diff --git a/lib/bumblebee.ex b/lib/bumblebee.ex
index ce8bc125..51f2330f 100644
--- a/lib/bumblebee.ex
+++ b/lib/bumblebee.ex
@@ -188,6 +188,8 @@ defmodule Bumblebee do
     "RobertaForTokenClassification" => {Bumblebee.Text.Roberta, :for_token_classification},
     "RobertaForCausalLM" => {Bumblebee.Text.Roberta, :for_causal_language_modeling},
     "RobertaModel" => {Bumblebee.Text.Roberta, :base},
+    "SwinModel" => {Bumblebee.Vision.Swin, :base},
+    "SwinForImageClassification" => {Bumblebee.Vision.Swin, :for_image_classification},
     "T5Model" => {Bumblebee.Text.T5, :base},
     "T5ForConditionalGeneration" => {Bumblebee.Text.T5, :for_conditional_generation},
     "T5EncoderModel" => {Bumblebee.Text.T5, :encoder},
diff --git a/lib/bumblebee/layers.ex b/lib/bumblebee/layers.ex
index cde12666..e6a61794 100644
--- a/lib/bumblebee/layers.ex
+++ b/lib/bumblebee/layers.ex
@@ -200,7 +200,7 @@ defmodule Bumblebee.Layers do
     * `query` - `{batch_size, sequence_length, num_heads, head_size}`
     * `key` - `{batch_size, kv_sequence_length, num_heads, head_size}`
     * `value` - `{batch_size, kv_sequence_length, num_heads, head_size}`
-    * `key_mask` (optional) - `{batch_size, kv_sequence_length}`
+    * `key_mask` (optional) - `{batch_size, kv_sequence_length} | {batch_size, num_heads, sequence_length, kv_sequence_length}`
     * `head_mask` (optional) - `{num_heads}`
     * `bias` (optional) - `{batch_size | 1, num_heads | 1, sequence_length, kv_sequence_length}`
     * `offset` (optional) - `{}`
@@ -273,8 +273,14 @@ defmodule Bumblebee.Layers do
 
     key_mask =
       case key_mask do
-        %Axon.None{} -> Nx.broadcast(1, {1, 1, 1, 1})
-        key_mask -> key_mask |> Nx.new_axis(1) |> Nx.new_axis(1)
+        %Axon.None{} ->
+          Nx.broadcast(1, {1, 1, 1, 1})
+
+        key_mask ->
+          case Nx.rank(key_mask) do
+            2 -> key_mask |> Nx.new_axis(1) |> Nx.new_axis(1)
+            4 -> key_mask
+          end
       end
 
     query_sequence_length = Nx.axis_size(query, 2)
diff --git a/lib/bumblebee/utils/nx.ex b/lib/bumblebee/utils/nx.ex
index 51c6dadd..f9806af6 100644
--- a/lib/bumblebee/utils/nx.ex
+++ b/lib/bumblebee/utils/nx.ex
@@ -408,6 +408,82 @@ defmodule Bumblebee.Utils.Nx do
     Nx.take(tensor, flat_idx, axis: opts[:axis])
   end
 
+  @doc """
+  Shifts elements along the specified axes.
+
+  When an shift is positive, the elements are shifted clockwise.
+  Negative shifts result in counter-clockwise shift.
