diff --git a/timm/models/__init__.py b/timm/models/__init__.py
index 238c1ccc..ca4d1167 100644
--- a/timm/models/__init__.py
+++ b/timm/models/__init__.py
@@ -61,6 +61,7 @@ from .selecsls import *
 from .senet import *
 from .sequencer import *
 from .sknet import *
+from .swiftformer import *
 from .swin_transformer import *
 from .swin_transformer_v2 import *
 from .swin_transformer_v2_cr import *
diff --git a/timm/models/swiftformer.py b/timm/models/swiftformer.py
new file mode 100644
index 00000000..a349a2e7
--- /dev/null
+++ b/timm/models/swiftformer.py
@@ -0,0 +1,605 @@
+"""SwiftFormer
+SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications
+Code: https://github.com/Amshaker/SwiftFormer
+Paper: https://arxiv.org/pdf/2303.15446
+
+@InProceedings{Shaker_2023_ICCV,
+    author    = {Shaker, Abdelrahman and Maaz, Muhammad and Rasheed, Hanoona and Khan, Salman and Yang, Ming-Hsuan and Khan, Fahad Shahbaz},
+    title     = {SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications},
+    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
+    year      = {2023},
+}
+"""
+import re
+from typing import Any, Dict, List, Optional, Set, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from timm.layers import DropPath, Linear, LayerType, to_2tuple, trunc_normal_
+from ._builder import build_model_with_cfg
+from ._features import feature_take_indices
+from ._manipulate import checkpoint_seq
+from ._registry import generate_default_cfgs, register_model
+
+__all__ = ['SwiftFormer']
+
+
+class LayerScale2d(nn.Module):
+    def __init__(self, dim: int, init_values: float = 1e-5, inplace: bool = False):
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(
+            init_values * torch.ones(dim, 1, 1), requires_grad=True)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x.mul_(self.gamma) if self.inplace else x * self.gamma
+    
+
+class Embedding(nn.Module):
+    """
+    Patch Embedding that is implemented by a layer of conv.
+    Input: tensor in shape [B, C, H, W]
+    Output: tensor in shape [B, C, H/stride, W/stride]
+    """
+    def __init__(
+            self,
+            in_chans: int = 3,
+            embed_dim: int = 768,
+            patch_size: int = 16,
+            stride: int = 16,
+            padding: int = 0,
+            norm_layer: LayerType = nn.BatchNorm2d,
+    ):
+        super().__init__()
+        patch_size = to_2tuple(patch_size)
+        stride = to_2tuple(stride)
+        padding = to_2tuple(padding)
+        self.proj = nn.Conv2d(in_chans, embed_dim, patch_size, stride, padding)
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x)
+        x = self.norm(x)
+        return x
+
+
+class ConvEncoder(nn.Module):
+    """
+    Implementation of ConvEncoder with 3*3 and 1*1 convolutions.
+    Input: tensor with shape [B, C, H, W]
+    Output: tensor with shape [B, C, H, W]
+    """
+    def __init__(
+            self, 
+            dim: int,
+            hidden_dim: int = 64,
+            kernel_size: int = 3,
+            drop_path: float = 0.,
+            act_layer: LayerType = nn.GELU,
+            norm_layer: LayerType = nn.BatchNorm2d,
+            use_layer_scale: bool = True,
+    ):
+        super().__init__()
+        self.dwconv = nn.Conv2d(dim, dim, kernel_size, padding=kernel_size // 2, groups=dim)
+        self.norm = norm_layer(dim)
+        self.pwconv1 = nn.Conv2d(dim, hidden_dim, 1)
+        self.act = act_layer()
+        self.pwconv2 = nn.Conv2d(hidden_dim, dim, 1)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.layer_scale = LayerScale2d(dim, 1) if use_layer_scale else nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        input = x
+        x = self.dwconv(x)
+        x = self.norm(x)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.pwconv2(x)
+        x = self.layer_scale(x)
+        x = input + self.drop_path(x)
+        return x
+
+
+class Mlp(nn.Module):
+    """
+    Implementation of MLP layer with 1*1 convolutions.
+    Input: tensor with shape [B, C, H, W]
+    Output: tensor with shape [B, C, H, W]
+    """
+    def __init__(
+            self,
+            in_features: int,
+            hidden_features: Optional[int] = None,
+            out_features: Optional[int] = None,
+            act_layer: LayerType = nn.GELU,
+            norm_layer: LayerType = nn.BatchNorm2d,
+            drop: float = 0.,
+    ):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.norm1 = norm_layer(in_features)
+        self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
+        self.act = act_layer()
+        self.fc2 = nn.Conv2d(hidden_features, out_features, 1)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.norm1(x)
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class EfficientAdditiveAttention(nn.Module):
+    """
+    Efficient Additive Attention module for SwiftFormer.
