Upload Phi4ForCausalLMV

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
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+[More Information Needed]
+
+**APA:**
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+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
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+[More Information Needed]
+
+## Model Card Contact
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+[More Information Needed]

config.json

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+  ],
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+    "num_hidden_layers": 27,
+    "patch_size": 16
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+  "vocab_size": 100352
+}

generation_config.json

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+  "pad_token_id": 100349,
+  "temperature": 0.8,
+  "top_p": 0.95,
+  "transformers_version": "5.3.0"
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+}

modeling_phi4_visionr.py

@@ -0,0 +1,1026 @@
+"""
+Minimal self-contained Phi4-Siglip model implementation.
+
+This module provides:
+- Phi4VisionR: Configuration class
+- Phi4ForCausalLMV: Main vision-language model
+- SiglipVisionTower: Vision encoder (standard SigLIP)
+- Siglip2VisionTower: Vision encoder with NaFlex (variable token count)
+- MLP Projector: Vision-to-language projection
+"""
+
+import logging
+import os
+import re
+import math
+from abc import ABC, abstractmethod
+from typing import List, Optional, Tuple, Union
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+from safetensors.torch import load_file
+
+logger = logging.getLogger(__name__)
+from transformers import (
+    AutoConfig,
+    AutoModelForCausalLM,
+    Phi3Config,
+    Phi3Model,
+    Phi3ForCausalLM,
+    SiglipVisionModel,
+    SiglipVisionConfig,
+    SiglipImageProcessor,
+    Siglip2VisionModel,
+    Siglip2VisionConfig,
+    BatchFeature,
+)
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.processing_utils import ImagesKwargs
+import transformers.models.siglip2.image_processing_siglip2 as siglip2_ips
+
+
+# =============================================================================
+# Constants
+# =============================================================================
+
+IGNORE_INDEX = -100
+IMAGE_TOKEN_INDEX = -200
+DEFAULT_IMAGE_TOKEN = "<image>"
+
+
+# =============================================================================
+# Model Arguments (simplified dataclass for initialization)
+# =============================================================================
+
+@dataclass
+class ModelArguments:
+    """Arguments for model initialization."""
+    vision_tower: Optional[str] = None
+    vision_tower_path: Optional[str] = None
+    mm_projector_type: str = "mlp2x_gelu"
+    pretrain_mm_mlp_adapter: Optional[str] = None
+    use_s2: bool = False
+    s2_scales: str = "384,768,1152"
+    hf_cache_dir: Optional[str] = None
+    # NaFlex-specific
+    min_num_patches: int = 256
+    max_num_patches: int = 3600
+    # Embedded vision config (to avoid network calls)
+    vision_config: Optional[dict] = None
+
+
+# =============================================================================
+# Vision Projector (MLP)
+# =============================================================================
+
+def build_vision_projector(config):
+    """Build vision-to-language projector based on config."""
+    projector_type = getattr(config, 'mm_projector_type', 'mlp2x_gelu')
+
+    if projector_type == 'linear':
+        return nn.Linear(config.mm_hidden_size, config.hidden_size)
+
+    elif projector_type.startswith('mlp'):
+        mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
+        if mlp_gelu_match:
+            mlp_depth = int(mlp_gelu_match.group(1))
+            modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
+            for _ in range(1, mlp_depth):
+                modules.append(nn.GELU())
+                modules.append(nn.Linear(config.hidden_size, config.hidden_size))
+            return nn.Sequential(*modules)
+
+    elif projector_type == 'identity':
+        return nn.Identity()
+
+    raise ValueError(f'Unknown projector type: {projector_type}')
+
+
+# =============================================================================
+# Vision Encoders - SigLIP
+# =============================================================================
+
+class SiglipVisionTower(nn.Module):
+    """Standard SigLIP vision encoder with fixed token count."""
