Add GoT OCR 2.0

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>

docling/cli/main.py

@@ -59,6 +59,7 @@ from docling.datamodel.pipeline_options import (
 )
 from docling.datamodel.settings import settings
 from docling.datamodel.vlm_model_specs import (
+    GOT2_TRANSFORMERS,
     GRANITE_VISION_OLLAMA,
     GRANITE_VISION_TRANSFORMERS,
     SMOLDOCLING_MLX,
@@ -621,6 +622,8 @@ def convert(  # noqa: C901
                 pipeline_options.vlm_options = GRANITE_VISION_TRANSFORMERS
             elif vlm_model == VlmModelType.GRANITE_VISION_OLLAMA:
                 pipeline_options.vlm_options = GRANITE_VISION_OLLAMA
+            elif vlm_model == VlmModelType.GOT_OCR_2:
+                pipeline_options.vlm_options = GOT2_TRANSFORMERS
             elif vlm_model == VlmModelType.SMOLDOCLING:
                 pipeline_options.vlm_options = SMOLDOCLING_TRANSFORMERS
                 if sys.platform == "darwin":

docling/datamodel/pipeline_options_vlm_model.py

@@ -46,6 +46,7 @@ class TransformersModelType(str, Enum):
 class TransformersPromptStyle(str, Enum):
     CHAT = "chat"
     RAW = "raw"
+    NONE = "none"
 
 
 class InlineVlmOptions(BaseVlmOptions):

docling/datamodel/vlm_model_specs.py

@@ -194,6 +194,26 @@ QWEN25_VL_3B_MLX = InlineVlmOptions(
     temperature=0.0,
 )
 
+# GoT 2.0
+GOT2_TRANSFORMERS = InlineVlmOptions(
+    repo_id="stepfun-ai/GOT-OCR-2.0-hf",
+    prompt="",
+    response_format=ResponseFormat.MARKDOWN,
+    inference_framework=InferenceFramework.TRANSFORMERS,
+    transformers_prompt_style=TransformersPromptStyle.NONE,
+    transformers_model_type=TransformersModelType.AUTOMODEL_IMAGETEXTTOTEXT,
+    supported_devices=[
+        AcceleratorDevice.CPU,
+        AcceleratorDevice.CUDA,
+        #    AcceleratorDevice.MPS,
+    ],
+    scale=2.0,
+    temperature=0.0,
+    stop_strings=["<|im_end|>"],
+    # extra_generation_config={"format": True},
+)
+
+
 # Gemma-3
 GEMMA3_12B_MLX = InlineVlmOptions(
     repo_id="mlx-community/gemma-3-12b-it-bf16",
@@ -215,6 +235,8 @@ GEMMA3_27B_MLX = InlineVlmOptions(
     temperature=0.0,
 )
 
+# Dolphin
+
 DOLPHIN_TRANSFORMERS = InlineVlmOptions(
     repo_id="ByteDance/Dolphin",
     prompt="<s>Read text in the image. <Answer/>",
@@ -238,3 +260,4 @@ class VlmModelType(str, Enum):
     GRANITE_VISION = "granite_vision"
     GRANITE_VISION_VLLM = "granite_vision_vllm"
     GRANITE_VISION_OLLAMA = "granite_vision_ollama"
+    GOT_OCR_2 = "got_ocr_2"

docling/models/vlm_models_inline/hf_transformers_model.py

@@ -270,16 +270,24 @@ class HuggingFaceTransformersVlmModel(BaseVlmPageModel, HuggingFaceModelDownload
             user_prompts = prompt
 
         # Use your prompt formatter verbatim
-        prompts: list[str] = [self.formulate_prompt(p) for p in user_prompts]
+        if self.vlm_options.transformers_prompt_style == TransformersPromptStyle.NONE:
+            inputs = self.processor(
+                pil_images,
+                return_tensors="pt",
+                padding=True,  # pad across batch for both text and vision
+                # no truncation by default; match SmolDocling examples
+            )
+        else:
+            prompts: list[str] = [self.formulate_prompt(p) for p in user_prompts]
 
-        # -- Processor performs BOTH text+image preprocessing + batch padding (recommended)
-        inputs = self.processor(
-            text=prompts,
-            images=pil_images,
-            return_tensors="pt",
-            padding=True,  # pad across batch for both text and vision
-            # no truncation by default; match SmolDocling examples
-        )
+            # -- Processor performs BOTH text+image preprocessing + batch padding (recommended)
+            inputs = self.processor(
+                text=prompts,
+                images=pil_images,
+                return_tensors="pt",
+                padding=True,  # pad across batch for both text and vision
+                # no truncation by default; match SmolDocling examples
+            )
         inputs = {k: v.to(self.device) for k, v in inputs.items()}
 
         # -- Optional stopping criteria