+    Input: tensor in shape [B, C, H, W]
+    Output: tensor in shape [B, C, H, W]
+    """
+    def __init__(self, in_dims: int = 512, token_dim: int = 256, num_heads: int = 1):
+        super().__init__()
+        self.scale_factor = token_dim ** -0.5
+        self.to_query = nn.Linear(in_dims, token_dim * num_heads)
+        self.to_key = nn.Linear(in_dims, token_dim * num_heads)
+
+        self.w_g = nn.Parameter(torch.randn(token_dim * num_heads, 1))
+        
+        self.proj = nn.Linear(token_dim * num_heads, token_dim * num_heads)
+        self.final = nn.Linear(token_dim * num_heads, token_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, _, H, W = x.shape
+        x = x.flatten(2).permute(0, 2, 1)
+
+        query = F.normalize(self.to_query(x), dim=-1)
+        key = F.normalize(self.to_key(x), dim=-1)
+
+        attn = F.normalize(query @ self.w_g * self.scale_factor, dim=1)
+        attn = torch.sum(attn * query, dim=1, keepdim=True)
+
+        out = self.proj(attn * key) + query
+        out = self.final(out).permute(0, 2, 1).reshape(B, -1, H, W)
+        return out
+
+
+class LocalRepresentation(nn.Module):
+    """
+    Local Representation module for SwiftFormer that is implemented by 3*3 depth-wise and point-wise convolutions.
+    Input: tensor in shape [B, C, H, W]
+    Output: tensor in shape [B, C, H, W]
+    """
+    def __init__(
+            self,
+            dim: int,
+            kernel_size: int = 3,
+            drop_path: float = 0.,
+            use_layer_scale: bool = True,
+            act_layer: LayerType = nn.GELU,
+            norm_layer: LayerType = nn.BatchNorm2d,
+    ):
+        super().__init__()
+        self.dwconv = nn.Conv2d(dim, dim, kernel_size, padding=kernel_size // 2, groups=dim)
+        self.norm = norm_layer(dim)
+        self.pwconv1 = nn.Conv2d(dim, dim, kernel_size=1)
+        self.act = act_layer()
+        self.pwconv2 = nn.Conv2d(dim, dim, kernel_size=1)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.layer_scale = LayerScale2d(dim, 1) if use_layer_scale else nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        skip = x
+        x = self.dwconv(x)
+        x = self.norm(x)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.pwconv2(x)
+        x = self.layer_scale(x)
+        x = skip + self.drop_path(x)
+        return x
+    
+
+class Block(nn.Module):
+    """
+    SwiftFormer Encoder Block for SwiftFormer. It consists of :
+    (1) Local representation module, (2) EfficientAdditiveAttention, and (3) MLP block.
+    Input: tensor in shape [B, C, H, W]
+    Output: tensor in shape [B, C, H, W]
+    """
+    def __init__(
+            self,
+            dim: int,
+            mlp_ratio: float = 4.,
+            drop_rate: float = 0.,
+            drop_path: float = 0.,
+            act_layer: LayerType = nn.GELU,
+            norm_layer: LayerType = nn.BatchNorm2d,
+            use_layer_scale: bool = True,
+            layer_scale_init_value: float = 1e-5,
+    ):
+        super().__init__()
+        self.local_representation = LocalRepresentation(
+            dim=dim, 
+            use_layer_scale=use_layer_scale,
+            act_layer=act_layer,
+            norm_layer=norm_layer,
+        )
+        self.attn = EfficientAdditiveAttention(in_dims=dim, token_dim=dim)
+        self.linear = Mlp(
+            in_features=dim,
+            hidden_features=int(dim * mlp_ratio),
+            act_layer=act_layer,
+            norm_layer=norm_layer, 
+            drop=drop_rate,
+        )
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.layer_scale_1 = LayerScale2d(dim, layer_scale_init_value) \
+            if use_layer_scale else nn.Identity()
+        self.layer_scale_2 = LayerScale2d(dim, layer_scale_init_value) \
+            if use_layer_scale else nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.local_representation(x)
+        x = x + self.drop_path(self.layer_scale_1(self.attn(x)))
+        x = x + self.drop_path(self.layer_scale_2(self.linear(x)))
+        return x
+
+
+class Stage(nn.Module):
+    """
+    Implementation of each SwiftFormer stages. Here, SwiftFormerEncoder used as the last block in all stages, while ConvEncoder used in the rest of the blocks.