+    
+    def __init__(self, vision_tower: str, args: ModelArguments = None, delay_load: bool = False):
+        super().__init__()
+
+        self.is_loaded = False
+        self.vision_tower_name = vision_tower
+        self.vision_tower_path = None
+        self.select_layer = -2
+
+        self.hf_hub_cache_dir = None
+        self.local_files_only = False
+
+        if args and getattr(args, 'hf_cache_dir', None):
+            self.hf_hub_cache_dir = args.hf_cache_dir
+            self.local_files_only = True
+        
+        # Load or create vision config once (avoids network calls if embedded config provided)
+        vision_config_dict = getattr(args, "vision_config", None) if args else None
+        if vision_config_dict is not None:
+            self._vision_config = SiglipVisionConfig(**vision_config_dict)
+        else:
+            self._vision_config = SiglipVisionConfig.from_pretrained(
+                self.vision_tower_name,
+                local_files_only=self.local_files_only,
+                cache_dir=self.hf_hub_cache_dir,
+            )
+            
+        if not delay_load:
+            self.load_model()
+
+    def load_model(self):
+        if self.is_loaded:
+            return
+
+        # Create image processor
+        self.image_processor = SiglipImageProcessor(
+            size={"height": self._vision_config.image_size, "width": self._vision_config.image_size},
+        )
+        self.image_processor.crop_size = self.image_processor.size
+
+        vision_tower_path = self.vision_tower_path if self.vision_tower_path else self.vision_tower_name
+        self.vision_tower = SiglipVisionModel.from_pretrained(
+            vision_tower_path,
+            config=self._vision_config,
+            local_files_only=self.local_files_only,
+            cache_dir=self.hf_hub_cache_dir,
+        )
+
+        self.vision_tower.requires_grad_(False)
+        self.is_loaded = True
+
+    def feature_select(self, image_forward_outs):
+        return image_forward_outs.hidden_states[self.select_layer]
+
+    def forward(self, images):
+        if isinstance(images, list):
+            image_features = []
+            for image in images:
+                image_forward_out = self.vision_tower(
+                    image.to(device=self.device, dtype=self.dtype).unsqueeze(0),
+                    output_hidden_states=True
+                )
+                image_feature = self.feature_select(image_forward_out).to(image.dtype)
+                image_features.append(image_feature)
+        else:
+            image_forward_outs = self.vision_tower(
+                images.to(device=self.device, dtype=self.dtype),
+                output_hidden_states=True
+            )
+            image_features = self.feature_select(image_forward_outs).to(images.dtype)
+
+        return image_features
+
+    @property
+    def dummy_feature(self):
+        return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
+
+    @property
+    def dtype(self):
+        return self.vision_tower.dtype
+
+    @property
+    def device(self):
+        return self.vision_tower.device
+
+    @property
+    def config(self):
+        return self.vision_tower.config if self.is_loaded else self._vision_config
+
+    @property
+    def hidden_size(self):
+        return self.config.hidden_size
+
+    @property
+    def num_patches(self):
+        return (self.config.image_size // self.config.patch_size) ** 2
+
+
+# =============================================================================
+# Vision Encoders - SigLIP2 with NaFlex (variable token count)
+# =============================================================================
+
+class Siglip2ImageProcessorKwargsNoUpscale(ImagesKwargs, total=False):
+    patch_size: int
+    max_num_patches: int
+    min_num_patches: int
+
+
+class Siglip2ImageProcessorNoUpscale(siglip2_ips.Siglip2ImageProcessor):
+    """Custom SigLIP2 image processor that doesn't upscale small images."""
+    
+    model_input_names = ["pixel_values", "pixel_attention_mask", "spatial_shapes"]
+    valid_kwargs = Siglip2ImageProcessorKwargsNoUpscale
+
+    def __init__(
+        self,
+        do_resize: bool = True,
+        resample = siglip2_ips.PILImageResampling.BILINEAR,
+        do_rescale: bool = True,
+        rescale_factor: float = 1 / 255,
+        do_normalize: bool = True,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_convert_rgb: Optional[bool] = None,
+        patch_size: int = 16,
+        max_num_patches: int = 256,
+        min_num_patches: int = 1,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        image_mean = image_mean if image_mean is not None else [0.5, 0.5, 0.5]
+        image_std = image_std if image_std is not None else [0.5, 0.5, 0.5]
+
+        self.do_resize = do_resize
+        self.resample = resample
+        self.do_rescale = do_rescale
+        self.rescale_factor = rescale_factor
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_convert_rgb = do_convert_rgb
+        self.patch_size = patch_size
+        self.max_num_patches = max_num_patches
+        self.min_num_patches = min_num_patches
+
+    @siglip2_ips.filter_out_non_signature_kwargs()
+    def preprocess(
+        self,
+        images,
+        resample=None,
+        do_rescale: Optional[bool] = None,
+        rescale_factor: Optional[float] = None,
+        do_normalize: Optional[bool] = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        return_tensors=None,
+        input_data_format=None,
+        do_convert_rgb: Optional[bool] = None,
+        patch_size: Optional[int] = None,
+        max_num_patches: Optional[int] = None,
+        min_num_patches: Optional[int] = None,
+    ):
+        resample = resample if resample is not None else self.resample
+        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+        image_mean = image_mean if image_mean is not None else self.image_mean
