初始化项目，由ModelHub XC社区提供模型

Model: AIDC-AI/Ovis2.5-2B
Source: Original Platform

configuration_ovis2_5.py

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+from typing import Any, Optional, List, Union
+
+from transformers import Qwen3Config
+from transformers.configuration_utils import PretrainedConfig
+
+__all__ = ["Siglip2NavitConfig", "Ovis2_5_Config"]
+
+
+class Siglip2NavitConfig(PretrainedConfig):
+    """This is the configuration class to store the configuration of an [`AIMv2Model`].
+
+    Instantiating a configuration with the defaults will yield a similar configuration
+    to that of the [apple/aimv2-large-patch14-224](https://huggingface.co/apple/aimv2-large-patch14-224).
+
+    Args:
+        hidden_size: Dimension of the hidden representations.
+        intermediate_size: Dimension of the SwiGLU representations.
+        num_hidden_layers: Number of hidden layers in the Transformer.
+        num_attention_heads: Number of attention heads for each attention layer
+            in the Transformer.
+        num_channels: Number of input channels.
+        image_size: Image size.
+        patch_size: Patch size.
+        rms_norm_eps: Epsilon value used for the RMS normalization layer.
+        attention_dropout: Dropout ratio for attention probabilities.
+        projection_dropout: Dropout ratio for the projection layer after the attention.
+        qkv_bias: Whether to add a bias to the queries, keys and values.
+        use_bias: Whether to add a bias in the feed-forward and projection layers.
+        kwargs: Keyword arguments for the [`PretrainedConfig`].
+    """
+
+    model_type: str = "siglip2_navit"
+
+    def __init__(
+        self,
+        hidden_size: int = 1024,
+        intermediate_size: int = 4096,
+        num_hidden_layers: int = 24,
+        num_attention_heads: int = 16,
+        num_channels: int = 3,
+        num_patches: int = -1,
+        image_size: int = 512,
+        patch_size: int = 16,
+        hidden_act: str="gelu_pytorch_tanh",
+        layer_norm_eps: float = 1e-6,
+        attention_dropout: float = 0.0,
+        hidden_stride: int = 2,
+        window_size: int = 112,
+        fullatt_block_indexes: Optional[list] = None,
+        temporal_patch_size: int = 1,
+        preserve_original_pe: bool = True,
+        use_rope: bool = True,
+        **kwargs: Any,
+    ):
+        super().__init__(**kwargs)
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.num_patches = num_patches
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.hidden_act = hidden_act
+        self.attention_dropout = attention_dropout
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_stride = hidden_stride
+        self.window_size = window_size
+        self.fullatt_block_indexes = fullatt_block_indexes
+        self.temporal_patch_size = temporal_patch_size
+        self.preserve_original_pe = preserve_original_pe
+        self.use_rope = use_rope
+
+class Ovis2_5_Config(PretrainedConfig):
+    model_type = "ovis2_5"
+    sub_configs = dict(llm_config=Qwen3Config, vit_config=Siglip2NavitConfig)
+
+    def __init__(self,
+        llm_config: Optional[Union[Qwen3Config, dict]] = None,
+        vit_config: Optional[Union[Siglip2NavitConfig, dict]] = None,
+        visual_vocab_size=65536,
+        hidden_size=None,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        if isinstance(llm_config, dict):
+            llm_config = Qwen3Config(**llm_config)
+        self.llm_config = llm_config
+        if isinstance(vit_config, dict):
+            vit_config = Siglip2NavitConfig(**vit_config)
+        self.vit_config = vit_config
+        self.visual_vocab_size = visual_vocab_size
+        self.hidden_size = hidden_size
+        if kwargs.get('attn_implementation'):
+            self.llm_config._attn_implementation = kwargs['attn_implementation']
+            self.vit_config._attn_implementation = kwargs['attn_implementation']