+    Input: tensor in shape [B, C, H, W]
+    Output: tensor in shape [B, C, H, W]
+    """
+    def __init__(
+            self,
+            dim: int,
+            index: int,
+            layers: List[int],
+            mlp_ratio: float = 4.,
+            act_layer: LayerType = nn.GELU,
+            norm_layer: LayerType = nn.BatchNorm2d,
+            drop_rate: float = 0.,
+            drop_path_rate: float = 0.,
+            use_layer_scale: bool = True,
+            layer_scale_init_value: float = 1e-5,
+            downsample: Optional[LayerType] = None,
+    ):
+        super().__init__()
+        self.grad_checkpointing = False
+        self.downsample = downsample if downsample is not None else nn.Identity()
+
+        blocks = []
+        for block_idx in range(layers[index]):
+            block_dpr = drop_path_rate * (block_idx + sum(layers[:index])) / (sum(layers) - 1)
+            if layers[index] - block_idx <= 1:
+                blocks.append(Block(
+                    dim, 
+                    mlp_ratio=mlp_ratio,
+                    drop_rate=drop_rate,
+                    drop_path=block_dpr,
+                    act_layer=act_layer,
+                    norm_layer=norm_layer,
+                    use_layer_scale=use_layer_scale,
+                    layer_scale_init_value=layer_scale_init_value,
+                ))
+            else:
+                blocks.append(ConvEncoder(
+                    dim=dim, 
+                    hidden_dim=int(mlp_ratio * dim), 
+                    kernel_size=3,
+                    drop_path=block_dpr,
+                    act_layer=act_layer,
+                    norm_layer=norm_layer,
+                    use_layer_scale=use_layer_scale,
+                ))
+        self.blocks = nn.Sequential(*blocks)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.downsample(x)
+        if self.grad_checkpointing and not torch.jit.is_scripting():
+            x = checkpoint_seq(self.blocks, x, flatten=True)
+        else:
+            x = self.blocks(x)
+        return x
+
+
+class SwiftFormer(nn.Module):
+    def __init__(
+            self,
+            layers: List[int] = [3, 3, 6, 4],
+            embed_dims: List[int] = [48, 56, 112, 220],
+            mlp_ratios: int = 4,
+            downsamples: List[bool] = [False, True, True, True],
+            act_layer: LayerType = nn.GELU,
+            down_patch_size: int = 3,
+            down_stride: int = 2,
+            down_pad: int = 1,
+            num_classes: int = 1000,
+            drop_rate: float = 0.,
+            drop_path_rate: float = 0.,
+            use_layer_scale: bool = True,
+            layer_scale_init_value: float = 1e-5,
+            global_pool: str = 'avg',
+            output_stride: int = 32,
+            in_chans: int = 3,
+            **kwargs,
+    ):
+        super().__init__()
+        assert output_stride == 32
+        self.num_classes = num_classes
+        self.global_pool = global_pool
+        self.feature_info = []
+
+        self.stem = nn.Sequential(
+            nn.Conv2d(in_chans, embed_dims[0] // 2, 3, 2, 1),
+            nn.BatchNorm2d(embed_dims[0] // 2),
+            nn.ReLU(),
+            nn.Conv2d(embed_dims[0] // 2, embed_dims[0], 3, 2, 1),
+            nn.BatchNorm2d(embed_dims[0]),
+            nn.ReLU(),
+        )
+        prev_dim = embed_dims[0]
+
+        stages = []
+        for i in range(len(layers)):
+            downsample = Embedding(
+                in_chans=prev_dim,
+                embed_dim=embed_dims[i],
+                patch_size=down_patch_size,
+                stride=down_stride,
+                padding=down_pad,
+            ) if downsamples[i] else nn.Identity()
+            stage = Stage(
+                dim=embed_dims[i], 
+                index=i, 
+                layers=layers, 
+                mlp_ratio=mlp_ratios,
+                act_layer=act_layer,
+                drop_rate=drop_rate,
+                drop_path_rate=drop_path_rate,
+                use_layer_scale=use_layer_scale,
+                layer_scale_init_value=layer_scale_init_value,
+                downsample=downsample,
+            )
+            prev_dim = embed_dims[i]
+            stages.append(stage)
+            self.feature_info += [dict(num_chs=embed_dims[i], reduction=2**(i+2), module=f'stages.{i}')]
+        self.stages = nn.Sequential(*stages)
+
+        # Classifier head
+        self.num_features  = self.head_hidden_size = out_chs = embed_dims[-1]
+        self.norm = nn.BatchNorm2d(out_chs)
+        self.head_drop = nn.Dropout(drop_rate)
+        self.head = Linear(out_chs, num_classes) if num_classes > 0 else nn.Identity()
+        # assuming model is always distilled (valid for current checkpoints, will split def if that changes)
+        self.head_dist = Linear(out_chs, num_classes) if num_classes > 0 else nn.Identity()
+        self.distilled_training = False  # must set this True to train w/ distillation token
+        self._initialize_weights()
+
+    def _initialize_weights(self):
+        for name, m in self.named_modules():
+            if isinstance(m, nn.Linear):
