diff --git a/mlx_vlm/models/phi3_v/__init__.py b/mlx_vlm/models/phi3_v/__init__.py
new file mode 100644
index 0000000..6e0acf1
--- /dev/null
+++ b/mlx_vlm/models/phi3_v/__init__.py
@@ -0,0 +1,8 @@
+from .phi3_v import (
+    LanguageModel,
+    Model,
+    ModelConfig,
+    TextConfig,
+    VisionConfig,
+    VisionModel,
+)
diff --git a/mlx_vlm/models/phi3_v/language.py b/mlx_vlm/models/phi3_v/language.py
new file mode 100644
index 0000000..f2401bf
--- /dev/null
+++ b/mlx_vlm/models/phi3_v/language.py
@@ -0,0 +1,19 @@
+from dataclasses import dataclass
+import inspect
+
+
+@dataclass
+class TextConfig:
+    @classmethod
+    def from_dict(cls, params):
+        return cls(
+            **{
+                k: v
+                for k, v in params.items()
+                if k in inspect.signature(cls).parameters
+            }
+        )
+
+
+class LanguageModel:
+    pass
diff --git a/mlx_vlm/models/phi3_v/phi3_v.py b/mlx_vlm/models/phi3_v/phi3_v.py
new file mode 100644
index 0000000..5fb2c32
--- /dev/null
+++ b/mlx_vlm/models/phi3_v/phi3_v.py
@@ -0,0 +1,235 @@
+import inspect
+import math
+from dataclasses import dataclass
+from typing import Dict, Optional, Tuple, Union
+from types import SimpleNamespace
+from typing import Optional
+
+import mlx.core as mx
+import mlx.nn as nn
+import numpy as np
+
+from .su_rope import Phi3SuScaledRotaryEmbedding
+from .language import TextConfig, LanguageModel
+from .vision import VisionConfig, VisionModel
+
+
+@dataclass
+class ModelConfig:
+    text_config: TextConfig
+    vision_config: VisionConfig
+    model_type: str
+    vocab_size: int
+
+    num_hidden_layers: int
+    intermediate_size: int
+    num_attention_heads: int
+    rms_norm_eps: float
+
+    ignore_index: int = -100
+    image_token_index: int = 257152
+    hidden_size: int = 2048
+    pad_token_id: int = 0
+
+    num_key_value_heads: int = None
+    rope_theta: float = 10000
+    rope_traditional: bool = False
+    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
+    max_position_embeddings: int = 131072
+    original_max_position_embeddings: int = 4096
+
+    @classmethod
+    def from_dict(cls, params):
+        return cls(
+            **{
+                k: v
+                for k, v in params.items()
+                if k in inspect.signature(cls).parameters
+            }
+        )
+
+
+class Attention(nn.Module):
+    def __init__(self, args: TextConfig):
+        super().__init__()
+
+        dim = args.hidden_size
+        self.n_heads = n_heads = args.num_attention_heads
+        self.n_kv_heads = n_kv_heads = args.num_key_value_heads
+        self.num_hidden_layers = args.num_hidden_layers
+
+        self.head_dim = head_dim = args.hidden_size // n_heads
+        self.scale = head_dim**-0.5
+
+        op_size = n_heads * head_dim + 2 * (n_kv_heads * head_dim)
+        self.qkv_proj = nn.Linear(dim, op_size, bias=False)
+        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)
+
+        rope_scale = 1.0
+        if args.rope_scaling and args.rope_scaling["type"] == "su":
+            self.rope = Phi3SuScaledRotaryEmbedding(
+                head_dim,
+                traditional=False,
+                base=args.rope_theta,
+                scale=rope_scale,
+                max_position_embeddings=args.max_position_embeddings,
+                original_max_position_embeddings=args.original_max_position_embeddings,
+                short_factor=args.rope_scaling["short_factor"],
+                long_factor=args.rope_scaling["long_factor"],
+            )
+        else:
+            if args.rope_scaling and args.rope_scaling["type"] == "linear":
+                rope_scale = 1 / args.rope_scaling["factor"]
+            self.rope = nn.RoPE(
+                head_dim,
+                traditional=args.rope_traditional,
+                base=args.rope_theta,
+                scale=rope_scale,
+            )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Tuple[mx.array, mx.array]] = None,
+    ) -> mx.array:
+        B, L, D = x.shape
+
+        qkv = self.qkv_proj(x)
+        query_pos = self.n_heads * self.head_dim
+        queries, keys, values = mx.split(
+            qkv, [query_pos, query_pos + self.n_kv_heads * self.head_dim], axis=-1
+        )
+
+        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
+        keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
+        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
+
+        if cache is not None:
+            offset = cache[0].shape[2]
+            queries = self.rope(queries, offset=offset)
+            keys = self.rope(keys, offset=offset)