+        image_std = image_std if image_std is not None else self.image_std
+        do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
+        patch_size = patch_size if patch_size is not None else self.patch_size
+        max_num_patches = max_num_patches if max_num_patches is not None else self.max_num_patches
+        min_num_patches = min_num_patches if min_num_patches is not None else self.min_num_patches
+
+        data_format = siglip2_ips.ChannelDimension.LAST
+
+        try:
+            images = self.fetch_images(images)
+        except TypeError:
+            pass
+        images = siglip2_ips.make_flat_list_of_images(images)
+
+        if not siglip2_ips.valid_images(images):
+            raise ValueError("Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, or torch.Tensor")
+        
+        siglip2_ips.validate_preprocess_arguments(
+            do_rescale=do_rescale,
+            rescale_factor=rescale_factor,
+            do_normalize=do_normalize,
+            image_mean=image_mean,
+            image_std=image_std,
+        )
+        
+        if do_convert_rgb:
+            images = [siglip2_ips.convert_to_rgb(image) for image in images]
+
+        images = [siglip2_ips.to_numpy_array(image) for image in images]
+
+        if input_data_format is None:
+            input_data_format = siglip2_ips.infer_channel_dimension_format(images[0])
+
+        pixel_masks = []
+        pixel_values = []
+        spatial_shapes = []
+
+        for image in images:
+            image = siglip2_ips.to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
+
+            num_patches = max((image.shape[1] // patch_size) * (image.shape[0] // patch_size), 1)
+            
+            # Resize only if image is too large/small
+            if num_patches < min_num_patches:
+                height, width = siglip2_ips.get_image_size_for_max_num_patches(
+                    image_height=image.shape[0],
+                    image_width=image.shape[1],
+                    patch_size=patch_size,
+                    max_num_patches=min_num_patches,
+                )
+            elif num_patches > max_num_patches:
+                height, width = siglip2_ips.get_image_size_for_max_num_patches(
+                    image_height=image.shape[0],
+                    image_width=image.shape[1],
+                    patch_size=patch_size,
+                    max_num_patches=max_num_patches,
+                )
+            else:
+                height, width = siglip2_ips.get_image_size_for_max_num_patches(
+                    image_height=image.shape[0],
+                    image_width=image.shape[1],
+                    patch_size=patch_size,
+                    max_num_patches=num_patches,
+                )
+            
+            image = siglip2_ips.resize(image=image, size=(height, width), resample=resample, input_data_format=data_format)
+
+            if do_rescale:
+                image = self.rescale(image=image, scale=rescale_factor, input_data_format=data_format)
+
+            if do_normalize:
+                image = self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=data_format)
+
+            patches = siglip2_ips.convert_image_to_patches(image, patch_size)
+            patches, mask = siglip2_ips.pad_along_first_dim(patches, max_num_patches)
+            num_patches_height = image.shape[0] // patch_size
+            num_patches_width = image.shape[1] // patch_size
+
+            spatial_shapes.append((num_patches_height, num_patches_width))
+            pixel_values.append(patches)
+            pixel_masks.append(mask)
+
+        return siglip2_ips.BatchFeature(
+            data={
+                "pixel_values": pixel_values,
+                "pixel_attention_mask": pixel_masks,
+                "spatial_shapes": spatial_shapes,
+            },
+            tensor_type=return_tensors,
+        )
+
+
+class Siglip2VisionTower(nn.Module):
+    """SigLIP2 vision encoder with NaFlex (variable token count per image)."""
+    
+    def __init__(self, vision_tower: str, args: ModelArguments = None, delay_load: bool = False):
+        super().__init__()
+
+        self.is_loaded = False
+        self.vision_tower_name = vision_tower
+        self.vision_tower_path = None
+        self.select_layer = -2
+
+        self.hf_hub_cache_dir = None
+        self.local_files_only = False
+
+        self.min_num_patches = getattr(args, "min_num_patches", 256) if args else 256
+        self.max_num_patches = getattr(args, "max_num_patches", 3600) if args else 3600
+
+        if args and getattr(args, 'hf_cache_dir', None):
+            self.hf_hub_cache_dir = args.hf_cache_dir
+            self.local_files_only = True
+        
+        # Load or create vision config once (avoids network calls if embedded config provided)
+        vision_config_dict = getattr(args, "vision_config", None) if args else None
+        if vision_config_dict is not None:
+            # Infer patch_size from model name if not in config
+            if 'patch_size' not in vision_config_dict:
+                if 'patch14' in self.vision_tower_name.lower():
+                    vision_config_dict['patch_size'] = 14
+                else:
+                    vision_config_dict['patch_size'] = 16  # default for patch16-naflex
+            self._vision_config = Siglip2VisionConfig(**vision_config_dict)
+        else:
+            self._vision_config = Siglip2VisionConfig.from_pretrained(
+                self.vision_tower_name,
+                local_files_only=self.local_files_only,
+                cache_dir=self.hf_hub_cache_dir,
+            )
+            
+        if not delay_load:
+            self.load_model()
+
+    def load_model(self, skip_weights: bool = False):
+        """Load the vision tower model.