+                trunc_normal_(m.weight, std=.02)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Conv2d):
+                trunc_normal_(m.weight, std=.02)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    @torch.jit.ignore
+    def no_weight_decay(self) -> Set:
+        return set()
+
+    @torch.jit.ignore
+    def group_matcher(self, coarse: bool = False) -> Dict[str, Any]:
+        matcher = dict(
+            stem=r'^stem',  # stem and embed
+            blocks=[(r'^stages\.(\d+)', None), (r'^norm', (99999,))]
+        )
+        return matcher
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable: bool = True):
+        for s in self.stages:
+            s.grad_checkpointing = enable
+
+    @torch.jit.ignore
+    def get_classifier(self) -> Tuple[nn.Module, nn.Module]:
+        return self.head, self.head_dist
+
+    def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None):
+        self.num_classes = num_classes
+        if global_pool is not None:
+            self.global_pool = global_pool
+        self.head = Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+        self.head_dist = Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+
+    @torch.jit.ignore
+    def set_distilled_training(self, enable: bool = True):
+        self.distilled_training = enable
+    
+    def forward_intermediates(
+            self,
+            x: torch.Tensor,
+            indices: Optional[Union[int, List[int]]] = None,
+            norm: bool = False,
+            stop_early: bool = False,
+            output_fmt: str = 'NCHW',
+            intermediates_only: bool = False,
+    ) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]:
+        """ Forward features that returns intermediates.
+
+        Args:
+            x: Input image tensor
+            indices: Take last n blocks if int, all if None, select matching indices if sequence
+            norm: Apply norm layer to compatible intermediates
+            stop_early: Stop iterating over blocks when last desired intermediate hit
+            output_fmt: Shape of intermediate feature outputs
+            intermediates_only: Only return intermediate features
+        Returns:
+
+        """
+        assert output_fmt in ('NCHW',), 'Output shape must be NCHW.'
+        intermediates = []
+        take_indices, max_index = feature_take_indices(len(self.stages), indices)
+        last_idx = len(self.stages) - 1
+
+        # forward pass
+        x = self.stem(x)
+        if torch.jit.is_scripting() or not stop_early:  # can't slice blocks in torchscript
+            stages = self.stages
+        else:
+            stages = self.stages[:max_index + 1]
+
+        for feat_idx, stage in enumerate(stages):
+            x = stage(x)
+            if feat_idx in take_indices:
+                if norm and feat_idx == last_idx:
+                    x_inter = self.norm(x)  # applying final norm last intermediate
+                else:
+                    x_inter = x
+                intermediates.append(x_inter) 
+
+        if intermediates_only:
+            return intermediates
+
+        x = self.norm(x)
+
+        return x, intermediates
+
+    def prune_intermediate_layers(
+            self,
+            indices: Union[int, List[int]] = 1,
+            prune_norm: bool = False,
+            prune_head: bool = True,
+    ) -> List[int]:
+        """ Prune layers not required for specified intermediates.
+        """
+        take_indices, max_index = feature_take_indices(len(self.stages), indices)
+        self.stages = self.stages[:max_index + 1]  # truncate blocks w/ stem as idx 0
+        if prune_norm:
+            self.norm = nn.Identity()
+        if prune_head:
+            self.reset_classifier(0, '')
+        return take_indices
+
+    def forward_features(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.stem(x)
+        x = self.stages(x)
+        x = self.norm(x)
+        return x
+
+    def forward_head(
+            self,
+            x: torch.Tensor,
+            pre_logits: bool = False,
+    ) -> Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor]:
+        if self.global_pool == 'avg':
+            x = x.mean(dim=(2, 3))
+        x = self.head_drop(x)
+        if pre_logits:
+            return x
+        x, x_dist = self.head(x), self.head_dist(x)
+        if self.distilled_training and self.training and not torch.jit.is_scripting():
+            # only return separate classification predictions when training in distilled mode
+            return x, x_dist
+        else:
+            # during standard train/finetune, inference average the classifier predictions
+            return (x + x_dist) / 2
+
+    def forward(self, x: torch.Tensor) -> Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor]:
+        x = self.forward_features(x)
+        x = self.forward_head(x)
+        return x
+
+
+def checkpoint_filter_fn(state_dict: Dict[str, torch.Tensor], model: nn.Module) -> Dict[str, torch.Tensor]:
+    if 'model' in state_dict:
+        state_dict = state_dict['model']
+
+    out_dict = {}
+    for k, v in state_dict.items():
+        k = k.replace('patch_embed.', 'stem.')