+            keys = mx.concatenate([cache[0], keys], axis=2)
+            values = mx.concatenate([cache[1], values], axis=2)
+        else:
+            queries = self.rope(queries)
+            keys = self.rope(keys)
+
+        output = mx.fast.scaled_dot_product_attention(
+            queries, keys, values, scale=self.scale, mask=mask
+        )
+        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
+        return self.o_proj(output), (keys, values)
+
+
+class MLP(nn.Module):
+    def __init__(self, dim, hidden_dim):
+        super().__init__()
+        self.gate_up_proj = nn.Linear(dim, 2 * hidden_dim, bias=False)
+        self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
+
+    def __call__(self, x) -> mx.array:
+        x = self.gate_up_proj(x)
+        gate, x = mx.split(x, 2, axis=-1)
+        return self.down_proj(nn.silu(gate) * x)
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, args: TextConfig):
+        super().__init__()
+        self.num_attention_heads = args.num_attention_heads
+        self.hidden_size = args.hidden_size
+        self.self_attn = Attention(args)
+        self.mlp = MLP(args.hidden_size, args.intermediate_size)
+        self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+        self.post_attention_layernorm = nn.RMSNorm(
+            args.hidden_size, eps=args.rms_norm_eps
+        )
+        self.args = args
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Tuple[mx.array, mx.array]] = None,
+    ) -> mx.array:
+        r, cache = self.self_attn(self.input_layernorm(x), mask, cache)
+        h = x + r
+        r = self.mlp(self.post_attention_layernorm(h))
+        out = h + r
+        return out, cache
+
+
+class Phi3V(nn.Module):
+    def __init__(self, args: TextConfig):
+        super().__init__()
+        self.args = args
+        self.vocab_size = args.vocab_size
+        self.num_hidden_layers = args.num_hidden_layers
+        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
+        self.vision_embed_tokens = VisionModel(args)
+        self.layers = [
+            TransformerBlock(args=args) for _ in range(args.num_hidden_layers)
+        ]
+        self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        pixel_values=None,
+        image_sizes=None,
+        cache=None,
+    ):
+        # print('inputs', inputs) # debug
+        h = self.embed_tokens(inputs)
+        p = np.argwhere(inputs < 0).tolist()
+        if pixel_values is not None:
+            h = self.vision_embed_tokens(pixel_values, h, image_sizes, p)
+        mask = None
+        if h.shape[1] > 1:
+            mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
+            mask = mask.astype(h.dtype)
+        if cache is None:
+            cache = [None] * len(self.layers)
+        for i, layer in enumerate(self.layers):
+            h, cache[i] = layer(h, mask, cache[i])
+        return self.norm(h), cache
+
+
+class Model(nn.Module):
+    def __init__(self, args: TextConfig):
+        super().__init__()
+        self.model_type = args.model_type
+        self.model = Phi3V(args)
+        self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
+        self.config = args
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        pixel_values=None,
+        mask=None,
+        cache=None,
+    ):
+        out, cache = self.model(inputs, pixel_values, mask, cache)
+        return self.lm_head(out).astype(self.lm_head.weight.dtype), cache
+
+    @property
+    def layers(self):
+        return self.model.layers
+
+    @property
+    def head_dim(self):
+        return self.args.hidden_size // self.args.num_attention_heads
+
+    @property
+    def n_kv_heads(self):
+        return self.args.num_key_value_heads
+
+    @property
+    def language_model(self):
+        return self
+
+    @property
+    def vision_model(self):
+        return self.model.vision_embed_tokens
diff --git a/mlx_vlm/models/phi3_v/su_rope.py b/mlx_vlm/models/phi3_v/su_rope.py
new file mode 100644
index 0000000..83dbdfe
--- /dev/null
+++ b/mlx_vlm/models/phi3_v/su_rope.py
@@ -0,0 +1,70 @@
+import math
+import mlx.core as mx
+
+
+class Phi3SuScaledRotaryEmbedding:
+    def __init__(
+        self,
+        dims: int,
+        traditional: bool = False,
+        base: float = 10000.0,
+        scale: float = 1.0,
+        max_position_embeddings: int = 131072,
+        original_max_position_embeddings: int = 4096,
+        short_factor: list[float] | float = 1.0,
+        long_factor: list[float] | float = 1.0,
+    ):
+        """
+        Phi3Su Scaled Rotary Embedding layer for Phi-3 models.