+
+  ## Options
+
+    * `:shifts` - the shift size to apply to the corresponding axis
+      from `:axes`
+
+    * `:axes` - the axes to apply shift to, must have the same length
+      as `:shifts`
+
+  ## Examples
+
+    iex> x = Nx.iota({3, 3})
+    iex> Bumblebee.Utils.Nx.roll(x, shifts: [1], axes: [0])
+    #Nx.Tensor<
+      s64[3][3]
+      [
+        [6, 7, 8],
+        [0, 1, 2],
+        [3, 4, 5]
+      ]
+    >
+
+    iex> x = Nx.iota({3, 3})
+    iex> Bumblebee.Utils.Nx.roll(x, shifts: [-1], axes: [0])
+    #Nx.Tensor<
+      s64[3][3]
+      [
+        [3, 4, 5],
+        [6, 7, 8],
+        [0, 1, 2]
+      ]
+    >
+
+    iex> x = Nx.iota({3, 3})
+    iex> Bumblebee.Utils.Nx.roll(x, shifts: [1, 2], axes: [0, 1])
+    #Nx.Tensor<
+      s64[3][3]
+      [
+        [7, 8, 6],
+        [1, 2, 0],
+        [4, 5, 3]
+      ]
+    >
+
+  """
+  deftransform roll(tensor, opts) do
+    opts = Keyword.validate!(opts, shifts: [], axes: [])
+
+    shifts = opts[:shifts]
+    axes = opts[:axes]
+
+    if length(shifts) != length(axes) do
+      raise ArgumentError,
+            "expected shifts and axes to have the same number of elements," <>
+              " got shifts: #{inspect(shifts)}, axes: #{inspect(axes)}"
+    end
+
+    shifts
+    |> Enum.zip(axes)
+    |> Enum.reduce(tensor, fn {shift, axis}, tensor ->
+      shift = rem(shift, Nx.axis_size(tensor, axis))
+
+      if shift == 0 do
+        tensor
+      else
+        {left, right} = Nx.split(tensor, -shift, axis: axis)
+        Nx.concatenate([right, left], axis: axis)
+      end
+    end)
+  end
+
   @doc """
   Returns size of the given `Nx.Batch`, including padding.
   """
diff --git a/lib/bumblebee/vision/swin.ex b/lib/bumblebee/vision/swin.ex
new file mode 100644
index 00000000..42da43ca
--- /dev/null
+++ b/lib/bumblebee/vision/swin.ex
@@ -0,0 +1,660 @@
+defmodule Bumblebee.Vision.Swin do
+  alias Bumblebee.Shared
+
+  options =
+    [
+      image_size: [
+        default: 224,
+        doc: "the size of the input spatial dimensions"
+      ],
+      num_channels: [
+        default: 3,
+        doc: "the number of channels in the input"
+      ],
+      patch_size: [
+        default: 4,
+        doc: "the size of the patch spatial dimensions"
+      ],
+      embedding_size: [
+        default: 96,
+        doc: "the dimensionality of patch embedding layer"
+      ],
+      use_absolute_position_embeddings: [
+        default: false,
+        doc: "whether to add absolute position embeddings to the patch embeddings"
+      ],
+      num_blocks: [
+        default: [2, 2, 6, 2],
+        doc: "the number of Transformer blocks in the encoder at each stage"
+      ],
+      num_attention_heads: [
+        default: [3, 6, 12, 24],
+        doc: "the number of attention heads for each attention layer in the encoder at each stage"
+      ],
+      window_size: [
+        default: 7,
+        doc:
+          "the window size, used to limit self-attention computation to non-overlapping windows"
+      ],
+      intermediate_size_ratio: [
+        default: 4,
+        doc: """
+        the dimensionality of the intermediate layer in the transformer feed-forward network (FFN) in the encoder,
+        expressed as a multiplier of hidden size (at the given stage)
+        """
+      ],
+      use_attention_bias: [
+        default: true,
+        doc: "whether to use bias in query, key, and value projections"
+      ],
+      activation: [
+        default: :gelu,
+        doc: "the activation function"
+      ],
+      dropout_rate: [
+        default: 0.0,
+        doc: "the dropout rate for encoder and decoder"
+      ],
+      attention_dropout_rate: [
+        default: 0.0,
+        doc: "the dropout rate for attention weights"
+      ],
+      initializer_scale: [
+        default: 0.02,
+        doc:
+          "the standard deviation of the normal initializer used for initializing kernel parameters"
+      ],
+      drop_path_rate: [
+        default: 0.1,
+        doc: "the drop path rate used to for stochastic depth"
+      ],
+      layer_norm_epsilon: [
+        default: 1.0e-5,
+        doc: "the epsilon used by the layer normalization layers"
+      ]
+    ] ++ Shared.common_options([:num_labels, :id_to_label])
+
+  @moduledoc """
+  Swin Transformer model.
