diff --git a/docling/datamodel/pipeline_options.py b/docling/datamodel/pipeline_options.py
index 30d4e50c4b..df3c1f66c9 100644
--- a/docling/datamodel/pipeline_options.py
+++ b/docling/datamodel/pipeline_options.py
@@ -203,6 +203,69 @@ class OcrMacOptions(OcrOptions):
     )
 
 
+class DeepSeekOcrOptions(OcrOptions):
+    """Options for the DeepSeek OCR engine.
+
+    DeepSeek-OCR is a Vision-Language Model (VLM) based OCR engine that uses
+    transformer models for document understanding and text extraction.
+    See: https://github.com/deepseek-ai/DeepSeek-OCR
+
+    Device Support:
+        - CUDA (NVIDIA GPU): Optimal performance with flash_attention_2 and bfloat16.
+          Uses the official 'deepseek-ai/DeepSeek-OCR' model.
+        - MPS (Apple Silicon M1/M2/M3/M4): Supported via MPS-compatible model fork.
+          Requires PyTorch 2.7.0+ for the aten::_upsample_bicubic2d_aa operator.
+          Automatically switches to 'Dogacel/DeepSeek-OCR-Metal-MPS' model with
+          float16 precision and eager attention.
+        - CPU: Not supported. Use EasyOcrOptions, TesseractOcrOptions, or
+          RapidOcrOptions for CPU-only environments.
+
+    Example:
+        >>> from docling.datamodel.pipeline_options import DeepSeekOcrOptions
+        >>> # Basic usage - auto-detects CUDA or MPS
+        >>> options = DeepSeekOcrOptions()
+        >>>
+        >>> # Custom prompt for specific OCR tasks
+        >>> options = DeepSeekOcrOptions(prompt="<image>\\nConvert to markdown.")
+    """
+
+    kind: ClassVar[Literal["deepseecocr"]] = "deepseecocr"
+
+    # DeepSeek-OCR is multilingual by default, no specific language configuration needed
+    lang: List[str] = []
+
+    # Model configuration
+    # Default is the official CUDA model; MPS users will auto-switch to MPS-compatible fork
+    repo_id: str = "deepseek-ai/DeepSeek-OCR"
+
+    # Prompt for OCR extraction
+    prompt: str = "<image>\nFree OCR."
+
+    # Image processing parameters
+    base_size: int = 1024
+    image_size: int = 640
+    crop_mode: bool = True
+
+    # Generation parameters
+    max_new_tokens: int = 4096
+    temperature: float = 0.0
+    do_sample: bool = False
+
+    # Trust remote code for loading the model
+    trust_remote_code: bool = True
+
+    # Attention implementation:
+    # - "flash_attention_2": Default for CUDA, optimal performance
+    # - "eager": Required for MPS (auto-selected), also works on CUDA
+    # - "sdpa": NOT supported by DeepSeek-OCR
+    attn_implementation: Optional[str] = None
+
+    model_config = ConfigDict(
+        extra="forbid",
+        protected_namespaces=(),
+    )
+
+
 class PictureDescriptionBaseOptions(BaseOptions):
     batch_size: int = 8
     scale: float = 2
@@ -273,6 +336,7 @@ class OcrEngine(str, Enum):
     TESSERACT = "tesseract"
     OCRMAC = "ocrmac"
     RAPIDOCR = "rapidocr"
+    DEEPSEECOCR = "deepseecocr"
 
 
 class PipelineOptions(BaseOptions):
diff --git a/docling/models/deepseek_ocr_model.py b/docling/models/deepseek_ocr_model.py
new file mode 100644
index 0000000000..ab56e2562c
--- /dev/null
+++ b/docling/models/deepseek_ocr_model.py
@@ -0,0 +1,439 @@
+"""DeepSeek OCR model integration for Docling.
+
+DeepSeek-OCR is a Vision-Language Model (VLM) based OCR engine that uses
+transformer models for document understanding and text extraction.
+See: https://github.com/deepseek-ai/DeepSeek-OCR
+"""
+
+import logging
+import tempfile
+from collections.abc import Iterable
+from pathlib import Path
+from typing import Any, Optional, Type
+
+from docling_core.types.doc import BoundingBox, CoordOrigin
+from docling_core.types.doc.page import BoundingRectangle, TextCell
+
+from docling.datamodel.accelerator_options import AcceleratorDevice, AcceleratorOptions
+from docling.datamodel.base_models import Page
+from docling.datamodel.document import ConversionResult
+from docling.datamodel.pipeline_options import DeepSeekOcrOptions, OcrOptions
+from docling.datamodel.settings import settings
+from docling.models.base_ocr_model import BaseOcrModel
+from docling.utils.profiling import TimeRecorder
+
+_log = logging.getLogger(__name__)
+
+# Model repository IDs
+_DEEPSEEK_OCR_CUDA_REPO = "deepseek-ai/DeepSeek-OCR"
+_DEEPSEEK_OCR_MPS_REPO = "Dogacel/DeepSeek-OCR-Metal-MPS"
+
+# Minimum PyTorch version required for MPS support
+# PyTorch 2.7.0+ is required for aten::_upsample_bicubic2d_aa operator on MPS
+_MIN_PYTORCH_VERSION_MPS = (2, 7, 0)
+
+
+def _parse_pytorch_version(version_str: str) -> tuple:
+    """Parse PyTorch version string into a tuple of integers."""