+
+        Args:
+            dims (int): The feature dimensions to be rotated.
+            traditional (bool, optional): Unused. Default: ``False``.
+            base (int, optional): Base for the exponential scaling.
+            scale (float, optional): The scale used to scale the positions. Default: 1.0.
+            max_position_embeddings (int, optional): The maximum sequence length that this model was trained with. This is used to determine the size of the original RoPE embeddings when using long scaling. Default: 131072.
+            original_max_position_embeddings (int, optional): The maximum sequence length that this model was trained with. This is used to determine the size of the original RoPE embeddings when using long scaling. Default: 4096.
+            short_factor (float or list of floats, optional): List of scaling factors for sequences of length lesser than original_max_position_embeddings. Default: 1.0.
+            long_factor (float or list of floats, optional): List of scaling factors for sequences of length greater than original_max_position_embeddings.  Default: 1.0.
+        """
+        self.inv_freq_short = 1.0 / (
+            mx.array(short_factor, dtype=mx.float32)
+            * base ** (mx.arange(0, dims, 2, dtype=mx.float32) / dims)
+        )
+        self.inv_freq_long = 1.0 / (
+            scale
+            * mx.array(long_factor, dtype=mx.float32)
+            * base ** (mx.arange(0, dims, 2, dtype=mx.float32) / dims)
+        )
+        self.original_max_position_embeddings = original_max_position_embeddings
+        self.scaling_factor = math.sqrt(
+            1
+            + math.log(max_position_embeddings / original_max_position_embeddings)
+            / math.log(original_max_position_embeddings)
+        )
+
+    def _get_cos_sin(self, offset, L):
+        position_ids = mx.arange(offset, offset + L, dtype=mx.float32)[None]
+        inv_freq = (
+            self.inv_freq_long
+            if position_ids.max() + 1 > self.original_max_position_embeddings
+            else self.inv_freq_short
+        )
+        inv_freq_expanded = mx.repeat(
+            inv_freq[None, :, None], position_ids.shape[0], axis=0
+        )
+        position_ids_expanded = position_ids[:, None, :]
+        freqs = (inv_freq_expanded @ position_ids_expanded).transpose(0, 2, 1)
+        emb = mx.concatenate([freqs, freqs], axis=-1)
+        cos = mx.cos(emb) * self.scaling_factor
+        sin = mx.sin(emb) * self.scaling_factor
+        return mx.expand_dims(cos, axis=1), mx.expand_dims(sin, axis=1)
+
+    def __call__(self, x, offset: int = 0):
+        def _rotate_half(_x):
+            midpoint = _x.shape[-1] // 2
+            x1, x2 = _x[..., :midpoint], _x[..., midpoint:]
+            return mx.concatenate([-x2, x1], axis=-1)
+
+        cos, sin = self._get_cos_sin(offset, x.shape[2])
+        return (x * cos) + (_rotate_half(x) * sin)
diff --git a/mlx_vlm/models/phi3_v/vision.py b/mlx_vlm/models/phi3_v/vision.py
new file mode 100644
index 0000000..f832e2d
--- /dev/null
+++ b/mlx_vlm/models/phi3_v/vision.py
@@ -0,0 +1,325 @@
+import inspect
+import math
+from dataclasses import dataclass
+from typing import Optional
+from types import SimpleNamespace
+
+import mlx.core as mx
+import mlx.nn as nn
+import numpy as np
+
+
+@dataclass
+class VisionConfig:
+    model_type: str = "phi3_v"
+    num_hidden_layers: int = 24
+    hidden_size: int = 1024
+    intermediate_size: int = 4096
+    num_attention_heads: int = 16
+    image_size: int = 336
+    patch_size: int = 14
+    projection_dim: int = 768
+    vocab_size: int = 32000
+    num_channels: int = 3
+    layer_norm_eps: float = 1e-5
+    image_dim_out: int = (1024,)
+    model_name: str = "openai/clip-vit-large-patch14-336"
+    name: str = "clip_vision_model"
+    num_img_tokens: int = 144
+
+    @classmethod
+    def from_dict(cls, params):
+        return cls(
+            **{
+                k: v
+                for k, v in params.items()
+                if k in inspect.signature(cls).parameters
+            }
+        )
+
+
+def check_array_shape(arr):
+    shape = arr.shape
+
+    # Check if the shape has 4 dimensions
+    if len(shape) != 4:
+        return False
+
+    out_channels, kH, KW, _ = shape
+
+    # Check if out_channels is the largest, and kH and KW are the same
+    if (out_channels >= kH) and (out_channels >= KW) and (kH == KW):
+        return True
+    else:
+        return False
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dims: int,
+        num_heads: int,
+        query_input_dims: Optional[int] = None,
+        key_input_dims: Optional[int] = None,
+        value_input_dims: Optional[int] = None,
+        value_dims: Optional[int] = None,
+        value_output_dims: Optional[int] = None,
+        bias: bool = False,
+    ):
+        super().__init__()
+
+        if (dims % num_heads) != 0:
+            raise ValueError(
+                "The input feature dimensions should be divisible by the "
+                f"number of heads ({dims} % {num_heads}) != 0"
+            )
+
+        query_input_dims = query_input_dims or dims
+        key_input_dims = key_input_dims or dims
+        value_input_dims = value_input_dims or key_input_dims
+        value_dims = value_dims or dims
+        value_output_dims = value_output_dims or dims
+
+        self.num_heads = num_heads = num_heads
+        head_dim = dims // num_heads
+        self.scale = head_dim**-0.5
+
+        self.q_proj = nn.Linear(query_input_dims, dims, bias=bias)
+        self.k_proj = nn.Linear(key_input_dims, dims, bias=bias)
+        self.v_proj = nn.Linear(value_input_dims, value_dims, bias=bias)
+        self.out_proj = nn.Linear(value_dims, value_output_dims, bias=bias)
+
+    def __call__(self, queries, keys, values, mask=None):
+        queries = self.q_proj(queries)
+        keys = self.k_proj(keys)
+        values = self.v_proj(values)
+
+        num_heads = self.num_heads
+        B, L, D = queries.shape
+        _, S, _ = keys.shape
+        queries = queries.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
+        keys = keys.reshape(B, S, num_heads, -1).transpose(0, 2, 1, 3)
+        values = values.reshape(B, S, num_heads, -1).transpose(0, 2, 1, 3)
+
+        output = mx.fast.scaled_dot_product_attention(
+            queries, keys, values, scale=self.scale, mask=mask
+        )
+        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
+
+        return self.out_proj(output)
+
+
+class MLP(nn.Module):
+    def __init__(self, config: VisionConfig):
+        super().__init__()
+        self.activation_fn = nn.GELU(approx="fast")
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+    def __call__(self, x: mx.array) -> mx.array:
+        x = self.activation_fn(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+
+class EncoderLayer(nn.Module):
+    def __init__(self, config: VisionConfig):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.self_attn = Attention(
+            config.hidden_size, config.num_attention_heads, bias=True
+        )
+        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+        self.mlp = MLP(config)
+        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+    def __call__(self, x: mx.array, mask: Optional[mx.array] = None) -> mx.array:
+        y = self.layer_norm1(x)
+        y = self.self_attn(y, y, y, mask)
+        x = x + y
+        y = self.layer_norm2(x)
+        y = self.mlp(y)
+        return x + y
+
+
+class Encoder(nn.Module):
+    def __init__(self, config: VisionConfig):
+        super().__init__()
+        self.layers = [EncoderLayer(config) for _ in range(config.num_hidden_layers)]
+
+
+class VisionEmbeddings(nn.Module):
+    def __init__(self, config: VisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.class_embedding = mx.zeros((config.hidden_size,))
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=config.num_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            bias=False,
+        )
+
+        self.num_patches = (self.image_size // self.patch_size) ** 2
+        self.num_positions = self.num_patches + 1
+        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
+
+    def __call__(self, x: mx.array) -> mx.array:
+        batch_size = x.shape[0]
+        patch_embeddings = self.patch_embedding(x)
+        patch_embeddings = mx.flatten(patch_embeddings, start_axis=1, end_axis=2)