+
+  ## Architectures
+
+    * `:base` - plain Swin without any head on top
+
+    * `:for_image_classification` - Swin tranformer model with a
+      classification head
+
+  ## Global layer options
+
+  #{Shared.global_layer_options_doc([:output_hidden_states, :output_attentions])}
+
+  ## Configuration
+
+  #{Shared.options_doc(options)}
+
+  ## References
+
+    * [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)
+
+  """
+
+  defstruct [architecture: :base] ++ Shared.option_defaults(options)
+
+  @behaviour Bumblebee.ModelSpec
+  @behaviour Bumblebee.Configurable
+
+  import Bumblebee.Utils.Model, only: [join: 2]
+  import Nx.Defn
+
+  alias Bumblebee.Layers
+
+  @impl true
+  def architectures(), do: [:base, :for_image_classification]
+
+  @impl true
+  def config(spec, opts) do
+    spec
+    |> Shared.put_config_attrs(opts)
+    |> Shared.validate_label_options()
+  end
+
+  @impl true
+  def input_template(spec) do
+    %{
+      "pixel_values" =>
+        Nx.template({1, spec.image_size, spec.image_size, spec.num_channels}, :f32)
+    }
+  end
+
+  @impl true
+  def model(%__MODULE__{architecture: :base} = spec) do
+    spec
+    |> inputs()
+    |> core(spec)
+    |> Layers.output()
+  end
+
+  def model(%__MODULE__{architecture: :for_image_classification} = spec) do
+    inputs = inputs(spec)
+    outputs = core(inputs, spec)
+
+    logits =
+      Axon.dense(outputs.pooled_state, spec.num_labels,
+        kernel_initializer: kernel_initializer(spec),
+        name: "image_classification_head.output"
+      )
+
+    Layers.output(%{
+      logits: logits,
+      hidden_states: outputs.hidden_states,
+      attentions: outputs.attentions
+    })
+  end
+
+  defp inputs(spec) do
+    shape = {nil, spec.image_size, spec.image_size, spec.num_channels}
+
+    Bumblebee.Utils.Model.inputs_to_map([
+      Axon.input("pixel_values", shape: shape),
+      Axon.input("patch_mask", shape: {nil, nil}, optional: true)
+    ])
+  end
+
+  defp core(inputs, spec, opts \\ []) do
+    name = opts[:name]
+
+    embeddings =
+      embedder(inputs["pixel_values"], inputs["patch_mask"], spec, name: join(name, "embedder"))
+
+    encoder_outputs =
+      encoder(embeddings, spec, name: join(name, "encoder"))
+
+    hidden_state =
+      Axon.layer_norm(encoder_outputs.hidden_state,
+        epsilon: spec.layer_norm_epsilon,
+        name: join(name, "norm")
+      )
+
+    pooled_state =
+      hidden_state
+      |> Axon.adaptive_avg_pool(output_size: {1}, name: join(name, "pooler"))
+      |> Axon.flatten()
+
+    %{
+      hidden_state: hidden_state,
+      pooled_state: pooled_state,
+      hidden_states: encoder_outputs.hidden_states,
+      attentions: encoder_outputs.attentions
+    }
+  end
+
+  defp embedder(pixel_values, patch_mask, spec, opts) do
+    name = opts[:name]
+
+    embeddings =
+      pixel_values
+      |> patch_embedding(spec, name: join(name, "patch_embedding"))
+      |> Layers.apply_vision_patch_mask(patch_mask, name: join(name, "mask_tokens"))
+      |> Axon.layer_norm(epsilon: spec.layer_norm_epsilon, name: join(name, "norm"))
+
+    embeddings =
+      if spec.use_absolute_position_embeddings do
+        num_patches = div(spec.image_size, spec.patch_size) ** 2
+
+        position_embeddings =
+          Layers.learned_embeddings(num_patches, spec.embedding_size,
+            initializer: :zeros,
+            name: join(name, "position_embedding")
+          )
+
+        Axon.add(embeddings, position_embeddings)
+      else
+        embeddings
+      end
+
+    embeddings
+    |> Axon.dropout(rate: spec.dropout_rate, name: join(name, "dropout"))
+  end
+
+  defp patch_embedding(pixel_values, spec, opts) do
+    name = opts[:name]
+    hidden_size = spec.embedding_size
+
+    pixel_values
+    |> Axon.conv(hidden_size,
+      kernel_size: spec.patch_size,
+      strides: spec.patch_size,