+    # Handle versions like "2.7.0", "2.7.0+cu118", "2.7.0.dev20241201"
+    import re
+
+    match = re.match(r"(\d+)\.(\d+)\.(\d+)", version_str)
+    if match:
+        return tuple(int(x) for x in match.groups())
+    return (0, 0, 0)
+
+
+class DeepSeekOcrModel(BaseOcrModel):
+    """DeepSeek OCR model for document text extraction.
+
+    This model uses the DeepSeek-OCR Vision-Language Model to extract text
+    from document images. Unlike traditional OCR engines that return bounding
+    boxes, DeepSeek-OCR returns structured text/markdown output.
+
+    Device Support:
+    - CUDA (NVIDIA GPU): Optimal performance with flash_attention_2
+    - MPS (Apple Silicon): Supported via MPS-compatible model fork (requires PyTorch 2.7.0+)
+    - CPU: Not supported (model contains GPU-specific operations)
+    """
+
+    def __init__(
+        self,
+        enabled: bool,
+        artifacts_path: Optional[Path],
+        options: DeepSeekOcrOptions,
+        accelerator_options: AcceleratorOptions,
+    ):
+        super().__init__(
+            enabled=enabled,
+            artifacts_path=artifacts_path,
+            options=options,
+            accelerator_options=accelerator_options,
+        )
+        self.options: DeepSeekOcrOptions
+
+        self.scale = 3  # multiplier for 72 dpi == 216 dpi
+
+        # Device and dtype will be set during model initialization
+        self.device: Any = None  # Will be set to torch.device during init
+        self.dtype: Any = None  # Will be set to torch.dtype during init
+
+        if self.enabled:
+            self._init_model(accelerator_options, artifacts_path)
+
+    def _init_model(
+        self,
+        accelerator_options: AcceleratorOptions,
+        artifacts_path: Optional[Path],
+    ) -> None:
+        """Initialize the DeepSeek-OCR model.
+
+        Device selection priority:
+        1. CUDA (NVIDIA GPU) - optimal performance with flash_attention_2
+        2. MPS (Apple Silicon) - supported via MPS-compatible model (requires PyTorch 2.7.0+)
+        3. CPU - not supported (raises error)
+        """
+        try:
+            import torch
+            from transformers import AutoModel, AutoTokenizer
+        except ImportError:
+            raise ImportError(
+                "DeepSeek-OCR requires 'transformers' and 'torch' packages. "
+                "Please install them via `pip install docling[deepseecocr]` to use this OCR engine. "
+                "Alternatively, Docling has support for other OCR engines. See the documentation."
+            )
+
+        # Detect available devices
+        has_cuda = torch.backends.cuda.is_built() and torch.cuda.is_available()
+        has_mps = torch.backends.mps.is_built() and torch.backends.mps.is_available()
+
+        # Determine device and configuration
+        if has_cuda:
+            # CUDA path - optimal performance
+            self.device = torch.device("cuda")
+            self.dtype = torch.bfloat16
+            model_repo = (
+                self.options.repo_id
+            )  # Use configured repo (default: deepseek-ai/DeepSeek-OCR)
+
+            # Determine attention implementation
+            if self.options.attn_implementation:
+                # User explicitly specified attention implementation
+                attn_implementation = self.options.attn_implementation
+            else:
+                # Auto-detect: prefer flash_attention_2 if available, fallback to eager
+                # Note: DeepSeek-OCR does not support 'sdpa', only 'flash_attention_2' or 'eager'
+                try:
+                    import flash_attn
+
+                    attn_implementation = "flash_attention_2"
+                    _log.debug("flash_attn package found, using flash_attention_2")
+                except ImportError:
+                    attn_implementation = "eager"
+                    _log.info(
+                        "flash_attn package not installed, using eager attention. "
+                        "For optimal performance on CUDA, install flash-attn: "
+                        "pip install flash-attn --no-build-isolation"
+                    )
+
+            _log.info(
+                f"DeepSeek-OCR using CUDA device with bfloat16 precision and {attn_implementation} attention"
+            )
+
+        elif has_mps:
+            # MPS path - Apple Silicon fallback
+            self._validate_mps_pytorch_version(torch)
+            self.device = torch.device("mps")
+            self.dtype = torch.float16  # MPS requires float16, not bfloat16
+            # Use MPS-compatible model unless user explicitly specified a different repo
+            if self.options.repo_id == _DEEPSEEK_OCR_CUDA_REPO:
+                model_repo = _DEEPSEEK_OCR_MPS_REPO
+                _log.info(
+                    f"DeepSeek-OCR: Switching to MPS-compatible model '{_DEEPSEEK_OCR_MPS_REPO}' "
+                    "for Apple Silicon. To use a custom model, set repo_id in DeepSeekOcrOptions."
