Add Phi-3.5-vision

docs/source/en/_toctree.yml

@@ -1091,6 +1091,8 @@
         title: Perceiver
       - local: model_doc/perception_lm
         title: PerceptionLM
+      - local: model_doc/phi3_v
+        title: Phi-3.5-vision-instruct
       - local: model_doc/phi4_multimodal
         title: Phi4 Multimodal
       - local: model_doc/pix2struct

docs/source/en/model_doc/phi3_v.md

@@ -0,0 +1,59 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contains specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Phi-3.5-vision-instruct
+
+
+
+## Phi3VConfig
+
+[[autodoc]] Phi3VConfig
+
+## Phi3VImageProcessor
+
+[[autodoc]] Phi3VImageProcessor
+    - preprocess
+
+## Phi3VProcessor
+
+[[autodoc]] Phi3VProcessor
+
+<frameworkcontent>
+<pt>
+
+## Phi3VModel
+
+[[autodoc]] Phi3VModel
+    - forward
+
+## Phi3VForCausalLM
+
+[[autodoc]] Phi3VForCausalLM
+    - forward
+    - generate
+
+## Phi3VForSequenceClassification
+
+[[autodoc]] Phi3VForSequenceClassification
+    - forward
+
+## Phi3VForTokenClassification
+
+[[autodoc]] Phi3VForTokenClassification
+    - forward
+
+</pt>
+</frameworkcontent>

src/transformers/models/__init__.py

@@ -253,6 +253,7 @@
     from .persimmon import *
     from .phi import *
     from .phi3 import *
+    from .phi3_v import *
     from .phi4_multimodal import *
     from .phimoe import *
     from .phobert import *

src/transformers/models/auto/configuration_auto.py

@@ -295,6 +295,7 @@
         ("persimmon", "PersimmonConfig"),
         ("phi", "PhiConfig"),
         ("phi3", "Phi3Config"),
+        ("phi3_v", "Phi3VConfig"),
         ("phi4_multimodal", "Phi4MultimodalConfig"),
         ("phimoe", "PhimoeConfig"),
         ("pix2struct", "Pix2StructConfig"),
@@ -724,6 +725,7 @@
         ("persimmon", "Persimmon"),
         ("phi", "Phi"),
         ("phi3", "Phi3"),
+        ("phi3_v", "Phi3V"),
         ("phi4_multimodal", "Phi4Multimodal"),
         ("phimoe", "Phimoe"),
         ("phobert", "PhoBERT"),

src/transformers/models/auto/image_processing_auto.py

@@ -146,6 +146,7 @@
             ("paligemma", ("SiglipImageProcessor", "SiglipImageProcessorFast")),
             ("perceiver", ("PerceiverImageProcessor", "PerceiverImageProcessorFast")),
             ("perception_lm", (None, "PerceptionLMImageProcessorFast")),
+            ("phi3_v", ("Phi3VImageProcessor", None)),
             ("phi4_multimodal", (None, "Phi4MultimodalImageProcessorFast")),
             ("pix2struct", ("Pix2StructImageProcessor", None)),
             ("pixtral", ("PixtralImageProcessor", "PixtralImageProcessorFast")),

src/transformers/models/auto/modeling_auto.py

@@ -294,6 +294,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("persimmon", "PersimmonModel"),
         ("phi", "PhiModel"),
         ("phi3", "Phi3Model"),
+        ("phi3_v", "Phi3VModel"),
         ("phi4_multimodal", "Phi4MultimodalModel"),
         ("phimoe", "PhimoeModel"),
         ("pixtral", "PixtralVisionModel"),
@@ -694,6 +695,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("persimmon", "PersimmonForCausalLM"),
         ("phi", "PhiForCausalLM"),
         ("phi3", "Phi3ForCausalLM"),
+        ("phi3_v", "Phi3VForCausalLM"),
         ("phi4_multimodal", "Phi4MultimodalForCausalLM"),
         ("phimoe", "PhimoeForCausalLM"),
         ("plbart", "PLBartForCausalLM"),
@@ -1239,6 +1241,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("persimmon", "PersimmonForSequenceClassification"),
         ("phi", "PhiForSequenceClassification"),
         ("phi3", "Phi3ForSequenceClassification"),
+        ("phi3_v", "Phi3VForSequenceClassification"),
         ("phimoe", "PhimoeForSequenceClassification"),
         ("plbart", "PLBartForSequenceClassification"),
         ("qdqbert", "QDQBertForSequenceClassification"),
@@ -1440,6 +1443,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("persimmon", "PersimmonForTokenClassification"),
         ("phi", "PhiForTokenClassification"),
         ("phi3", "Phi3ForTokenClassification"),
+        ("phi3_v", "Phi3VForTokenClassification"),
         ("qdqbert", "QDQBertForTokenClassification"),
         ("qwen2", "Qwen2ForTokenClassification"),
         ("qwen2_moe", "Qwen2MoeForTokenClassification"),