+      padding: :valid,
+      kernel_initializer: kernel_initializer(spec),
+      name: join(name, "projection")
+    )
+    |> Axon.reshape({:batch, :auto, spec.embedding_size}, name: join(name, "reshape"))
+  end
+
+  defp encoder(hidden_state, spec, opts) do
+    name = opts[:name]
+
+    state = %{
+      hidden_state: hidden_state,
+      hidden_states: Axon.container({hidden_state}),
+      attentions: Axon.container({})
+    }
+
+    for stage_idx <- 0..(length(spec.num_blocks) - 1), reduce: state do
+      state ->
+        name = name |> join("stages") |> join(stage_idx)
+
+        grid_size = div(spec.image_size, spec.patch_size)
+        input_resolution = div(grid_size, 2 ** stage_idx)
+
+        {hidden_state, attention, hidden_state_before_downsample} =
+          stage(state.hidden_state, spec,
+            hidden_size: spec.embedding_size * 2 ** stage_idx,
+            num_blocks: Enum.at(spec.num_blocks, stage_idx),
+            num_attention_heads: Enum.at(spec.num_attention_heads, stage_idx),
+            downsample: stage_idx < length(spec.num_blocks) - 1,
+            input_resolution: input_resolution,
+            name: name
+          )
+
+        %{
+          hidden_state: hidden_state,
+          hidden_states: Layers.append(state.hidden_states, hidden_state_before_downsample),
+          attentions: Layers.append(state.attentions, attention)
+        }
+    end
+  end
+
+  defp stage(hidden_state, spec, opts) do
+    name = opts[:name]
+    downsample = opts[:downsample]
+    hidden_size = opts[:hidden_size]
+    num_blocks = opts[:num_blocks]
+    num_attention_heads = opts[:num_attention_heads]
+    input_resolution = opts[:input_resolution]
+
+    # Note that we include only record hidden_state and attention
+    # from the last block in each stage
+
+    {hidden_state, attention} =
+      for block_idx <- 0..(num_blocks - 1), reduce: {hidden_state, nil} do
+        {hidden_state, _attention} ->
+          name = name |> join("blocks") |> join(block_idx)
+
+          shift_size =
+            if rem(block_idx, 2) == 0 do
+              0
+            else
+              div(spec.window_size, 2)
+            end
+
+          {hidden_state, attention} =
+            transformer_block(hidden_state,
+              num_attention_heads: num_attention_heads,
+              hidden_size: hidden_size,
+              kernel_initializer: kernel_initializer(spec),
+              dropout_rate: spec.dropout_rate,
+              attention_dropout_rate: spec.attention_dropout_rate,
+              layer_norm_epsilon: spec.layer_norm_epsilon,
+              intermediate_size: floor(spec.intermediate_size_ratio * hidden_size),
+              activation: spec.activation,
+              name: name,
+              window_size: spec.window_size,
+              shift_size: shift_size,
+              input_resolution: input_resolution
+            )
+
+          {hidden_state, attention}
+      end
+
+    hidden_state_before_downsample = hidden_state
+
+    hidden_state =
+      if downsample do
+        patch_merging(hidden_state,
+          input_resolution: input_resolution,
+          hidden_size: hidden_size,
+          layer_norm_epsilon: spec.layer_norm_epsilon,
+          kernel_initializer: kernel_initializer(spec),
+          name: join(name, "downsample")
+        )
+      else
+        hidden_state
+      end
+
+    {hidden_state, attention, hidden_state_before_downsample}
+  end
+
+  defp transformer_block(hidden_state, opts) do
+    num_attention_heads = opts[:num_attention_heads]
+    hidden_size = opts[:hidden_size]
+    kernel_initializer = opts[:kernel_initializer]
+    dropout_rate = opts[:dropout_rate]
+    attention_dropout_rate = opts[:attention_dropout_rate]
+    layer_norm_epsilon = opts[:layer_norm_epsilon]
+    intermediate_size = opts[:intermediate_size]