+                )
+            else:
+                model_repo = self.options.repo_id
+            # MPS requires eager attention (no flash_attention_2 support)
+            attn_implementation = "eager"
+            _log.info(
+                "DeepSeek-OCR using MPS device (Apple Silicon) with float16 precision"
+            )
+
+        else:
+            # No GPU available - raise helpful error
+            raise RuntimeError(
+                "DeepSeek-OCR requires a GPU (CUDA or Apple Silicon MPS). "
+                "No compatible GPU was detected on this system.\n\n"
+                "Options:\n"
+                "  - For NVIDIA GPUs: Ensure CUDA is properly installed\n"
+                "  - For Apple Silicon (M1/M2/M3/M4): Ensure PyTorch 2.7.0+ is installed\n"
+                "  - For CPU-only environments: Use a different OCR engine:\n"
+                "    * EasyOcrOptions - Good multilingual support\n"
+                "    * TesseractOcrOptions - Classic OCR engine\n"
+                "    * RapidOcrOptions - Fast CPU-based OCR"
+            )
+
+        # Validate attention implementation
+        if attn_implementation == "sdpa":
+            _log.warning(
+                "DeepSeek-OCR does not support 'sdpa' attention. Falling back to 'eager'."
+            )
+            attn_implementation = "eager"
+
+        _log.debug(f"DeepSeek-OCR using device: {self.device}, dtype: {self.dtype}")
+
+        # Patch missing LlamaFlashAttention2 for compatibility with older/newer transformers
+        self._patch_llama_flash_attention()
+
+        # Determine model path (check for local cache)
+        model_path = model_repo
+        if artifacts_path is not None:
+            repo_cache_folder = model_repo.replace("/", "--")
+            local_path = artifacts_path / repo_cache_folder
+            if local_path.exists():
+                model_path = str(local_path)
+
+        # Load tokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            model_path,
+            trust_remote_code=self.options.trust_remote_code,
+        )
+        # Set pad token to eos token (required for some models)
+        self.tokenizer.pad_token = self.tokenizer.eos_token
+
+        # Load model
+        # Note: We must use attn_implementation parameter (not _attn_implementation)
+        # to properly override the model's config which may have flash_attention_2 hardcoded
+        try:
+            self.model = AutoModel.from_pretrained(
+                model_path,
+                attn_implementation=attn_implementation,
+                trust_remote_code=self.options.trust_remote_code,
+                use_safetensors=True,
+                torch_dtype=self.dtype,
+            )
+            # Move model to device
+            self.model = self.model.eval().to(self.device)
+
+        except Exception as e:
+            _log.error(f"Failed to load DeepSeek-OCR model: {e}")
+            raise
+
+    def _validate_mps_pytorch_version(self, torch) -> None:
+        """Validate PyTorch version for MPS support.
+
+        PyTorch 2.7.0+ is required for MPS because earlier versions lack the
+        aten::_upsample_bicubic2d_aa operator needed by DeepSeek-OCR.
+        """
+        pytorch_version = _parse_pytorch_version(torch.__version__)
+        if pytorch_version < _MIN_PYTORCH_VERSION_MPS:
+            raise RuntimeError(
+                f"DeepSeek-OCR on Apple Silicon MPS requires PyTorch {'.'.join(map(str, _MIN_PYTORCH_VERSION_MPS))} or later. "
+                f"Current version: {torch.__version__}\n\n"
+                "Please upgrade PyTorch:\n"
+                "  pip install --upgrade torch>=2.7.0\n\n"
+                "Or use a different OCR engine for older PyTorch versions:\n"
+                "  * EasyOcrOptions - Supports MPS on older PyTorch\n"
+                "  * TesseractOcrOptions - CPU-based, no PyTorch dependency"
+            )
+
+    def _patch_llama_flash_attention(self) -> None:
+        """Patch missing LlamaFlashAttention2 for transformers compatibility.
+
+        The DeepSeek-OCR model's custom code imports LlamaFlashAttention2 from
+        transformers.models.llama.modeling_llama, but this class may not exist
+        in all versions of transformers. This method adds a dummy class to
+        prevent import errors.