+        embed_dim = patch_embeddings.shape[-1]
+        cls_embeddings = mx.broadcast_to(
+            self.class_embedding, (batch_size, 1, embed_dim)
+        )
+        position_ids = mx.array(np.arange(self.num_positions)[None, :])
+
+        embeddings = mx.concatenate((cls_embeddings, patch_embeddings), axis=1)
+        embeddings += self.position_embedding(position_ids)
+        return embeddings
+
+
+class ClipModel(nn.Module):
+    def __init__(self, config: VisionConfig):
+        super().__init__()
+        self.model_type = config.model_type
+        self.embeddings = VisionEmbeddings(config)
+        self.pre_layrnorm = nn.LayerNorm(config.hidden_size)
+        self.encoder = Encoder(config)
+        self.post_layernorm = nn.LayerNorm(config.hidden_size)
+
+    def __call__(
+        self,
+        x: mx.array,
+        output_hidden_states: Optional[bool] = None,
+    ) -> mx.array:
+        x = self.embeddings(x)
+        x = self.pre_layrnorm(x)
+
+        encoder_states = (x,) if output_hidden_states else None
+
+        for l in self.encoder.layers:
+            x = l(x, mask=None)
+            if output_hidden_states:
+                encoder_states = encoder_states + (x,)
+
+        pooler_output = self.post_layernorm(x[:, 0, :])
+        return pooler_output, x, encoder_states
+
+
+class ClipVModel(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.model_type = config.model_type
+        self.vision_model = ClipModel(config)
+
+
+class VisionModel(nn.Module):
+    CLIP_VIT_LARGE_PATCH14_336_CONFIG = SimpleNamespace(
+        model_type="phi3_v",
+        hidden_size=1024,
+        image_size=336,
+        intermediate_size=4096,
+        layer_norm_eps=1e-05,
+        num_attention_heads=16,
+        num_channels=3,
+        num_hidden_layers=24,
+        patch_size=14,
+    )
+
+    def __init__(self, config):
+        super().__init__()
+        self.model_type = config.model_type
+        self.img_processor = ClipVModel(self.CLIP_VIT_LARGE_PATCH14_336_CONFIG)
+        self.image_dim_out = image_dim_out = 1024
+        self.glb_GN = mx.zeros([1, 1, image_dim_out * 4])
+        self.sub_GN = mx.zeros([1, 1, 1, image_dim_out * 4])
+        self.img_projection = [
+            nn.Linear(image_dim_out * 4, config.hidden_size),
+            nn.GELU(),
+            nn.Linear(config.hidden_size, config.hidden_size),
+        ]
+
+    def __call__(
+        self,
+        img_embeds,
+        txt_embeds=None,
+        img_sizes=None,
+        positions=None,
+        output_hidden_states=None,
+    ):
+        if output_hidden_states:
+            return self.img_processor.vision_model(
+                img_embeds, output_hidden_states=output_hidden_states
+            )
+        # print(0, txt_embeds.shape, img_embeds.shape, img_sizes.shape)
+        img_embeds = mx.array(img_embeds)
+        img_sizes = mx.array(img_sizes)
+        B = img_embeds.shape[0]
+        img_sizes = (img_sizes // 336).tolist()
+        img_features = self.img_processor.vision_model(
+            img_embeds.reshape(-1, *img_embeds.shape[2:]).transpose(0, 2, 3, 1), True
+        )[-1][-2][:, 1:]
+        img_features = img_features.reshape(B, -1, *img_features.shape[1:])
+        C, H = self.image_dim_out, int(img_features.shape[2] ** 0.5)
+        output_imgs, output_len = [], []
+        for _bs in range(B):
+            h, w = img_sizes[_bs]
+            B_ = h * w
+
+            def _reshape_and_concatenate(img, shape, tile_shape):
+                return mx.concatenate(
+                    [
+                        img.reshape(shape)
+                        .transpose(0, 1, 3, 2, 4, 5)
+                        .reshape(tile_shape),
+                        mx.tile(self.sub_GN, (1, tile_shape[1], 1, 1)),
+                    ],
+                    axis=2,
+                ).reshape(1, -1, 4 * C)
+
+            glb_img = _reshape_and_concatenate(
+                img_features[_bs, :1],
+                (1, H // 2, 2, H // 2, 2, C),
+                (1, H // 2, H // 2, 4 * C),
+            )
+            sub_img = _reshape_and_concatenate(
+                img_features[_bs, 1 : B_ + 1],
+                (B_, H // 2, 2, H // 2, 2, C),
