, 20.0, 20.0, 20.0, 30.0, 30.0, 40.0, 40.0, 0.0, 0.0, 0.0, 0.0]
-    #    [20.0, 20.0, 30.0, 30.0, 40.0, 40.0, 0.0, 0.0, 0.0, 0.0, 50.0, 50.0]
 
     if isnan(default_value):
         expected = cp.asarray(
-            [[20.0, 26.666666, 40.0, cp.nan], [23.333334, 36.666668, cp.nan, 50.0]]
+            [[[20.0, 26.666666, 40.0, cp.nan]], [[23.333334, 36.666668, cp.nan, 50.0]]]
         )
     else:
         expected = cp.asarray(
-            [[20.0, 26.666666, 26.666666, 0.0], [23.333334, 36.666668, 0.0, 33.333332]]
+            [
+                [[20.0, 26.666666, 26.666666, 0.0]],
+                [[23.333334, 36.666668, 0.0, 33.333332]],
+            ]
         )
+
+    expected = reshape_expected(expected, batch_size, n_tracks)
     print("expected:")
     print(expected)
     print("actual:")
     print(values)
-    assert cp.allclose(values, expected, equal_nan=True)
+    assert all_close(values, expected, dtype)
 
 
+@pytest.mark.parametrize("dtype", ["float32", "bfloat16"])
 @pytest.mark.parametrize("default_value", [0.0, cp.nan])
-def test_one_track_one_sample(default_value) -> None:
+def test_one_track_one_sample(default_value, dtype) -> None:
     """
     This tests a specific combination of track and batch index
     of the larger test case below:
@@ -255,7 +317,9 @@ def test_one_track_one_sample(default_value) -> None:
     )
     query_starts = cp.asarray([9], dtype=cp.int32)
     query_ends = cp.asarray([20], dtype=cp.int32)
-    reserved = cp.zeros([1, 1, 3], dtype=cp.dtype("<f4"))
+    batch_size = len(query_starts)
+    n_tracks = 1
+    reserved = cp.zeros([batch_size, 3, n_tracks], dtype=cp.dtype("<f4"))
     values = intervals_to_values(
         track_starts,
         track_ends,
@@ -266,6 +330,7 @@ def test_one_track_one_sample(default_value) -> None:
         window_size=3,
         default_value=default_value,
         out=reserved,
+        dtype=dtype,
     )
 
     x = default_value
@@ -288,16 +353,18 @@ def apply_window(full_matrix):
         )
 
     expected = apply_window(expected)
+    expected = reshape_expected(expected, batch_size, n_tracks)
 
     print("expected:")
     print(expected)
     print("actual:")
     print(values)
-    assert cp.allclose(values, expected, equal_nan=True)
+    assert all_close(values, expected, dtype)
 
 
+@pytest.mark.parametrize("dtype", ["float32", "bfloat16"])
 @pytest.mark.parametrize("default_value", [0.0, cp.nan])
-def test_one_track_one_sample_2(default_value) -> None:
+def test_one_track_one_sample_2(default_value, dtype) -> None:
     """
     This tests a specific combination of track and batch index
     of the larger test case below:
@@ -314,7 +381,9 @@ def test_one_track_one_sample_2(default_value) -> None:
     )
     query_starts = cp.asarray([9], dtype=cp.int32)
     query_ends = cp.asarray([20], dtype=cp.int32)
-    reserved = cp.zeros([1, 1, 3], dtype=cp.dtype("<f4"))
+    batch_size = len(query_starts)
+    n_tracks = 1
+    reserved = cp.zeros([batch_size, 3, n_tracks], dtype=cp.dtype("<f4"))
     values = intervals_to_values(
         track_starts,
         track_ends,
@@ -325,6 +394,7 @@ def test_one_track_one_sample_2(default_value) -> None:
         window_size=3,
         default_value=default_value,
         out=reserved,
+        dtype=dtype,
     )
     x = default_value
     expected = cp.asarray(
@@ -358,6 +428,7 @@ def apply_window(full_matrix):
         )
 
     expected = apply_window(expected)
+    expected = reshape_expected(expected, batch_size, n_tracks)
 
     print("expected:")
     print(expected)
@@ -365,11 +436,12 @@ def apply_window(full_matrix):
     print(values)
     print("difference")
     print(values - expected)
-    assert cp.allclose(values, expected, atol=1e-2, rtol=1e-2, equal_nan=True)
+    assert all_close(values, expected, dtype)
 