+        k = k.replace('dist_head.', 'head_dist.')
+        k = k.replace('attn.Proj.', 'attn.proj.')
+        k = k.replace('.layer_scale_1', '.layer_scale_1.gamma')
+        k = k.replace('.layer_scale_2', '.layer_scale_2.gamma')
+        k = re.sub(r'\.layer_scale(?=$|\.)', '.layer_scale.gamma', k)
+        m = re.match(r'^network\.(\d+)\.(.*)', k)
+        if m:
+            n_idx, rest = int(m.group(1)), m.group(2)
+            stage_idx = n_idx // 2
+            if n_idx % 2 == 0:
+                k = f'stages.{stage_idx}.blocks.{rest}'
+            else:
+                k = f'stages.{stage_idx+1}.downsample.{rest}'
+
+        out_dict[k] = v
+    return out_dict
+
+
+def _cfg(url: str = '', **kwargs: Any) -> Dict[str, Any]:
+    return {
+        'url': url,
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, 'fixed_input_size': True,
+        'crop_pct': .95, 'interpolation': 'bicubic',
+        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
+        'first_conv': 'stem.0', 'classifier': ('head', 'head_dist'),
+        'paper_ids': 'arXiv:2303.15446',
+        'paper_name': 'SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications',
+        'origin_url': 'https://github.com/Amshaker/SwiftFormer',
+        **kwargs
+    }
+
+
+default_cfgs = generate_default_cfgs({
+    # 'swiftformer_xs.dist_in1k': _cfg(url=''),
+    # 'swiftformer_s.dist_in1k': _cfg(url=''),
+    # 'swiftformer_l1.dist_in1k': _cfg(url=''),
+    # 'swiftformer_l3.dist_in1k': _cfg(url=''),
+    'swiftformer_xs.untrained': _cfg(),
+    'swiftformer_s.untrained': _cfg(),
+    'swiftformer_l1.untrained': _cfg(),
+    'swiftformer_l3.untrained': _cfg(),
+})
+
+
+def _create_swiftformer(variant: str, pretrained: bool = False, **kwargs: Any) -> SwiftFormer:
+    model = build_model_with_cfg(
+        SwiftFormer, variant, pretrained,
+        pretrained_filter_fn=checkpoint_filter_fn,
+        feature_cfg=dict(out_indices=(0, 1, 2, 3), flatten_sequential=True),
+        **kwargs,
+    )
+    return model
+
+
+@register_model
+def swiftformer_xs(pretrained: bool = False, **kwargs: Any) -> SwiftFormer:
+    model_args = dict(layers=[3, 3, 6, 4], embed_dims=[48, 56, 112, 220])
+    return _create_swiftformer('swiftformer_xs', pretrained=pretrained, **dict(model_args, **kwargs))
+
+
+# @register_model
+# def swiftformer_s(pretrained: bool = False, **kwargs: Any) -> SwiftFormer:
+#     model_args = dict(layers=[3, 3, 9, 6], embed_dims=[48, 64, 168, 224])
+#     return _create_swiftformer('swiftformer_s', pretrained=pretrained, **dict(model_args, **kwargs))
+
+# @register_model
+# def swiftformer_l1(pretrained: bool = False, **kwargs: Any) -> SwiftFormer:
+#     model_args = dict(layers=[4, 3, 10, 5], embed_dims=[48, 96, 192, 384])
+#     return _create_swiftformer('swiftformer_l1', pretrained=pretrained, **dict(model_args, **kwargs))
+
+
+# @register_model
+# def swiftformer_l3(pretrained: bool = False, **kwargs: Any) -> SwiftFormer:
+#     model_args = dict(layers=[4, 4, 12, 6], embed_dims=[64, 128, 320, 512])
+#     return _create_swiftformer('swiftformer_l3', pretrained=pretrained, **dict(model_args, **kwargs))
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