+    activation = opts[:activation]
+    name = opts[:name]
+    window_size = opts[:window_size]
+    shift_size = opts[:shift_size]
+    input_resolution = opts[:input_resolution]
+
+    {shift_size, window_size} =
+      if input_resolution <= window_size do
+        {0, input_resolution}
+      else
+        {shift_size, window_size}
+      end
+
+    shortcut = hidden_state
+
+    attention_mask =
+      window_attention_mask(hidden_state, shift_size, window_size, input_resolution)
+
+    relative_attention_bias =
+      relative_attention_bias(window_size, num_attention_heads,
+        name: join(name, "self_attention.relative_attention_bias")
+      )
+
+    hidden_state =
+      hidden_state
+      |> Axon.layer_norm(epsilon: layer_norm_epsilon, name: join(name, "self_attention_norm"))
+      |> hidden_state_windows(shift_size, window_size, input_resolution)
+
+    {hidden_state, attention, _self_attention_cache, _attention_relative_bias} =
+      Layers.Transformer.multi_head_attention(hidden_state, hidden_state, hidden_state,
+        attention_mask: attention_mask,
+        attention_relative_bias: relative_attention_bias,
+        num_heads: num_attention_heads,
+        hidden_size: hidden_size,
+        kernel_initializer: kernel_initializer,
+        dropout_rate: attention_dropout_rate,
+        name: join(name, "self_attention")
+      )
+
+    hidden_state =
+      Axon.dropout(hidden_state, rate: dropout_rate, name: join(name, "self_attention_dropout"))
+
+    hidden_state =
+      hidden_state
+      |> reverse_hidden_state_windows(shift_size, window_size, input_resolution)
+      |> Axon.dropout(rate: dropout_rate)
+
+    hidden_state = Axon.add(hidden_state, shortcut)
+
+    shortcut = hidden_state
+
+    hidden_state =
+      hidden_state
+      |> Axon.layer_norm(epsilon: layer_norm_epsilon, name: join(name, "output_norm"))
+      |> Axon.dense(intermediate_size, name: join(name, "ffn.intermediate"))
+      |> Layers.activation(activation)
+      |> Axon.dense(hidden_size, name: join(name, "ffn.output"))
+      |> Axon.dropout(rate: dropout_rate)
+
+    hidden_state = Axon.add(hidden_state, shortcut)
+
+    {hidden_state, attention}
+  end
+
+  defp window_attention_mask(hidden_state, shift_size, window_size, input_resolution) do
+    if shift_size > 0 do
+      # Computes attention mask for shifted window multi-head self-attention (SW-MSA)
+
+      Axon.nx(hidden_state, fn hidden_state ->
+        {batch_size, _dimension, _hidden_size} = Nx.shape(hidden_state)
+        height = width = input_resolution
+
+        # See Figure 4. in the paper. We color the 2D patches (tokens)
+        # into 4 groups. Then, we compute a mask such that each token
+        # attends only to tokens within the same group.
+
+        grid_0 = Nx.broadcast(0, {height - shift_size, width - shift_size})
+        grid_b = Nx.broadcast(1, {height - shift_size, shift_size})
+        grid_c = Nx.broadcast(2, {shift_size, width - shift_size})
+        grid_a = Nx.broadcast(3, {shift_size, shift_size})
+
+        grid =
+          Nx.concatenate([
+            Nx.concatenate([grid_0, grid_b], axis: 1),
+            Nx.concatenate([grid_c, grid_a], axis: 1)
+          ])
+
+        windowed_patch_groups =
+          grid
+          |> Nx.reshape({1, height, width, 1})
+          |> window_partition(window_size)
+          |> Nx.reshape({:auto, window_size * window_size})
+
+        windows_attention_mask =
+          Nx.equal(
+            Nx.new_axis(windowed_patch_groups, 1),
+            Nx.new_axis(windowed_patch_groups, 2)
+          )
+          |> Nx.new_axis(1)
+
+        # Note that we repeat the mask for each batched input, so that
+        # the batch dimension has size batch_size * num_windows, which
+        # matches the input. This way we can apply the mask as usual,
+        # without reshaping back and forth.