 
+@pytest.mark.parametrize("dtype", ["float32", "bfloat16"])
 @pytest.mark.parametrize("default_value", [0.0, cp.nan])
-def test_get_values_from_intervals_multiple_tracks(default_value) -> None:
+def test_get_values_from_intervals_multiple_tracks(default_value, dtype) -> None:
     """Test interval_to_values with 3 tracks and a batch size of 1."""
     track_starts = cp.asarray(
         [5, 10, 12, 18, 8, 9, 10, 18, 25, 10, 100, 1000], dtype=cp.int32
@@ -383,7 +455,9 @@ def test_get_values_from_intervals_multiple_tracks(default_value) -> None:
     )
     query_starts = cp.asarray([9], dtype=cp.int32)
     query_ends = cp.asarray([20], dtype=cp.int32)
-    reserved = cp.zeros([3, 1, 3], dtype=cp.dtype("<f4"))
+    batch_size = len(query_starts)
+    n_tracks = 3
+    reserved = cp.zeros([batch_size, 3, n_tracks], dtype=cp.dtype("<f4"))
     values = intervals_to_values(
         track_starts,
         track_ends,
@@ -394,6 +468,7 @@ def test_get_values_from_intervals_multiple_tracks(default_value) -> None:
         window_size=3,
         default_value=default_value,
         out=reserved,
+        dtype=dtype,
     )
 
     x = default_value
@@ -434,22 +509,24 @@ def apply_window(full_matrix):
         )
 
     expected = apply_window(expected)
+    expected = reshape_expected(expected, batch_size, n_tracks)
 
-    print("expected:")
+    print("expected:", expected.shape)
     print(expected)
-    print("actual:")
+    print("actual:", values.shape)
     print(values)
     print("difference")
     print(values - expected)
-    assert cp.allclose(values, expected, atol=1e-2, rtol=1e-2, equal_nan=True)
+    assert all_close(values, expected, dtype)
 
 
+@pytest.mark.parametrize("dtype", ["float32", "bfloat16"])
 @pytest.mark.parametrize(
     "sequence_length, window_size", product([8, 9, 10, 11, 13, 23], [2, 3, 4, 10, 11])
 )
 @pytest.mark.parametrize("default_value", [0.0, cp.nan])
 def test_combinations_sequence_length_window_size(
-    sequence_length, window_size, default_value
+    sequence_length, window_size, default_value, dtype
 ) -> None:
     """Test intervals_to_values with 3 tracks and a batch size of 4."""
     track_starts = cp.asarray(
@@ -466,7 +543,6 @@ def test_combinations_sequence_length_window_size(
     query_ends = query_starts + sequence_length
     reduced_dim = sequence_length // window_size
 
-    reserved = cp.zeros([3, 4, reduced_dim], dtype=cp.dtype("<f4"))
     values = intervals_to_values(
         track_starts,
         track_ends,
@@ -476,10 +552,9 @@ def test_combinations_sequence_length_window_size(
         sizes=cp.asarray([4, 5, 3], dtype=cp.int32),
         window_size=window_size,
         default_value=default_value,
-        out=reserved,
+        dtype=dtype,
     )
 
-    reserved = cp.zeros([3, 4, sequence_length], dtype=cp.dtype("<f4"))
     values_with_window_size_1 = intervals_to_values(
         track_starts,
         track_ends,
@@ -489,28 +564,38 @@ def test_combinations_sequence_length_window_size(
         sizes=cp.asarray([4, 5, 3], dtype=cp.int32),
         window_size=1,
         default_value=default_value,
-        out=reserved,
+        dtype=dtype,
     )
 
-    full_matrix = values_with_window_size_1[:, :, : reduced_dim * window_size]
+    # For bfloat16, we need to convert to float32 first to compute the expected values correctly
+    # because cp.nanmean on uint16 would treat them as integers, not as bfloat16 bit patterns
+    if dtype == "bfloat16":
+        from conftest import uint16_to_float32
+
+        values_for_expected = uint16_to_float32(values_with_window_size_1)
+    else:
+        values_for_expected = values_with_window_size_1
+
+    full_matrix = values_for_expected[:, : reduced_dim * window_size, :]
     full_matrix = full_matrix.reshape(
-        full_matrix.shape[0], full_matrix.shape[1], reduced_dim, window_size
+        full_matrix.shape[0], reduced_dim, window_size, full_matrix.shape[2]
     )
-    expected = cp.nanmean(full_matrix, axis=-1)
+    expected = cp.nanmean(full_matrix, axis=2)
 
     print("expected:")
     print(expected)
     print("actual:")
     print(values)
-    assert cp.allclose(values, expected, equal_nan=True)
+    assert all_close(values, expected, dtype)
 