+        """
+        try:
+            from transformers.models.llama import modeling_llama
+
+            if not hasattr(modeling_llama, "LlamaFlashAttention2"):
+                # Create a dummy class that will raise an error if actually used
+                class LlamaFlashAttention2Dummy:
+                    def __init__(self, *args, **kwargs):
+                        raise NotImplementedError(
+                            "LlamaFlashAttention2 is not available in this version of transformers. "
+                            "Please use attn_implementation='sdpa' or 'eager' instead of 'flash_attention_2', "
+                            "or upgrade transformers to a version that supports flash attention."
+                        )
+
+                modeling_llama.LlamaFlashAttention2 = LlamaFlashAttention2Dummy  # type: ignore[attr-defined]
+                _log.debug(
+                    "Patched missing LlamaFlashAttention2 in transformers.models.llama.modeling_llama"
+                )
+        except Exception as e:
+            _log.warning(f"Failed to patch LlamaFlashAttention2: {e}")
+
+    def __call__(
+        self, conv_res: ConversionResult, page_batch: Iterable[Page]
+    ) -> Iterable[Page]:
+        if not self.enabled:
+            yield from page_batch
+            return
+
+        for page in page_batch:
+            assert page._backend is not None
+            if not page._backend.is_valid():
+                yield page
+            else:
+                with TimeRecorder(conv_res, "ocr"):
+                    ocr_rects = self.get_ocr_rects(page)
+
+                    all_ocr_cells = []
+                    for ocr_rect in ocr_rects:
+                        # Skip zero area boxes
+                        if ocr_rect.area() == 0:
+                            continue
+
+                        # Get high resolution image for the OCR region
+                        high_res_image = page._backend.get_page_image(
+                            scale=self.scale, cropbox=ocr_rect
+                        )
+
+                        # Run DeepSeek-OCR inference
+                        cells = self._run_ocr(high_res_image, ocr_rect)
+                        all_ocr_cells.extend(cells)
+
+                        del high_res_image
+
+                    # Post-process the cells
+                    self.post_process_cells(all_ocr_cells, page)
+
+                # DEBUG code:
+                if settings.debug.visualize_ocr:
+                    self.draw_ocr_rects_and_cells(conv_res, page, ocr_rects)
+
+                yield page
+
+    def _run_ocr(self, image, ocr_rect: BoundingBox) -> list[TextCell]:
+        """Run DeepSeek-OCR on an image region and return TextCells.
+
+        Since DeepSeek-OCR returns text without bounding boxes, we create
+        a single TextCell covering the entire OCR region with the extracted text.
+        """
+        import tempfile
+
+        tmp_path = None
+        tmp_dir = None
+        try:
+            # Create a temporary directory for output (required by DeepSeek-OCR)
+            tmp_dir = tempfile.mkdtemp(prefix="deepseek_ocr_")
+
+            # Save image to temporary file (DeepSeek-OCR expects file path)
+            with tempfile.NamedTemporaryFile(
+                suffix=".png", delete=False, mode="wb", dir=tmp_dir
+            ) as tmp_file:
+                tmp_path = tmp_file.name
+                image.save(tmp_path, format="PNG")
+
+            _log.debug(f"DeepSeek-OCR processing image: {tmp_path}")
+
+            # Verify the file exists and has content
+            if not Path(tmp_path).exists():
+                _log.warning(f"Temp file was not created: {tmp_path}")
+                return []
+
+            # Run inference using DeepSeek-OCR's infer method
+            # Note: output_path is required even if save_results=False
+            # For MPS compatibility, we pass device and dtype explicitly
+            result = self.model.infer(
+                self.tokenizer,
+                device=self.device,
+                dtype=self.dtype,
+                prompt=self.options.prompt,
+                image_file=tmp_path,
+                output_path=tmp_dir,
+                base_size=self.options.base_size,
+                image_size=self.options.image_size,
+                crop_mode=self.options.crop_mode,
+                save_results=False,
+                test_compress=False,
+            )
+
+            # Parse the result text into lines
+            if result and isinstance(result, str) and result.strip():
+                cells = self._parse_text_to_cells(result, ocr_rect)
+                return cells
+
+            return []
+
+        except Exception as e:
+            error_msg = str(e)
+            # Detect transformers version incompatibility with MPS model
+            if "DynamicCache" in error_msg and (
+                "seen_tokens" in error_msg or "get_max_length" in error_msg
+            ):
+                _log.error(
+                    "DeepSeek-OCR MPS model incompatibility detected!\n\n"
+                    "The MPS-compatible model 'Dogacel/DeepSeek-OCR-Metal-MPS' uses deprecated "
+                    "transformers APIs (DynamicCache.seen_tokens) that are not compatible with "
+                    "your current transformers version.\n\n"
+                    "This is a known issue with the community-maintained MPS fork.\n"
+                    "See: https://github.com/Dogacel/DeepSeek-OCR-Metal-MPS/issues\n\n"
+                    "Workarounds:\n"
+                    "  1. Use a different OCR engine that supports MPS:\n"
+                    "     - EasyOcrOptions(lang=['en'])\n"
+                    "     - RapidOcrOptions()\n"
+                    "  2. Wait for the MPS model to be updated for newer transformers versions\n"
+                    "  3. Test in an isolated environment with transformers==4.43.4 (not recommended)\n"
+                )
+            else:
+                _log.warning(f"DeepSeek-OCR inference failed: {e}")
+            return []
+        finally:
+            # Clean up temp files and directory
+            if tmp_path:
+                Path(tmp_path).unlink(missing_ok=True)
+            if tmp_dir:
+                import shutil
+
+                shutil.rmtree(tmp_dir, ignore_errors=True)
+
+    def _parse_text_to_cells(self, text: str, ocr_rect: BoundingBox) -> list[TextCell]:
+        """Parse extracted text into TextCell objects.