+        
+        Args:
+            skip_weights: If True, only load the architecture without pretrained weights.
+                         Useful when weights will be loaded from a checkpoint later.
+        """
+        if self.is_loaded:
+            return
+
+        # Create image processor
+        self.image_processor = Siglip2ImageProcessorNoUpscale(
+            patch_size=self._vision_config.patch_size,
+            max_num_patches=self.max_num_patches,
+            min_num_patches=self.min_num_patches,
+        )
+
+        if skip_weights:
+            # Load architecture only, no pretrained weights (will load from checkpoint)
+            self.vision_tower = Siglip2VisionModel(self._vision_config)
+            logger.info("Vision tower initialized without pretrained weights (will load from checkpoint).")
+        else:
+            vision_tower_path = self.vision_tower_path if self.vision_tower_path else self.vision_tower_name
+            self.vision_tower = Siglip2VisionModel.from_pretrained(
+                vision_tower_path,
+                config=self._vision_config,
+                local_files_only=self.local_files_only,
+                cache_dir=self.hf_hub_cache_dir,
+            )
+
+        self.vision_tower.config.min_num_patches = self.min_num_patches
+        self.vision_tower.config.max_num_patches = self.max_num_patches
+
+        self.vision_tower.requires_grad_(False)
+        self.is_loaded = True
+
+    def feature_select(self, image_forward_outs):
+        return image_forward_outs.hidden_states[self.select_layer]
+
+    def forward(self, images):
+        if isinstance(images, (dict, BatchFeature)):
+            images = {
+                "pixel_values": images["pixel_values"].to(device=self.device, dtype=self.dtype),
+                "pixel_attention_mask": images["pixel_attention_mask"].to(device=self.device, dtype=self.dtype),
+                "spatial_shapes": images["spatial_shapes"].cpu().numpy(),
+            }
+            images_forward_out = self.vision_tower(**images, output_hidden_states=True)
+            image_features = self.feature_select(images_forward_out).to(self.dtype)
+            # Remove pad tokens
+            image_features = [
+                feat[images["pixel_attention_mask"][j].bool()] 
+                for j, feat in enumerate(image_features)
+            ]
+
+        elif isinstance(images, list):
+            image_features = []
+            for image in images:
+                image = {
+                    "pixel_values": image["pixel_values"].to(device=self.device, dtype=self.dtype),
+                    "pixel_attention_mask": image["pixel_attention_mask"].to(device=self.device, dtype=self.dtype),
+                    "spatial_shapes": image["spatial_shapes"].cpu().numpy(),
+                }
+                image_forward_out = self.vision_tower(**image, output_hidden_states=True)
+                image_feature = self.feature_select(image_forward_out).to(self.dtype)
+                image_feature = [
+                    feat[image["pixel_attention_mask"][j].bool()] 
+                    for j, feat in enumerate(image_feature)
+                ]
+                image_features.append(image_feature)
+        else:
+            raise ValueError(f"Unsupported image type: {type(images)}")
+
+        return image_features
+
+    @property
+    def dummy_feature(self):
+        return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
+
+    @property
+    def dtype(self):
+        return self.vision_tower.dtype
+
+    @property
+    def device(self):
+        return self.vision_tower.device
+
+    @property
+    def config(self):
+        return self.vision_tower.config if self.is_loaded else self._vision_config
+
+    @property
+    def hidden_size(self):
+        return self.config.hidden_size
+
+
+# =============================================================================
+# Vision Tower Builder
+# =============================================================================
+
+def build_vision_tower(config, delay_load: bool = False):
+    """Build the appropriate vision tower based on config."""