 
+@pytest.mark.parametrize("dtype", ["float32", "bfloat16"])
 @pytest.mark.parametrize(
     "window_size, batch_size, n_tracks", product([1, 2, 3], [1, 2, 3], [1, 2, 3])
 )
 @pytest.mark.parametrize("default_value", [0.0, cp.nan])
 def test_combinations_window_size_batch_size_n_tracks(
-    window_size, batch_size, n_tracks, default_value
+    window_size, batch_size, n_tracks, default_value, dtype
 ) -> None:
     """."""
     track_starts = cp.asarray([1, 3, 10, 12, 16] * n_tracks, dtype=cp.int32)
@@ -531,6 +616,7 @@ def test_combinations_window_size_batch_size_n_tracks(
         sizes=sizes,
         window_size=window_size,
         default_value=default_value,
+        dtype=dtype,
     )
 
     values_with_window_size_1 = intervals_to_values(
@@ -542,21 +628,30 @@ def test_combinations_window_size_batch_size_n_tracks(
         sizes=sizes,
         window_size=1,
         default_value=default_value,
+        dtype=dtype,
     )
 
+    # For bfloat16, we need to convert to float32 first to compute the expected values correctly
+    if dtype == "bfloat16":
+        from conftest import uint16_to_float32
+
+        values_for_expected = uint16_to_float32(values_with_window_size_1)
+    else:
+        values_for_expected = values_with_window_size_1
+
     reduced_dim = sequence_length // window_size
-    full_matrix = values_with_window_size_1[:, :, : reduced_dim * window_size]
+    full_matrix = values_for_expected[:, : reduced_dim * window_size, :]
     full_matrix = full_matrix.reshape(
-        full_matrix.shape[0], full_matrix.shape[1], reduced_dim, window_size
+        full_matrix.shape[0], reduced_dim, window_size, full_matrix.shape[2]
     )
-    expected = cp.nanmean(full_matrix, axis=-1)
+    expected = cp.nanmean(full_matrix, axis=2)
 
     print("expected:")
     print(expected)
     print("actual:")
     print(values)
 
-    assert cp.allclose(values, expected, equal_nan=True)
+    assert all_close(values, expected, dtype)
 
 
 def create_random_track_data(n_tracks, min_intervals=10, max_intervals=20):
@@ -582,7 +677,7 @@ def create_random_track_data(n_tracks, min_intervals=10, max_intervals=20):
     return (
         cp.asarray(track_starts, dtype=cp.int32),
         cp.asarray(track_ends, dtype=cp.int32),
-        cp.asarray(values, dtype=cp.int32),
+        cp.asarray(values, dtype=cp.float32),
         cp.asarray(sizes, dtype=cp.int32),
     )
 
@@ -593,13 +688,14 @@ def create_random_queries(batch_size, sequence_length=200):
     return cp.asarray(start, dtype=cp.int32), cp.asarray(end, dtype=cp.int32)
 
 
+@pytest.mark.parametrize("dtype", ["float32", "bfloat16"])
 @pytest.mark.parametrize(
     "window_size, batch_size, n_tracks",
     product([1, 2, 3, 7], [1, 2, 3, 7], [1, 2, 3, 7]),
 )
 @pytest.mark.parametrize("default_value", [0.0, cp.nan])
 def test_combinations_window_size_batch_size_n_tracks_on_random_data(
-    window_size, batch_size, n_tracks, default_value
+    window_size, batch_size, n_tracks, default_value, dtype
 ) -> None:
     sequence_length = 200
     track_starts, track_ends, track_values, sizes = create_random_track_data(n_tracks)
@@ -616,6 +712,7 @@ def test_combinations_window_size_batch_size_n_tracks_on_random_data(
         sizes=sizes,
         window_size=window_size,
         default_value=default_value,
+        dtype=dtype,
     )
 
     values_with_window_size_1 = intervals_to_values(
@@ -627,23 +724,31 @@ def test_combinations_window_size_batch_size_n_tracks_on_random_data(
         sizes=sizes,
         window_size=1,
         default_value=cp.nan,
+        dtype=dtype,
     )
 