+
+        Since DeepSeek-OCR doesn't provide bounding boxes, we create cells
+        based on text lines, distributing them evenly across the OCR region.
+        """
+        lines = [line.strip() for line in text.split("\n") if line.strip()]
+
+        if not lines:
+            return []
+
+        # Calculate approximate line height
+        region_height = ocr_rect.b - ocr_rect.t
+        line_height = region_height / len(lines) if lines else region_height
+
+        cells = []
+        for idx, line in enumerate(lines):
+            # Calculate approximate position for this line
+            top = ocr_rect.t + (idx * line_height)
+            bottom = top + line_height
+
+            cell = TextCell(
+                index=idx,
+                text=line,
+                orig=line,
+                from_ocr=True,
+                confidence=1.0,  # DeepSeek-OCR doesn't provide confidence scores
+                rect=BoundingRectangle.from_bounding_box(
+                    BoundingBox.from_tuple(
+                        coord=(
+                            ocr_rect.l / self.scale,
+                            top / self.scale,
+                            ocr_rect.r / self.scale,
+                            bottom / self.scale,
+                        ),
+                        origin=CoordOrigin.TOPLEFT,
+                    )
+                ),
+            )
+            cells.append(cell)
+
+        return cells
+
+    @classmethod
+    def get_options_type(cls) -> Type[OcrOptions]:
+        return DeepSeekOcrOptions
diff --git a/docling/models/plugins/defaults.py b/docling/models/plugins/defaults.py
index 11c8d7b323..8a75a747ed 100644
--- a/docling/models/plugins/defaults.py
+++ b/docling/models/plugins/defaults.py
@@ -1,5 +1,6 @@
 def ocr_engines():
     from docling.models.auto_ocr_model import OcrAutoModel
+    from docling.models.deepseek_ocr_model import DeepSeekOcrModel
     from docling.models.easyocr_model import EasyOcrModel
     from docling.models.ocr_mac_model import OcrMacModel
     from docling.models.rapid_ocr_model import RapidOcrModel
@@ -9,6 +10,7 @@ def ocr_engines():
     return {
         "ocr_engines": [
             OcrAutoModel,
+            DeepSeekOcrModel,
             EasyOcrModel,
             OcrMacModel,
             RapidOcrModel,
diff --git a/docs/getting_started/installation.md b/docs/getting_started/installation.md
index cb77184ce1..344796e55a 100644
--- a/docs/getting_started/installation.md
+++ b/docs/getting_started/installation.md
@@ -56,6 +56,7 @@ The following table summarizes the extras available in the `docling` package. Th
 | `tesserocr` | Installs the Tesseract binding for using it as OCR engine. |
 | `ocrmac` | Installs the OcrMac OCR engine. |
 | `rapidocr` | Installs the [RapidOCR](https://github.com/RapidAI/RapidOCR) OCR engine with [onnxruntime](https://github.com/microsoft/onnxruntime/) backend. |
+| `deepseecocr` | Installs the [DeepSeek-OCR](https://github.com/deepseek-ai/DeepSeek-OCR) VLM-based OCR engine. Supports CUDA (NVIDIA GPU) and MPS (Apple Silicon M1/M2/M3/M4). MPS requires PyTorch 2.7.0+. |
 
 
 ### OCR engines
@@ -72,6 +73,7 @@ the following engines.
