Support InternVL3.5 model

paddleformers/transformers/__init__.py

@@ -171,6 +171,10 @@
     "paddleocr_vl.modeling": ["PaddleOCRVLForConditionalGeneration"],
     "paddleocr_vl.image_processor": ["PaddleOCRVLImageProcessor"],
     "paddleocr_vl.processor": ["PaddleOCRVLProcessor"],
+    "internvl3_5.configuration": ["InternVisionConfig", "InternVLChatConfig"],
+    "internvl3_5.modeling": ["InternVisionModel", "InternVLChatModel"],
+    "internvl3_5.image_processor": ["InternVLImageProcessor"],
+    "internvl3_5.processor": ["InternVLProcessor"],
     "gpt_oss.configuration": ["GptOssConfig"],
     "gpt_oss.modeling": ["GptOssModel", "GptOssForCausalLM", "GptOssForCausalLMPipe"],
     "granite.configuration": ["GraniteConfig"],

paddleformers/transformers/auto/configuration.py

@@ -69,6 +69,8 @@
         ("shieldgemma2", "ShieldGemma2Config"),
         ("glm4v_moe", "Glm4vMoeConfig"),
         ("glm_ocr", "GlmOcrConfig"),
+        ("internvl_chat", "InternVLChatConfig"),
+        ("intern_vit_6b", "InternVisionConfig"),
         ("qwen3_5", "Qwen3_5Config"),
         ("qwen3_5_moe", "Qwen3_5MoEConfig"),
         ("olmo2", "Olmo2Config"),
@@ -114,6 +116,7 @@
         ("gemma3", "Gemma3ForConditionalGeneration"),
         ("gemma3_text", "Gemma3TextModel"),
         ("shieldgemma2", "ShieldGemma2ForImageClassification"),
+        ("internvl_chat", "InternVLChat"),
         ("qwen3_5_moe", "Qwen3_5MoEForConditionalGeneration"),
         ("qwen3_5", "Qwen3_5ForConditionalGeneration"),
         ("olmo2", "Olmo2ForCausalLM"),
@@ -139,6 +142,8 @@
         ("qwen2_5_vl_text", "qwen2_5_vl"),
         ("qwen3_vl_text", "qwen3_vl"),
         ("qwen3_vl_moe_text", "qwen3_vl_moe"),
+        ("internvl_chat", "internvl3_5"),
+        ("intern_vit_6b", "internvl3_5"),
         ("internlm3", "intern_lm3"),
         ("internlm2", "intern"),
         # TODO(VL): Remove these when Gemma4 VL module (gemma4/) is created

paddleformers/transformers/auto/image_processing.py

@@ -62,6 +62,8 @@
         "qwen3_vl": ("Qwen3VLImageProcessor", "Qwen3VLImageProcessorFast"),
         "glm_ocr": ("Glm46VImageProcessor"),
         "paligemma": ("PaliGemmaImageProcessor"),
+        "internvl_chat": ("InternVLImageProcessor"),
+        "intern_vit_6b": ("InternVLImageProcessor"),
     }
 )
 
@@ -309,6 +311,12 @@ def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
             and config_dict.get("processor_class") == "PaliGemmaProcessor"
         ):
             image_processor_type = "PaliGemmaImageProcessor"
+        if (
+            image_processor_type in {"GotOcr2ImageProcessor", "GotOcr2ImageProcessorFast"}
+            and config_dict.get("processor_class") == "InternVLProcessor"
+        ):
+            image_processor_type = "InternVLImageProcessor"
+            use_fast = False
         image_processor_auto_map = None
         if "AutoImageProcessor" in config_dict.get("auto_map", {}):
             image_processor_auto_map = config_dict["auto_map"]["AutoImageProcessor"]

paddleformers/transformers/auto/modeling.py

@@ -91,6 +91,7 @@
         ("Olmo2", "olmo2"),
         ("InternLM3", "intern_lm3"),
         ("InternLM2", "intern"),
+        ("InternVLChat", "internvl3_5"),
     ]
 )
 

paddleformers/transformers/auto/processing.py

@@ -61,6 +61,7 @@
         ("glm4v_moe", "Glm4vProcessor"),
         ("glm_ocr", "Glm46VProcessor"),
         ("paligemma2", "PaliGemmaProcessor"),
+        ("internvl_chat", "InternVLProcessor"),
     ]
 )
 

paddleformers/transformers/internvl3_5/__init__.py

@@ -0,0 +1,28 @@
+# Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+
+import sys
+from typing import TYPE_CHECKING
+
+from ...utils.lazy_import import _LazyModule
+
+import_structure = {
+    "configuration": ["InternVisionConfig", "InternVLChatConfig"],
+    "image_processor": ["InternVLImageProcessor"],
+    "modeling": ["InternVisionModel", "InternVLChatModel"],
+    "processor": ["InternVLProcessor"],
+}
+
+if TYPE_CHECKING:
+    from .configuration import *
+    from .image_processor import *
+    from .modeling import *
+    from .processor import *
+else:
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        import_structure,
+        module_spec=__spec__,
+    )