+    # For bfloat16, we need to convert to float32 first to compute the expected values correctly
+    if dtype == "bfloat16":
+        from conftest import uint16_to_float32
+
+        values_with_window_size_1 = uint16_to_float32(values_with_window_size_1)
+        values = uint16_to_float32(values)
+
     cp.nan_to_num(values_with_window_size_1, copy=False, nan=default_value)
 
     reduced_dim = sequence_length // window_size
-    full_matrix = values_with_window_size_1[:, :, : reduced_dim * window_size]
+    full_matrix = values_with_window_size_1[:, : reduced_dim * window_size, :]
     full_matrix = full_matrix.reshape(
-        full_matrix.shape[0], full_matrix.shape[1], reduced_dim, window_size
+        full_matrix.shape[0], reduced_dim, window_size, full_matrix.shape[2]
     )
-    expected = cp.nanmean(full_matrix, axis=-1)
+    expected = cp.nanmean(full_matrix, axis=2)
 
     print("expected:")
     print(expected)
     print("actual:")
     print(values)
 
-    assert cp.allclose(values, expected, equal_nan=True)
+    assert all_close(values, expected, dtype)
 
 
 if __name__ == "__main__":
diff --git a/tests/test_pytorch_dataset.py b/tests/test_pytorch_dataset.py
index d7ca95d..cc7057f 100644
--- a/tests/test_pytorch_dataset.py
+++ b/tests/test_pytorch_dataset.py
@@ -28,7 +28,7 @@ def pytorch_dataset(bigwig_path, reference_genome_path, merged_intervals):
 
 def test_pytorch_dataset(pytorch_dataset):
     for sequence, target in pytorch_dataset:
-        assert target.shape == (256, 2, 1000)
+        assert target.shape == (256, 1000, 2)
 
 
 def test_input_and_target_is_torch_tensor(pytorch_dataset):
@@ -37,9 +37,35 @@ def test_input_and_target_is_torch_tensor(pytorch_dataset):
     assert isinstance(target, torch.Tensor)
 
 
-@pytest.mark.parametrize("default_value", [0.0, torch.nan, 4.0, 5.6])
-def test_pytorch_dataset_with_window_function(
+@pytest.mark.parametrize("default_value", [4.0, 5.6])
+def test_pytorch_dataset_with_window_function_some_positive_values(
     default_value, bigwig_path, reference_genome_path, merged_intervals
+):
+    """
+    Testing window function with default values 4.0 and 5.6.
+    Unlikely you will ever do this,butgood as a test.
+    """
+    _pytorch_dataset_with_window_function(
+        default_value,
+        bigwig_path,
+        reference_genome_path,
+        merged_intervals,
+        dtype="float32",
+    )
+
+
+@pytest.mark.parametrize("dtype", ["float32", "bfloat16"])
+@pytest.mark.parametrize("default_value", [0.0, torch.nan])
+def test_pytorch_dataset_with_window_function(
+    dtype, default_value, bigwig_path, reference_genome_path, merged_intervals
+):
+    _pytorch_dataset_with_window_function(
+        default_value, bigwig_path, reference_genome_path, merged_intervals, dtype
+    )
+
+
+def _pytorch_dataset_with_window_function(
+    default_value, bigwig_path, reference_genome_path, merged_intervals, dtype
 ):
     from bigwig_loader.pytorch import PytorchBigWigDataset
 
@@ -70,6 +96,7 @@ def test_pytorch_dataset_with_window_function(
         custom_position_sampler=position_sampler,
         default_value=default_value,
         return_batch_objects=True,
+        dtype=dtype,
     )
 
     dataset_with_window = PytorchBigWigDataset(
@@ -86,6 +113,7 @@ def test_pytorch_dataset_with_window_function(
         custom_position_sampler=position_sampler,
         default_value=default_value,
         return_batch_objects=True,
+        dtype=dtype,
     )
 