 | OcrMac | System dependency. See description below. | `OcrMacOptions` |
 | [RapidOCR](https://github.com/RapidAI/RapidOCR) | `rapidocr` extra can or via `pip install rapidocr onnxruntime` | `RapidOcrOptions` |
 | [OnnxTR](https://github.com/felixdittrich92/OnnxTR) | Can be installed via the plugin system `pip install "docling-ocr-onnxtr[cpu]"`. Please take a look at [docling-OCR-OnnxTR](https://github.com/felixdittrich92/docling-OCR-OnnxTR).| `OnnxtrOcrOptions` |
+| [DeepSeek-OCR](https://github.com/deepseek-ai/DeepSeek-OCR) | `deepseecocr` extra or via `pip install transformers torch`. Supports CUDA (NVIDIA) and MPS (Apple Silicon M1/M2/M3/M4 with PyTorch 2.7.0+). | `DeepSeekOcrOptions` |
 
 The Docling `DocumentConverter` allows to choose the OCR engine with the `ocr_options` settings. For example
 
diff --git a/pyproject.toml b/pyproject.toml
index b4c402f23c..b854331bd8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -93,6 +93,15 @@ docling-tools = "docling.cli.tools:app"
 easyocr = ['easyocr (>=1.7,<2.0)']
 tesserocr = ['tesserocr (>=2.7.1,<3.0.0)']
 ocrmac = ['ocrmac (>=1.0.0,<2.0.0) ; sys_platform == "darwin"']
+deepseecocr = [
+  'transformers (>=4.46.0,<5.0.0)',
+  'torch (>=2.0.0)',
+  'einops',
+  'Pillow (>=10.0.0)',
+  'addict',
+  'easydict',
+  'matplotlib',
+]
 vlm = [
   'transformers (>=4.46.0,<5.0.0)',
   'accelerate (>=1.2.1,<2.0.0)',
diff --git a/tests/test_deepseek_ocr_model.py b/tests/test_deepseek_ocr_model.py
new file mode 100644
index 0000000000..c15e420f2a
--- /dev/null
+++ b/tests/test_deepseek_ocr_model.py
@@ -0,0 +1,393 @@
+"""
+Test DeepSeek-OCR model integration with mock-based unit tests.
+
+These tests use mocking to test the DeepSeek-OCR model logic without requiring
+actual GPU hardware (CUDA or MPS), following the pattern used in test_asr_mlx_whisper.py.
+"""
+
+import sys
+from pathlib import Path
+from unittest.mock import MagicMock, Mock, patch
+
+import pytest
+from docling_core.types.doc import BoundingBox, CoordOrigin
+
+from docling.datamodel.accelerator_options import (
+    AcceleratorDevice,
+    AcceleratorOptions,
+)
+from docling.datamodel.pipeline_options import DeepSeekOcrOptions
+from docling.models.deepseek_ocr_model import (
+    _DEEPSEEK_OCR_CUDA_REPO,
+    _DEEPSEEK_OCR_MPS_REPO,
+    DeepSeekOcrModel,
+    _parse_pytorch_version,
+)
+
+
+class TestParsePytorchVersion:
+    """Test PyTorch version parsing utility."""
+
+    def test_parse_standard_version(self):
+        """Test parsing standard version strings."""
+        assert _parse_pytorch_version("2.7.0") == (2, 7, 0)
+        assert _parse_pytorch_version("2.6.1") == (2, 6, 1)
+        assert _parse_pytorch_version("1.13.0") == (1, 13, 0)
+
+    def test_parse_version_with_suffix(self):
+        """Test parsing versions with CUDA/dev suffixes."""
+        assert _parse_pytorch_version("2.7.0+cu118") == (2, 7, 0)
+        assert _parse_pytorch_version("2.7.0.dev20241201") == (2, 7, 0)
+        assert _parse_pytorch_version("2.7.0+cpu") == (2, 7, 0)
+
+    def test_parse_invalid_version(self):
+        """Test parsing invalid version strings."""
+        assert _parse_pytorch_version("invalid") == (0, 0, 0)
+        assert _parse_pytorch_version("") == (0, 0, 0)
+
+
+class TestDeepSeekOcrOptions:
+    """Test DeepSeekOcrOptions configuration."""
+
+    def test_default_options(self):
+        """Test default option values."""
+        options = DeepSeekOcrOptions()
+        assert options.kind == "deepseecocr"
+        assert options.repo_id == "deepseek-ai/DeepSeek-OCR"
+        assert options.prompt == "<image>\nFree OCR."
+        assert options.base_size == 1024
+        assert options.image_size == 640
+        assert options.crop_mode is True
+        assert options.trust_remote_code is True
+        assert options.lang == []
+
+    def test_custom_options(self):
+        """Test custom option values."""
+        options = DeepSeekOcrOptions(
+            repo_id="custom/model",
+            prompt="<image>\nConvert to markdown.",
+            base_size=2048,
+            image_size=1280,
+        )
+        assert options.repo_id == "custom/model"
+        assert options.prompt == "<image>\nConvert to markdown."
+        assert options.base_size == 2048
+        assert options.image_size == 1280
+
+
+class TestDeepSeekOcrModelDisabled:
+    """Test DeepSeekOcrModel when disabled."""