src/transformers/models/phi3_v/__init__.py

@@ -0,0 +1,29 @@
+# Copyright 2025 Microsoft and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_phi3_v import *
+    from .image_processing_phi3_v import *
+    from .modeling_phi3_v import *
+    from .processing_phi3_v import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)

src/transformers/models/phi3_v/configuration_phi3_v.py

@@ -0,0 +1,219 @@
+# coding=utf-8
+# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Phi-3-V model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+PHI3V_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "microsoft/Phi-3-vision-128k-instruct": "https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/resolve/main/config.json",
+    "microsoft/Phi-3.5-vision-instruct": "https://huggingface.co/microsoft/Phi-3.5-vision-instruct/resolve/main/config.json",
+}
+
+
+class Phi3VConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Phi3VModel`]. It is used to instantiate a Phi-3
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the
+    [microsoft/Phi-3-vision-128k-instruct](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct).
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32064):
+            Vocabulary size of the Phi-3-V model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Phi3VModel`].
+        hidden_size (`int`, *optional*, defaults to 3072):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 8192):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        resid_pdrop (`float`, *optional*, defaults to 0.0):
+            Dropout probability for mlp outputs.
+        embd_pdrop (`int`, *optional*, defaults to 0.0):
+            The dropout ratio for the embeddings.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio after computing the attention scores.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model might ever be used with.
+        original_max_position_embeddings (`int`, *optional*, defaults to 4096):
+            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.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon value used for the RMSNorm.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`dict`, *optional*):
+            The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
+            contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be either `su` or `yarn` and
+            the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size
+            divided by the number of attention heads divided by 2.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 32000):
+            The id of the "end-of-sequence" token.
+        pad_token_id (`int`, *optional*, defaults to 32000):
+            The id of the padding token.
+        sliding_window (`int`, *optional*):
+            Sliding window attention window size. If `None`, no sliding window is applied.
+        embd_layer (`str`, *optional*, defaults to `"default"`):
+            The embedding layer to use. Can be either `"default"` or `"image"`. "default" uses the standard embedding for text.
+
+    Example:
+
+    ```python
+    >>> from transformers import Phi3VModel, Phi3VConfig
+
+    >>> # Initializing a Phi-3-V style configuration
+    >>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-vision-128k-instruct")
+
+    >>> # Initializing a model from the configuration
+    >>> model = Phi3VModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "phi3_v"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32064,
+        hidden_size=3072,
+        intermediate_size=8192,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        resid_pdrop=0.0,
+        embd_pdrop=0.0,
+        attention_dropout=0.0,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        original_max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        bos_token_id=1,
+        eos_token_id=32000,
+        pad_token_id=32000,
+        sliding_window=None,
+        embd_layer: str = "default",
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attention_dropout = attention_dropout
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.original_max_position_embeddings = original_max_position_embeddings
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.sliding_window = sliding_window
+        self.embd_layer = embd_layer
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
+        rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["su", "yarn"]:
+            raise ValueError(f"`rope_scaling`'s type field must be one of ['su', 'yarn'], got {rope_scaling_type}")
+        if not (
+            isinstance(rope_scaling_short_factor, list)
+            and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
+            )
+        if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
+            raise ValueError(
+                f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
+            )
+        if not (
+            isinstance(rope_scaling_long_factor, list)
+            and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
+            )
+        if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
+            raise ValueError(
+                f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
+            )
+
+
+__all__ = ["Phi3VConfig"]