     print(dataset_with_window._dataset.bigwig_collection.bigwig_paths)
@@ -99,17 +127,31 @@ def test_pytorch_dataset_with_window_function(
         print(batch_with_window.starts)
         print(batch.ends)
         print(batch_with_window.ends)
+        # expected = batch.values.reshape(
+        #     batch.values.shape[0], batch.values.shape[1], reduced_dim, window_size
+        # )
         expected = batch.values.reshape(
-            batch.values.shape[0], batch.values.shape[1], reduced_dim, window_size
+            batch.values.shape[0],
+            reduced_dim,
+            window_size,
+            batch.values.shape[-1],
         )
         if not isnan(default_value) or default_value == 0:
             expected = torch.nan_to_num(expected, nan=default_value)
-        expected = torch.nanmean(expected, axis=-1)
+        expected = torch.nanmean(expected, axis=-2)
         print("---")
-        print("expected")
-        print(expected)
-        print("batch_with_window")
-        print(batch_with_window.values)
-        assert torch.allclose(expected, batch_with_window.values, equal_nan=True)
+        print("expected", expected.shape)
+        print(expected[2, :, :])
+        print("batch_with_window", batch_with_window.values.shape)
+        print(batch_with_window.values[2, :, :])
+        # Use looser tolerances for bfloat16 due to lower precision
+        if dtype == "bfloat16":
+            assert torch.allclose(
+                expected, batch_with_window.values, rtol=1e-2, atol=1e-2, equal_nan=True
+            )
+        else:
+            assert torch.allclose(expected, batch_with_window.values, equal_nan=True)
+
+        # TODO: I need the bigwig file with empty intervals
         if isnan(default_value):
             assert torch.isnan(batch_with_window.values).any()
diff --git a/tests/test_streamed_dataset.py b/tests/test_streamed_dataset.py
index 20aa671..1ab35f1 100644
--- a/tests/test_streamed_dataset.py
+++ b/tests/test_streamed_dataset.py
@@ -41,7 +41,7 @@ def input_generator() -> (
             if i == 2:
                 break
 
-    assert batch.values.shape == (batch_size, len(collection), sequence_length)
+    assert batch.values.shape == (batch_size, sequence_length, len(collection))
     assert len(batch.starts) == batch_size
     assert len(batch.ends) == batch_size
     assert len(batch.chromosomes) == batch_size
@@ -132,7 +132,7 @@ def input_generator() -> (
     )
     with data_loader:
         for i, batch in enumerate(data_loader):
-            assert batch.values.shape == (batch_size, len(collection), sequence_length)
+            assert batch.values.shape == (batch_size, sequence_length, len(collection))
             assert len(batch.starts) == batch_size
             assert len(batch.ends) == batch_size
             assert len(batch.chromosomes) == batch_size
@@ -199,8 +199,8 @@ def input_generator() -> (
         for i, batch in enumerate(data_loader):
             assert batch.values.shape == (
                 batch_size,
-                batch.other["n_tracks"],
                 sequence_length,
+                batch.other["n_tracks"],
             )
             assert len(batch.starts) == batch_size
             assert len(batch.ends) == batch_size
diff --git a/tests/test_window_function.py b/tests/test_window_function.py
index 7c828a3..2680981 100644
--- a/tests/test_window_function.py
+++ b/tests/test_window_function.py
@@ -36,11 +36,11 @@ def test_same_output(bigwig_path, window_size, default_value):
     else:
         assert not cp.isnan(full_batch).any()
 
-    full_matrix = full_batch[:, :, : reduced_dim * window_size]
+    full_matrix = full_batch[:, : reduced_dim * window_size, :]
     full_matrix = full_matrix.reshape(
-        full_matrix.shape[0], full_matrix.shape[1], reduced_dim, window_size
+        full_matrix.shape[0], reduced_dim, window_size, full_matrix.shape[-1]
     )
-    expected = cp.nanmean(full_matrix, axis=-1)
+    expected = cp.nanmean(full_matrix, axis=-2)
     print(batch_with_window)
     print(expected)
     assert cp.allclose(expected, batch_with_window, equal_nan=True)
@@ -105,15 +105,18 @@ def test_dataset_with_window_function(
         print(batch.ends)
         print(batch_with_window.ends)
         expected = batch.values.reshape(
-            batch.values.shape[0], batch.values.shape[1], reduced_dim, window_size
+            batch.values.shape[0],
+            reduced_dim,
+            window_size,
+            batch.values.shape[-1],
         )
         if not isnan(default_value) or default_value == 0:
             cp.nan_to_num(expected, copy=False, nan=default_value)
-        expected = cp.nanmean(expected, axis=-1)
+        expected = cp.nanmean(expected, axis=-2)
         print("---")
-        print("expected")
+        print("expected", expected.shape)
         print(expected)
-        print("batch_with_window")
+        print("batch_with_window", batch_with_window.values.shape)
         print(batch_with_window.values)
         assert cp.allclose(expected, batch_with_window.values, equal_nan=True)
         if isnan(default_value):