+                (1, h * 12, w * 12, 4 * C),
+            )
+            x = mx.concatenate([sub_img, self.glb_GN, glb_img], axis=1)
+            for l in self.img_projection:
+                x = l(x)
+            output_imgs.append(np.array(x.astype(mx.float32)))
+            output_len.append(int((h * w + 1) * 144 + 1 + (h + 1) * 12))
+        idx = 0
+        txt_embeds = np.array(txt_embeds.astype(mx.float32))
+        for i, cnt in enumerate(output_len):
+            txt_embeds[
+                positions[idx][0], positions[idx][1] : positions[idx][1] + cnt
+            ] = output_imgs[i]
+            idx += cnt
+        txt_embeds = mx.array(txt_embeds)
+        return txt_embeds
+
+    def sanitize(self, weights):
+        sanitized_weights = {}
+        for k, v in weights.items():
+            if "position_ids" in k:
+                continue
+            elif "patch_embedding.weight" in k:
+                if check_array_shape(v):
+                    sanitized_weights[k] = v
+                else:
+                    sanitized_weights[k] = v.transpose(0, 2, 3, 1)
+            else:
+                sanitized_weights[k] = v
+
+        return sanitized_weights
diff --git a/mlx_vlm/tests/test_models.py b/mlx_vlm/tests/test_models.py
index 613995d..a709ca8 100644
--- a/mlx_vlm/tests/test_models.py
+++ b/mlx_vlm/tests/test_models.py
@@ -468,6 +468,156 @@ def test_multi_modality(self):
             args.vision_config.num_channels,
             (args.vision_config.image_size, args.vision_config.image_size),
         )
+    def test_phi3_v(self):
+        from mlx_vlm.models import phi3_v
+
+        text_config = phi3_v.TextConfig()
+
+        vision_config = phi3_v.VisionConfig(
+            model_type="phi3_v",
+            image_dim_out=1024,
+            model_name="openai/clip-vit-large-patch14-336",
+            name="clip_vision_model",
+            num_img_tokens=144,
+        )
+
+        args = phi3_v.ModelConfig(
+            text_config=text_config,
+            vision_config=vision_config,
+            **{
+                "hidden_size": 3072,
+                "intermediate_size": 8192,
+                "max_position_embeddings": 131072,
+                "model_type": "phi3_v",
+                "num_attention_heads": 32,
+                "num_hidden_layers": 32,
+                "num_key_value_heads": 32,
+                "original_max_position_embeddings": 4096,
+                "rms_norm_eps": 1e-05,
+                "rope_scaling": {
+                    "long_factor": [
+                        1.0299999713897705,
+                        1.0499999523162842,
+                        1.0499999523162842,
+                        1.0799999237060547,
+                        1.2299998998641968,
+                        1.2299998998641968,
+                        1.2999999523162842,
+                        1.4499999284744263,
+                        1.5999999046325684,
+                        1.6499998569488525,
+                        1.8999998569488525,
+                        2.859999895095825,
+                        3.68999981880188,
+                        5.419999599456787,
+                        5.489999771118164,
+                        5.489999771118164,
+                        9.09000015258789,
+                        11.579999923706055,
+                        15.65999984741211,
+                        15.769999504089355,
+                        15.789999961853027,
+                        18.360000610351562,
+                        21.989999771118164,
+                        23.079999923706055,
+                        30.009998321533203,
+                        32.35000228881836,
+                        32.590003967285156,
+                        35.56000518798828,
+                        39.95000457763672,
+                        53.840003967285156,
+                        56.20000457763672,
+                        57.95000457763672,
+                        59.29000473022461,
+                        59.77000427246094,
+                        59.920005798339844,
+                        61.190006256103516,
+                        61.96000671386719,
+                        62.50000762939453,