+        Nx.tile(windows_attention_mask, [batch_size, 1, 1, 1])
+      end)
+    else
+      Layers.none()
+    end
+  end
+
+  defp relative_attention_bias(window_size, num_attention_heads, opts) do
+    name = opts[:name]
+
+    kernel =
+      Axon.param("kernel", {(2 * window_size - 1) * (2 * window_size - 1), num_attention_heads})
+
+    Axon.layer(
+      fn kernel, opts ->
+        window_size = opts[:window_size]
+
+        idx = relative_position_index(window_size) |> Nx.reshape({:auto})
+
+        kernel
+        |> Nx.take(idx)
+        |> Nx.reshape({window_size * window_size, window_size * window_size, :auto})
+        |> Nx.transpose(axes: [2, 0, 1])
+        |> Nx.new_axis(0)
+      end,
+      [kernel],
+      window_size: window_size,
+      name: name
+    )
+  end
+
+  defp relative_position_index(window_size) do
+    coords_h = Nx.iota({window_size, window_size}, axis: 0) |> Nx.flatten()
+    coords_w = Nx.iota({window_size, window_size}, axis: 1) |> Nx.flatten()
+    coord_pairs = Nx.stack([coords_h, coords_w])
+
+    relative_coords = Nx.subtract(Nx.new_axis(coord_pairs, 2), Nx.new_axis(coord_pairs, 1))
+
+    relative_coords
+    |> Nx.add(Nx.reshape(Nx.tensor([window_size - 1, window_size - 1]), {2, 1, 1}))
+    |> Nx.multiply(Nx.reshape(Nx.tensor([2 * window_size - 1, 1]), {2, 1, 1}))
+    |> Nx.sum(axes: [0])
+  end
+
+  defp hidden_state_windows(hidden_state, shift_size, window_size, input_resolution) do
+    Axon.nx(hidden_state, fn hidden_state ->
+      {batch_size, _dimension, hidden_size} = Nx.shape(hidden_state)
+
+      height = width = input_resolution
+      hidden_state = Nx.reshape(hidden_state, {batch_size, height, width, hidden_size})
+
+      # Apply cyclic shift
+      hidden_state =
+        if shift_size > 0 do
+          Bumblebee.Utils.Nx.roll(hidden_state, shifts: [-shift_size, -shift_size], axes: [1, 2])
+        else
+          hidden_state
+        end
+
+      # Partition windows
+      hidden_state
+      |> window_partition(window_size)
+      |> Nx.reshape({:auto, window_size * window_size, hidden_size})
+    end)
+  end
+
+  defp reverse_hidden_state_windows(hidden_state, shift_size, window_size, input_resolution) do
+    Axon.nx(hidden_state, fn hidden_state ->
+      {_batch_size, _dimension, hidden_size} = Nx.shape(hidden_state)
+      height = width = input_resolution
+
+      # Reverse window partitioning
+      hidden_state =
+        hidden_state
+        |> Nx.reshape({:auto, window_size, window_size, hidden_size})
+        |> window_unpartition(window_size, height, width)
+
+      # Reverse cyclic shift
+      hidden_state =
+        if shift_size > 0 do
+          Bumblebee.Utils.Nx.roll(hidden_state, shifts: [shift_size, shift_size], axes: [1, 2])
+        else
+          hidden_state
+        end
+
+      Nx.reshape(hidden_state, {:auto, height * width, hidden_size})
+    end)
+  end
+
+  defnp window_partition(tensor, window_size) do
+    {batch_size, height, width, hidden_size} = Nx.shape(tensor)
+    windowed_height = div(height, window_size)
+    windowed_width = div(width, window_size)
+
+    Nx.reshape(
+      tensor,
+      {batch_size, windowed_height, window_size, windowed_width, window_size, hidden_size}
+    )
+    |> Nx.transpose(axes: [0, 1, 3, 2, 4, 5])
+    |> Nx.reshape({:auto, window_size, window_size, hidden_size})
+  end
+