+    vision_tower = getattr(config, 'mm_vision_tower', getattr(config, 'vision_tower', None))
+    
+    if vision_tower is None:
+        return None
+    
+    # Create a minimal args object from config
+    args = ModelArguments(
+        vision_tower=vision_tower,
+        hf_cache_dir=getattr(config, 'hf_cache_dir', None),
+        min_num_patches=getattr(config, 'min_num_patches', 256),
+        max_num_patches=getattr(config, 'max_num_patches', 3600),
+        vision_config=getattr(config, 'vision_config', None),
+    )
+    
+    if 'siglip' in vision_tower.lower():
+        if 'naflex' in vision_tower.lower():
+            return Siglip2VisionTower(vision_tower, args=args, delay_load=delay_load)
+        else:
+            return SiglipVisionTower(vision_tower, args=args, delay_load=delay_load)
+    
+    raise ValueError(f'Unknown vision tower: {vision_tower}. Only SigLIP variants are supported.')
+
+
+# =============================================================================
+# Configuration
+# =============================================================================
+
+class Phi4VisionR(Phi3Config):
+    """Configuration for Phi4-Siglip model."""
+    model_type = "phi4-siglip"
+    
+    def __init__(
+        self,
+        mm_vision_tower: Optional[str] = None,
+        mm_projector_type: str = "mlp2x_gelu",
+        mm_hidden_size: int = 1152,
+        min_num_patches: int = 256,
+        max_num_patches: int = 3600,
+        vision_config: Optional[dict] = None,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.mm_vision_tower = mm_vision_tower
+        self.mm_projector_type = mm_projector_type
+        self.mm_hidden_size = mm_hidden_size
+        self.min_num_patches = min_num_patches
+        self.max_num_patches = max_num_patches
+        self.vision_config = vision_config
+
+
+# =============================================================================
+# Base Model with Vision Integration
+# =============================================================================
+
+class Phi4VisionRModel(Phi3Model):
+    """Phi3 model with vision tower and projector."""
+    config_class = Phi4VisionR
+
+    def __init__(self, config: Phi4VisionR):
+        super().__init__(config)
+
+        if hasattr(config, "mm_vision_tower") and config.mm_vision_tower:
+            self.vision_tower = build_vision_tower(config, delay_load=not getattr(config, 'continuous_training', False))
+            if getattr(config, 'continuous_training', False):
+                config.continuous_training = False
+            self.mm_projector = build_vision_projector(config)
+
+    def get_vision_tower(self):
+        vision_tower = getattr(self, 'vision_tower', None)
+        if isinstance(vision_tower, list):
+            vision_tower = vision_tower[0]
+        return vision_tower
+
+    def initialize_vision_modules(self, model_args: ModelArguments):
+        """Initialize vision tower and projector from model arguments."""
+        vision_tower_name = model_args.vision_tower
+
+        self.config.mm_vision_tower = vision_tower_name
+
+        if self.get_vision_tower() is None:
+            vision_tower = build_vision_tower(model_args)
+            self.vision_tower = vision_tower
+        else:
+            vision_tower = self.vision_tower
+            if model_args.vision_tower_path:
+                vision_tower.vision_tower_path = model_args.vision_tower_path
+            vision_tower.load_model()
+
+        self.config.use_mm_proj = True
+        self.config.mm_projector_type = model_args.mm_projector_type
+        self.config.mm_hidden_size = vision_tower.hidden_size
+
+        if getattr(self, 'mm_projector', None) is None:
+            self.mm_projector = build_vision_projector(self.config)
+
+        # Ensure projector is trainable
+        for p in self.mm_projector.parameters():
+            p.requires_grad = True
+
+        # Load pretrained projector weights if provided
+        if model_args.pretrain_mm_mlp_adapter is not None:
+            mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
+            
+            def get_w(weights, keyword):
+                return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
+
+            self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
+
+
+# =============================================================================
+# Causal LM with Multimodal Support
+# =============================================================================
+
+class Phi4ForCausalLMV(Phi3ForCausalLM):
+    """Phi4-Siglip model for causal language modeling with vision support."""
+    config_class = Phi4VisionR
+    
+    # Tell transformers to not warn about vision tower weights - we load them separately
+    _keys_to_ignore_on_load_unexpected = [r"model\.vision_tower\.vision_tower\..*"]
+
+    def __init__(self, config: Phi4VisionR):
+        super(Phi3ForCausalLM, self).__init__(config)
+        self.model = Phi4VisionRModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.post_init()
+
+    def get_model(self):
+        return self.model
+
+    def get_vision_tower(self):
+        return self.get_model().get_vision_tower()
+
+    def encode_images(self, images):
+        """Encode images through vision tower and projector."""