+                        63.3700065612793,
+                        63.48000717163086,
+                        63.48000717163086,
+                        63.66000747680664,
+                        63.850006103515625,
+                        64.08000946044922,
+                        64.760009765625,
+                        64.80001068115234,
+                        64.81001281738281,
+                        64.81001281738281,
+                    ],
+                    "short_factor": [
+                        1.05,
+                        1.05,
+                        1.05,
+                        1.1,
+                        1.1,
+                        1.1,
+                        1.2500000000000002,
+                        1.2500000000000002,
+                        1.4000000000000004,
+                        1.4500000000000004,
+                        1.5500000000000005,
+                        1.8500000000000008,
+                        1.9000000000000008,
+                        2.000000000000001,
+                        2.000000000000001,
+                        2.000000000000001,
+                        2.000000000000001,
+                        2.000000000000001,
+                        2.000000000000001,
+                        2.000000000000001,
+                        2.000000000000001,
+                        2.000000000000001,
+                        2.000000000000001,
+                        2.000000000000001,
+                        2.000000000000001,
+                        2.000000000000001,
+                        2.000000000000001,
+                        2.000000000000001,
+                        2.000000000000001,
+                        2.000000000000001,
+                        2.000000000000001,
+                        2.000000000000001,
+                        2.1000000000000005,
+                        2.1000000000000005,
+                        2.2,
+                        2.3499999999999996,
+                        2.3499999999999996,
+                        2.3499999999999996,
+                        2.3499999999999996,
+                        2.3999999999999995,
+                        2.3999999999999995,
+                        2.6499999999999986,
+                        2.6999999999999984,
+                        2.8999999999999977,
+                        2.9499999999999975,
+                        3.049999999999997,
+                        3.049999999999997,
+                        3.049999999999997,
+                    ],
+                    "type": "su",
+                },
+                "rope_theta": 10000.0,
+                "vocab_size": 32064,
+            },
+        )
+
+        model = phi3_v.Model(args)
+
+        self.language_test_runner(
+            model.language_model,
+            args.model_type,
+            args.vocab_size,
+            args.num_hidden_layers,
+        )
+
+        self.vision_test_runner(
+            model.vision_model,
+            args.vision_config.model_type,
+            args.vision_config.hidden_size,
+            args.vision_config.num_channels,
+            (args.vision_config.image_size, args.vision_config.image_size),
+        )
 
 
 if __name__ == "__main__":
diff --git a/mlx_vlm/utils.py b/mlx_vlm/utils.py
index 1b44586..42998a1 100644
--- a/mlx_vlm/utils.py
+++ b/mlx_vlm/utils.py
@@ -159,6 +159,9 @@ def load_model(model_path: Path, lazy: bool = False) -> nn.Module:
         config["text_config"] = text_config
     if model_type == "idefics2":
         config = AutoConfig.from_pretrained(model_path).to_dict()
+    if model_type == "phi3_v":
+        config["vision_config"] = config['img_processor']
+        config["text_config"] = {}
 
     model_config = model_class.ModelConfig.from_dict(config)
 
@@ -705,6 +708,8 @@ def prepare_inputs(image_processor, processor, image, prompt, image_token_index)
         pixel_values = mx.array(inputs["pixel_values"])
         input_ids = mx.array(inputs["input_ids"])
         mask = mx.array(inputs["attention_mask"])
+        if 'image_sizes' in inputs:
+            return input_ids, pixel_values, inputs['image_sizes']
     return input_ids, pixel_values, mask