+
+    def test_disabled_model_no_init(self):
+        """Test that disabled model doesn't initialize GPU resources."""
+        options = DeepSeekOcrOptions()
+        accelerator_options = AcceleratorOptions(device=AcceleratorDevice.AUTO)
+
+        # Should not raise even without GPU
+        model = DeepSeekOcrModel(
+            enabled=False,
+            artifacts_path=None,
+            options=options,
+            accelerator_options=accelerator_options,
+        )
+
+        assert model.enabled is False
+        assert model.device is None
+        assert model.dtype is None
+
+
+class TestDeepSeekOcrModelDeviceDetection:
+    """Test device detection logic with mocked torch."""
+
+    def test_cuda_device_detection(self, monkeypatch):
+        """Test CUDA device is selected when available."""
+        # Create mock torch module
+        mock_torch = MagicMock()
+        mock_torch.__version__ = "2.7.0"
+        mock_torch.backends.cuda.is_built.return_value = True
+        mock_torch.cuda.is_available.return_value = True
+        mock_torch.backends.mps.is_built.return_value = False
+        mock_torch.backends.mps.is_available.return_value = False
+        mock_torch.device.return_value = Mock()
+        mock_torch.bfloat16 = "bfloat16"
+
+        # Mock transformers
+        mock_transformers = MagicMock()
+        mock_model = MagicMock()
+        mock_model.eval.return_value = mock_model
+        mock_model.to.return_value = mock_model
+        mock_transformers.AutoModel.from_pretrained.return_value = mock_model
+        mock_transformers.AutoTokenizer.from_pretrained.return_value = MagicMock()
+
+        monkeypatch.setitem(sys.modules, "torch", mock_torch)
+        monkeypatch.setitem(sys.modules, "transformers", mock_transformers)
+
+        options = DeepSeekOcrOptions()
+        accelerator_options = AcceleratorOptions(device=AcceleratorDevice.CUDA)
+
+        with patch.object(
+            DeepSeekOcrModel, "_patch_llama_flash_attention", return_value=None
+        ):
+            model = DeepSeekOcrModel(
+                enabled=True,
+                artifacts_path=None,
+                options=options,
+                accelerator_options=accelerator_options,
+            )
+
+        mock_torch.device.assert_called_with("cuda")
+        assert model.dtype == "bfloat16"
+
+    def test_mps_device_detection(self, monkeypatch):
+        """Test MPS device is selected on Apple Silicon."""
+        # Create mock torch module
+        mock_torch = MagicMock()
+        mock_torch.__version__ = "2.7.0"
+        mock_torch.backends.cuda.is_built.return_value = False
+        mock_torch.cuda.is_available.return_value = False
+        mock_torch.backends.mps.is_built.return_value = True
+        mock_torch.backends.mps.is_available.return_value = True
+        mock_torch.device.return_value = Mock()
+        mock_torch.float16 = "float16"
+
+        # Mock transformers
+        mock_transformers = MagicMock()
+        mock_model = MagicMock()
+        mock_model.eval.return_value = mock_model
+        mock_model.to.return_value = mock_model
+        mock_transformers.AutoModel.from_pretrained.return_value = mock_model
+        mock_transformers.AutoTokenizer.from_pretrained.return_value = MagicMock()
+
+        monkeypatch.setitem(sys.modules, "torch", mock_torch)
+        monkeypatch.setitem(sys.modules, "transformers", mock_transformers)
+
+        options = DeepSeekOcrOptions()
+        accelerator_options = AcceleratorOptions(device=AcceleratorDevice.MPS)
+
+        with patch.object(
+            DeepSeekOcrModel, "_patch_llama_flash_attention", return_value=None
+        ):
+            model = DeepSeekOcrModel(
+                enabled=True,
+                artifacts_path=None,
+                options=options,
+                accelerator_options=accelerator_options,
+            )
+
+        mock_torch.device.assert_called_with("mps")
+        assert model.dtype == "float16"
+
+    def test_no_gpu_raises_error(self, monkeypatch):
+        """Test that RuntimeError is raised when no GPU is available."""
+        # Create mock torch module with no GPU
+        mock_torch = MagicMock()
+        mock_torch.__version__ = "2.7.0"
+        mock_torch.backends.cuda.is_built.return_value = False
+        mock_torch.cuda.is_available.return_value = False
+        mock_torch.backends.mps.is_built.return_value = False
+        mock_torch.backends.mps.is_available.return_value = False
+
+        # Mock transformers
+        mock_transformers = MagicMock()
+
+        monkeypatch.setitem(sys.modules, "torch", mock_torch)
+        monkeypatch.setitem(sys.modules, "transformers", mock_transformers)
+
+        options = DeepSeekOcrOptions()
+        accelerator_options = AcceleratorOptions(device=AcceleratorDevice.AUTO)
+
+        with pytest.raises(RuntimeError, match="DeepSeek-OCR requires a GPU"):
+            DeepSeekOcrModel(
+                enabled=True,
+                artifacts_path=None,
+                options=options,
+                accelerator_options=accelerator_options,
+            )
+
+    def test_mps_old_pytorch_raises_error(self, monkeypatch):
+        """Test that old PyTorch version raises error for MPS."""