+        image_features = self.get_model().get_vision_tower()(images)
+        
+        # Handle dynamic tokens (NaFlex)
+        if isinstance(image_features, list) and isinstance(image_features[0], list):
+            image_features = [
+                [self.get_model().mm_projector(image) for image in batch] 
+                for batch in image_features
+            ]
+        elif isinstance(image_features, list):
+            image_features = [self.get_model().mm_projector(image) for image in image_features]
+        else:
+            image_features = self.get_model().mm_projector(image_features)
+        
+        return image_features
+
+    def prepare_inputs_labels_for_multimodal(
+        self, input_ids, position_ids, attention_mask, past_key_values, labels, images
+    ):
+        """
+        Prepare inputs by replacing image tokens with actual image embeddings.
+        
+        This is the core multimodal integration logic that:
+        1. Encodes images through the vision tower
+        2. Finds IMAGE_TOKEN_INDEX positions in input_ids
+        3. Replaces those positions with image embeddings
+        4. Handles padding and attention masks
+        """
+        vision_tower = self.get_vision_tower()
+        
+        if vision_tower is None or images is None or input_ids.shape[1] == 1:
+            # Handle KV cache case during generation
+            if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[1] == 1:
+                target_shape = past_key_values[-1][-1].shape[-2] + 1
+                attention_mask = torch.cat((
+                    attention_mask,
+                    torch.ones(
+                        (attention_mask.shape[0], target_shape - attention_mask.shape[1]),
+                        dtype=attention_mask.dtype,
+                        device=attention_mask.device
+                    )
+                ), dim=1)
+                position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
+            return input_ids, position_ids, attention_mask, past_key_values, None, labels
+
+        # Encode images
+        if (isinstance(images, torch.Tensor) and images.ndim == 5) or \
+           (isinstance(images, list) and isinstance(images[0], torch.Tensor)):
+            images = torch.cat([image for image in images], dim=0)
+            image_features = self.encode_images(images).to(self.device)
+        elif isinstance(images, list) and isinstance(images[0], (dict, BatchFeature)):
+            # NaFlex case
+            image_features = self.encode_images(images)
+            image_features = [image.to(self.device) for batch in image_features for image in batch]
+        elif isinstance(images, (dict, BatchFeature)):
+            image_features = self.encode_images(images)
+            image_features = [image.to(self.device) for image in image_features]
+        else:
+            image_features = self.encode_images(images).to(self.device)
+
+        # Store original values
+        _labels = labels
+        _position_ids = position_ids
+        _attention_mask = attention_mask
+        
+        # Create defaults if not provided
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
+        else:
+            attention_mask = attention_mask.bool()
+        if position_ids is None:
+            position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
+        if labels is None:
+            labels = torch.full_like(input_ids, IGNORE_INDEX)
+
+        input_ids_temp = input_ids
+
+        # Remove padding using attention_mask
+        input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in
+                     zip(input_ids, attention_mask)]
+        labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]
+
+        # Replace IMAGE_TOKEN_INDEX with 0 for compatibility
+        input_ids_temp[input_ids_temp == IMAGE_TOKEN_INDEX] = 0
+
+        new_input_embeds = []
+        new_labels = []
+        cur_image_idx = 0
+        
+        for batch_idx, cur_input_ids in enumerate(input_ids):
+            num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
+            
+            if num_images == 0:
+                # No image tokens - just embed text
+                cur_image_features = image_features[cur_image_idx]
+                cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids)
+                cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0)
+                new_input_embeds.append(cur_input_embeds)
+                new_labels.append(labels[batch_idx])
+                cur_image_idx += 1
+                continue
+
+            # Find image token positions
+            image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [
+                cur_input_ids.shape[0]]
+            
+            cur_input_ids_noim = []
+            cur_labels = labels[batch_idx]
+            cur_labels_noim = []
+            
+            # Split by image tokens
+            for i in range(len(image_token_indices) - 1):
+                cur_input_ids_noim.append(cur_input_ids[image_token_indices[i] + 1:image_token_indices[i + 1]])
+                cur_labels_noim.append(cur_labels[image_token_indices[i] + 1:image_token_indices[i + 1]])
+            
+            split_sizes = [x.shape[0] for x in cur_labels_noim]
+            cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_noim))
+            cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0)
+            
+            cur_new_input_embeds = []
+            cur_new_labels = []
+
+            # Interleave text and image embeddings
+            for i in range(num_images + 1):
+                cur_new_input_embeds.append(cur_input_embeds_no_im[i])
+                cur_new_labels.append(cur_labels_noim[i])
+                if i < num_images:
+                    cur_image_features = image_features[cur_image_idx]
+                    cur_image_idx += 1
+                    cur_new_input_embeds.append(cur_image_features)
+                    cur_new_labels.append(
+                        torch.full(
+                            (cur_image_features.shape[0],), 
+                            IGNORE_INDEX, 