paddleformers/transformers/internvl3_5/configuration.py

@@ -0,0 +1,129 @@
+# Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved.
+# Copyright 2024 OpenGVLab. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+
+import copy
+
+from ..configuration_utils import PretrainedConfig
+from ..qwen3.configuration import Qwen3Config
+from ..qwen3_moe.configuration import Qwen3MoeConfig
+
+__all__ = ["InternVisionConfig", "InternVLChatConfig"]
+
+
+def _get_llm_config_class(llm_config):
+    if isinstance(llm_config, (Qwen3Config, Qwen3MoeConfig)):
+        return llm_config.__class__
+    model_type = llm_config.get("model_type") if isinstance(llm_config, dict) else None
+    architectures = llm_config.get("architectures", []) if isinstance(llm_config, dict) else []
+    if model_type == "qwen3_moe" or any(architecture.startswith("Qwen3Moe") for architecture in architectures):
+        return Qwen3MoeConfig
+    return Qwen3Config
+
+
+class InternVisionConfig(PretrainedConfig):
+    model_type = "intern_vit_6b"
+    base_config_key = "vision_config"
+
+    def __init__(
+        self,
+        num_channels=3,
+        patch_size=14,
+        image_size=224,
+        qkv_bias=False,
+        hidden_size=3200,
+        num_attention_heads=25,
+        intermediate_size=12800,
+        qk_normalization=True,
+        num_hidden_layers=48,
+        use_flash_attn=True,
+        hidden_act="gelu",
+        norm_type="rms_norm",
+        layer_norm_eps=1e-6,
+        dropout=0.0,
+        drop_path_rate=0.0,
+        attention_dropout=0.0,
+        initializer_range=0.02,
+        initializer_factor=0.1,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.drop_path_rate = drop_path_rate
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.initializer_range = initializer_range
+        self.initializer_factor = initializer_factor
+        self.attention_dropout = attention_dropout
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+        self.norm_type = norm_type
+        self.qkv_bias = qkv_bias
+        self.qk_normalization = qk_normalization
+        self.use_flash_attn = use_flash_attn
+
+
+class InternVLChatConfig(PretrainedConfig):
+    model_type = "internvl_chat"
+    is_composition = True
+    sub_configs = {"vision_config": InternVisionConfig, "llm_config": Qwen3Config}
+
+    def __init__(
+        self,
+        vision_config=None,
+        llm_config=None,
+        use_backbone_lora=0,
+        use_llm_lora=0,
+        select_layer=-1,
+        force_image_size=None,
+        downsample_ratio=0.5,
+        template=None,
+        dynamic_image_size=False,
+        use_thumbnail=False,
+        ps_version="v1",
+        min_dynamic_patch=1,
+        max_dynamic_patch=6,
+        img_context_token_id=151671,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        if vision_config is None:
+            vision_config = {"architectures": ["InternVisionModel"]}
+        if llm_config is None:
+            llm_config = {"architectures": ["Qwen3ForCausalLM"]}
+
+        self.vision_config = InternVisionConfig(**vision_config) if isinstance(vision_config, dict) else vision_config
+        llm_config_class = _get_llm_config_class(llm_config)
+        self.llm_config = llm_config_class(**llm_config) if isinstance(llm_config, dict) else llm_config
+
+        self.use_backbone_lora = use_backbone_lora
+        self.use_llm_lora = use_llm_lora
+        self.select_layer = select_layer
+        self.force_image_size = force_image_size
+        self.downsample_ratio = downsample_ratio
+        self.template = template
+        self.dynamic_image_size = dynamic_image_size
+        self.use_thumbnail = use_thumbnail
+        self.ps_version = ps_version
+        self.min_dynamic_patch = min_dynamic_patch
+        self.max_dynamic_patch = max_dynamic_patch
+        self.img_context_token_id = img_context_token_id
+        self.tie_word_embeddings = self.llm_config.tie_word_embeddings
+        self.vocab_size = self.llm_config.vocab_size
+        self.hidden_size = self.llm_config.hidden_size
+        self.pad_token_id = getattr(self.llm_config, "pad_token_id", getattr(self, "pad_token_id", None))
+        self.eos_token_id = getattr(self.llm_config, "eos_token_id", getattr(self, "eos_token_id", None))
+        self.bos_token_id = getattr(self.llm_config, "bos_token_id", getattr(self, "bos_token_id", None))
+
+    def to_dict(self, *args, **kwargs):
+        output = copy.deepcopy(self.__dict__)
+        output["vision_config"] = self.vision_config.to_dict()
+        output["llm_config"] = self.llm_config.to_dict()
+        output["model_type"] = self.__class__.model_type
+        return output