+  defnp window_unpartition(tensor, window_size, height, width) do
+    {_batch_size, _height, _width, hidden_size} = Nx.shape(tensor)
+    windowed_height = div(height, window_size)
+    windowed_width = div(width, window_size)
+
+    Nx.reshape(
+      tensor,
+      {:auto, windowed_height, windowed_width, window_size, window_size, hidden_size}
+    )
+    |> Nx.transpose(axes: [0, 1, 3, 2, 4, 5])
+    |> Nx.reshape({:auto, height, width, hidden_size})
+  end
+
+  defp patch_merging(hidden_state, opts) do
+    input_resolution = opts[:input_resolution]
+    hidden_size = opts[:hidden_size]
+    layer_norm_epsilon = opts[:layer_norm_epsilon]
+    kernel_initializer = opts[:kernel_initializer]
+    name = opts[:name]
+
+    # We group patches from each 2x2 square and apply a dense layer
+    # against each group
+
+    hidden_state
+    |> Axon.nx(fn hidden_state ->
+      {batch_size, _sequence_length, _hidden_size} = Nx.shape(hidden_state)
+
+      hidden_state =
+        Nx.reshape(hidden_state, {batch_size, input_resolution, input_resolution, :auto})
+
+      input_feature_0 = hidden_state[[.., 0..-1//2, 0..-1//2, ..]]
+      input_feature_1 = hidden_state[[.., 1..-1//2, 0..-1//2, ..]]
+      input_feature_2 = hidden_state[[.., 0..-1//2, 1..-1//2, ..]]
+      input_feature_3 = hidden_state[[.., 1..-1//2, 1..-1//2, ..]]
+
+      Nx.concatenate([input_feature_0, input_feature_1, input_feature_2, input_feature_3],
+        axis: -1
+      )
+      |> Nx.reshape({batch_size, :auto, 4 * hidden_size})
+    end)
+    |> Axon.layer_norm(epsilon: layer_norm_epsilon, name: join(name, "norm"))
+    |> Axon.dense(2 * hidden_size,
+      kernel_initializer: kernel_initializer,
+      name: join(name, "reduction"),
+      use_bias: false
+    )
+  end
+
+  defp kernel_initializer(spec) do
+    Axon.Initializers.normal(scale: spec.initializer_scale)
+  end
+
+  defimpl Bumblebee.HuggingFace.Transformers.Config do
+    def load(spec, data) do
+      import Shared.Converters
+
+      opts =
+        convert!(data,
+          attention_dropout_rate: {"attention_probs_dropout_prob", number()},
+          num_blocks: {"depths", list(number())},
+          drop_path_rate: {"drop_path_rate", number()},
+          embedding_size: {"embed_dim", number()},
+          activation: {"hidden_act", activation()},
+          dropout_rate: {"hidden_dropout_prob", number()},
+          image_size: {"image_size", number()},
+          initializer_scale: {"initializer_range", number()},
+          layer_norm_epsilon: {"layer_norm_eps", number()},
+          intermediate_size_ratio: {"mlp_ratio", number()},
+          num_channels: {"num_channels", number()},
+          num_attention_heads: {"num_heads", list(number())},
+          patch_size: {"patch_size", number()},
+          use_attention_bias: {"qkv_bias", boolean()},
+          use_absolute_position_embeddings: {"use_absolute_embeddings", boolean()},
+          window_size: {"window_size", number()}
+        ) ++ Shared.common_options_from_transformers(data, spec)
+
+      @for.config(spec, opts)
+    end
+  end
+
+  defimpl Bumblebee.HuggingFace.Transformers.Model do
+    def params_mapping(_spec) do
+      %{
+        "embedder.patch_embedding.projection" => "swin.embeddings.patch_embeddings.projection",
+        "embedder.norm" => "swin.embeddings.norm",
+        "encoder.stages.{n}.blocks.{m}.output_norm" =>