+                            device=cur_labels.device,
+                            dtype=cur_labels.dtype
+                        )
+                    )
+
+            cur_new_input_embeds = torch.cat(cur_new_input_embeds)
+            cur_new_labels = torch.cat(cur_new_labels)
+
+            new_input_embeds.append(cur_new_input_embeds)
+            new_labels.append(cur_new_labels)
+
+        # Truncate to max length
+        tokenizer_model_max_length = getattr(self.config, 'tokenizer_model_max_length', None)
+        if tokenizer_model_max_length is not None:
+            new_input_embeds = [x[:tokenizer_model_max_length] for x in new_input_embeds]
+            new_labels = [x[:tokenizer_model_max_length] for x in new_labels]
+
+        # Pad sequences to same length
+        max_len = max(x.shape[0] for x in new_input_embeds)
+        batch_size = len(new_input_embeds)
+
+        new_input_embeds_padded = []
+        new_labels_padded = torch.full(
+            (batch_size, max_len), IGNORE_INDEX, 
+            dtype=new_labels[0].dtype, device=new_labels[0].device
+        )
+        attention_mask = torch.zeros(
+            (batch_size, max_len), 
+            dtype=attention_mask.dtype, device=attention_mask.device
+        )
+        position_ids = torch.zeros(
+            (batch_size, max_len), 
+            dtype=position_ids.dtype, device=position_ids.device
+        )
+
+        for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)):
+            cur_len = cur_new_embed.shape[0]
+            padding_side = getattr(self.config, 'tokenizer_padding_side', 'right')
+            
+            if padding_side == "left":
+                new_input_embeds_padded.append(torch.cat((
+                    torch.zeros(
+                        (max_len - cur_len, cur_new_embed.shape[1]), 
+                        dtype=cur_new_embed.dtype, device=cur_new_embed.device
+                    ),
+                    cur_new_embed
+                ), dim=0))
+                if cur_len > 0:
+                    new_labels_padded[i, -cur_len:] = cur_new_labels
+                    attention_mask[i, -cur_len:] = True
+                    position_ids[i, -cur_len:] = torch.arange(
+                        0, cur_len, dtype=position_ids.dtype, device=position_ids.device
+                    )
+            else:
+                new_input_embeds_padded.append(torch.cat((
+                    cur_new_embed,
+                    torch.zeros(
+                        (max_len - cur_len, cur_new_embed.shape[1]), 
+                        dtype=cur_new_embed.dtype, device=cur_new_embed.device
+                    )
+                ), dim=0))
+                if cur_len > 0:
+                    new_labels_padded[i, :cur_len] = cur_new_labels
+                    attention_mask[i, :cur_len] = True
+                    position_ids[i, :cur_len] = torch.arange(
+                        0, cur_len, dtype=position_ids.dtype, device=position_ids.device
+                    )
+
+        new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)
+
+        # Restore None values if originally None
+        new_labels = None if _labels is None else new_labels_padded
+        attention_mask = None if _attention_mask is None else attention_mask.to(dtype=_attention_mask.dtype)
+        position_ids = None if _position_ids is None else position_ids
+
+        return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        images: Optional[torch.FloatTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        pixel_attention_mask: Optional[torch.Tensor] = None,
+        spatial_shapes: Optional[torch.Tensor] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,     
+        logits_to_keep: Union[int, torch.Tensor] = 0,                   
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+
+        # Accept processor output format (pixel_values, pixel_attention_mask, spatial_shapes)
+        if images is None and pixel_values is not None:
+            images = BatchFeature({
+                "pixel_values": pixel_values,
+                "pixel_attention_mask": pixel_attention_mask,
+                "spatial_shapes": spatial_shapes,
+            })
+
+        if inputs_embeds is None:
+            (
+                input_ids,
+                position_ids,
+                attention_mask,
+                past_key_values,
+                inputs_embeds,
+                labels
+            ) = self.prepare_inputs_labels_for_multimodal(
+                input_ids,
+                position_ids,
+                attention_mask,
+                past_key_values,
+                labels,
+                images
+            )
+
+        return super().forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            labels=labels,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+            logits_to_keep=logits_to_keep         
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, inputs_embeds=None, attention_mask=None, **kwargs
+    ):
+        images = kwargs.pop("images", None)
+        
+        # Also accept processor output format (pixel_values, pixel_attention_mask, spatial_shapes)
+        pixel_values = kwargs.pop("pixel_values", None)
+        pixel_attention_mask = kwargs.pop("pixel_attention_mask", None)
+        spatial_shapes = kwargs.pop("spatial_shapes", None)
+        
+        # If processor output format is provided, package as BatchFeature for the model
+        if images is None and pixel_values is not None:
+            images = BatchFeature({
+                "pixel_values": pixel_values,
+                "pixel_attention_mask": pixel_attention_mask,
+                "spatial_shapes": spatial_shapes,
+            })
+
+        _inputs = super().prepare_inputs_for_generation(
+            input_ids, 
+            past_key_values=past_key_values, 
+            inputs_embeds=inputs_embeds, 
+            attention_mask=attention_mask,
+            **kwargs
+        )
+
+        if images is not None:
+            _inputs['images'] = images
+        return _inputs
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
+        """Load model from pretrained weights."""
+        # Extract dtype before passing to super() since we need it later
+        torch_dtype = kwargs.get("torch_dtype", None)
+        
+        # Check if loading from local checkpoint that contains vision tower weights
+        load_vision_from_checkpoint = False
+        if os.path.isdir(pretrained_model_name_or_path):
+            for file_name in os.listdir(pretrained_model_name_or_path):
+                if file_name.endswith("safetensors"):
+                    fpath = os.path.join(pretrained_model_name_or_path, file_name)
+                    shard_weights = load_file(fpath)
+                    if any(k.startswith("model.vision_tower.vision_tower.") for k in shard_weights.keys()):
+                        load_vision_from_checkpoint = True
+                        logger.info("Detected vision tower weights in checkpoint - will skip downloading from HuggingFace.")
+                        break
+        
+        model = super().from_pretrained(pretrained_model_name_or_path, **kwargs)
+
+        vision_tower = model.get_vision_tower()
+
+        # Load vision weights if model is a local path
+        if vision_tower is not None:
+            if not vision_tower.is_loaded:
+                # Skip downloading pretrained weights if we'll load from checkpoint
+                vision_tower.load_model(skip_weights=load_vision_from_checkpoint)
+
+            if load_vision_from_checkpoint:
+                try:
+                    vision_weights = {}
+                    for file_name in os.listdir(pretrained_model_name_or_path):
+                        if file_name.endswith("safetensors"):
+                            fpath = os.path.join(pretrained_model_name_or_path, file_name)
+                            shard_weights = load_file(fpath)
+                            
+                            # Handle weights with prefix "model.vision_tower.vision_tower."
+                            # (the nested vision_tower is the actual encoder)
+                            prefix_nested = "model.vision_tower.vision_tower."
+                            prefix_simple = "model.vision_tower."
+                            
+                            for k, v in shard_weights.items():
+                                if k.startswith(prefix_nested):
+                                    # Strip to get "vision_tower.xxx"
+                                    new_key = k[len("model.vision_tower."):]
+                                    vision_weights[new_key] = v
+                                elif k.startswith(prefix_simple) and not k.startswith(prefix_nested):
+                                    # Direct vision_tower weights (like image_processor params if saved)
+                                    new_key = k[len(prefix_simple):]
+                                    vision_weights[new_key] = v
+
+                    if vision_weights:
+                        vision_tower.load_state_dict(vision_weights, strict=False)
+                        logger.info("Vision tower weights loaded from checkpoint.")
+                    else:
+                        logger.warning("No vision tower weights found in checkpoint!")
+                except Exception as e:
+                    logger.warning(
+                        "Vision tower weights NOT loaded from checkpoint. "
+                        f"Exception: {e}"
+                    )
+
+            vision_tower.to(model.device)
+
+        # Sync dtype
+        dtype = torch_dtype if torch_dtype is not None else model.dtype
+        dtype = model.dtype if dtype == "auto" else dtype
+        model.to(dtype)
+
+        # Fix generation config
+        if isinstance(model.generation_config.eos_token_id, (list, set)):
+            model.generation_config.eos_token_id = model.generation_config.eos_token_id[0]
+        if model.generation_config.pad_token_id is None:
+            model.generation_config.pad_token_id = model.generation_config.eos_token_id
+
+        return model
+
+
+# =============================================================================
+# Register with AutoConfig/AutoModel
+# =============================================================================
+
+AutoConfig.register("phi4-siglip", Phi4VisionR)
+AutoModelForCausalLM.register(Phi4VisionR, Phi4ForCausalLMV)