+          "swin.encoder.layers.{n}.blocks.{m}.layernorm_after",
+        "encoder.stages.{n}.blocks.{m}.self_attention_norm" =>
+          "swin.encoder.layers.{n}.blocks.{m}.layernorm_before",
+        "encoder.stages.{n}.blocks.{m}.self_attention.key" =>
+          "swin.encoder.layers.{n}.blocks.{m}.attention.self.key",
+        "encoder.stages.{n}.blocks.{m}.self_attention.output" =>
+          "swin.encoder.layers.{n}.blocks.{m}.attention.output.dense",
+        "encoder.stages.{n}.blocks.{m}.self_attention.query" =>
+          "swin.encoder.layers.{n}.blocks.{m}.attention.self.query",
+        "encoder.stages.{n}.blocks.{m}.self_attention.value" =>
+          "swin.encoder.layers.{n}.blocks.{m}.attention.self.value",
+        "encoder.stages.{n}.blocks.{m}.self_attention.relative_attention_bias" => %{
+          "kernel" => {
+            [
+              {"swin.encoder.layers.{n}.blocks.{m}.attention.self",
+               "relative_position_bias_table"}
+            ],
+            fn [kernel] -> kernel end
+          }
+        },
+        "encoder.stages.{n}.blocks.{m}.ffn.intermediate" =>
+          "swin.encoder.layers.{n}.blocks.{m}.intermediate.dense",
+        "encoder.stages.{n}.blocks.{m}.ffn.output" =>
+          "swin.encoder.layers.{n}.blocks.{m}.output.dense",
+        "encoder.stages.{n}.downsample.norm" => "swin.encoder.layers.{n}.downsample.norm",
+        "encoder.stages.{n}.downsample.reduction" =>
+          "swin.encoder.layers.{n}.downsample.reduction",
+        "norm" => "swin.layernorm",
+        "image_classification_head.output" => "classifier"
+      }
+    end
+  end
+end
diff --git a/test/bumblebee/vision/swin_test.exs b/test/bumblebee/vision/swin_test.exs
new file mode 100644
index 00000000..081e7401
--- /dev/null
+++ b/test/bumblebee/vision/swin_test.exs
@@ -0,0 +1,57 @@
+defmodule Bumblebee.Vision.SwinTest do
+  use ExUnit.Case, async: true
+
+  import Bumblebee.TestHelpers
+
+  @moduletag model_test_tags()
+
+  test ":base" do
+    assert {:ok, %{model: model, params: params, spec: spec}} =
+             Bumblebee.load_model({:hf, "hf-internal-testing/tiny-random-SwinModel"})
+
+    assert %Bumblebee.Vision.Swin{architecture: :base} = spec
+
+    inputs = %{
+      "pixel_values" => Nx.broadcast(0.5, {1, 32, 32, 3})
+    }
+
+    outputs = Axon.predict(model, params, inputs)
+
+    assert Nx.shape(outputs.hidden_state) == {1, 16, 64}
+    assert Nx.shape(outputs.pooled_state) == {1, 64}
+
+    assert_all_close(
+      outputs.hidden_state[[.., 1..3, 1..3]],
+      Nx.tensor([
+        [[-0.4605, 0.9336, -0.5528], [-0.4605, 0.9336, -0.5528], [-0.4605, 0.9336, -0.5528]]
+      ])
+    )
+
+    assert_all_close(
+      outputs.pooled_state[[.., 1..3]],
+      Nx.tensor([[-0.4605, 0.9336, -0.5528]])
+    )
+  end
+
+  test ":for_image_classification" do
+    assert {:ok, %{model: model, params: params, spec: spec}} =
+             Bumblebee.load_model(
+               {:hf, "hf-internal-testing/tiny-random-SwinForImageClassification"}
+             )
+
+    assert %Bumblebee.Vision.Swin{architecture: :for_image_classification} = spec
+
+    inputs = %{
+      "pixel_values" => Nx.broadcast(0.5, {1, 32, 32, 3})
+    }
+
+    outputs = Axon.predict(model, params, inputs)
+
+    assert Nx.shape(outputs.logits) == {1, 2}
+
+    assert_all_close(
+      outputs.logits,
+      Nx.tensor([[0.0361, 0.1352]])
+    )
+  end
+end