+        # Create mock torch module with old version
+        mock_torch = MagicMock()
+        mock_torch.__version__ = "2.5.0"  # Below 2.7.0 requirement
+        mock_torch.backends.cuda.is_built.return_value = False
+        mock_torch.cuda.is_available.return_value = False
+        mock_torch.backends.mps.is_built.return_value = True
+        mock_torch.backends.mps.is_available.return_value = True
+
+        # Mock transformers
+        mock_transformers = MagicMock()
+
+        monkeypatch.setitem(sys.modules, "torch", mock_torch)
+        monkeypatch.setitem(sys.modules, "transformers", mock_transformers)
+
+        options = DeepSeekOcrOptions()
+        accelerator_options = AcceleratorOptions(device=AcceleratorDevice.MPS)
+
+        with pytest.raises(RuntimeError, match=r"requires PyTorch 2\.7\.0"):
+            DeepSeekOcrModel(
+                enabled=True,
+                artifacts_path=None,
+                options=options,
+                accelerator_options=accelerator_options,
+            )
+
+    def test_mps_auto_switches_to_mps_repo(self, monkeypatch):
+        """Test that MPS auto-switches to MPS-compatible model repo."""
+        # Create mock torch module
+        mock_torch = MagicMock()
+        mock_torch.__version__ = "2.7.0"
+        mock_torch.backends.cuda.is_built.return_value = False
+        mock_torch.cuda.is_available.return_value = False
+        mock_torch.backends.mps.is_built.return_value = True
+        mock_torch.backends.mps.is_available.return_value = True
+        mock_torch.device.return_value = Mock()
+        mock_torch.float16 = "float16"
+
+        # Mock transformers
+        mock_transformers = MagicMock()
+        mock_model = MagicMock()
+        mock_model.eval.return_value = mock_model
+        mock_model.to.return_value = mock_model
+        mock_transformers.AutoModel.from_pretrained.return_value = mock_model
+        mock_transformers.AutoTokenizer.from_pretrained.return_value = MagicMock()
+
+        monkeypatch.setitem(sys.modules, "torch", mock_torch)
+        monkeypatch.setitem(sys.modules, "transformers", mock_transformers)
+
+        # Use default CUDA repo - should auto-switch to MPS repo
+        options = DeepSeekOcrOptions(repo_id=_DEEPSEEK_OCR_CUDA_REPO)
+        accelerator_options = AcceleratorOptions(device=AcceleratorDevice.MPS)
+
+        with patch.object(
+            DeepSeekOcrModel, "_patch_llama_flash_attention", return_value=None
+        ):
+            DeepSeekOcrModel(
+                enabled=True,
+                artifacts_path=None,
+                options=options,
+                accelerator_options=accelerator_options,
+            )
+
+        # Verify MPS-compatible model was loaded
+        mock_transformers.AutoModel.from_pretrained.assert_called_once()
+        call_args = mock_transformers.AutoModel.from_pretrained.call_args
+        assert call_args[0][0] == _DEEPSEEK_OCR_MPS_REPO
+
+
+class TestDeepSeekOcrModelTextParsing:
+    """Test text parsing logic."""
+
+    def test_parse_text_to_cells_single_line(self):
+        """Test parsing single line of text."""
+        options = DeepSeekOcrOptions()
+        accelerator_options = AcceleratorOptions(device=AcceleratorDevice.AUTO)
+
+        model = DeepSeekOcrModel(
+            enabled=False,
+            artifacts_path=None,
+            options=options,
+            accelerator_options=accelerator_options,
+        )
+        model.scale = 3
+
+        ocr_rect = BoundingBox(l=0, t=0, r=300, b=100, coord_origin=CoordOrigin.TOPLEFT)
+        cells = model._parse_text_to_cells("Hello World", ocr_rect)
+
+        assert len(cells) == 1
+        assert cells[0].text == "Hello World"
+        assert cells[0].from_ocr is True
+        assert cells[0].confidence == 1.0
+
+    def test_parse_text_to_cells_multiple_lines(self):
+        """Test parsing multiple lines of text."""
+        options = DeepSeekOcrOptions()
+        accelerator_options = AcceleratorOptions(device=AcceleratorDevice.AUTO)
+
+        model = DeepSeekOcrModel(
+            enabled=False,
+            artifacts_path=None,
+            options=options,
+            accelerator_options=accelerator_options,
+        )
+        model.scale = 3
+
+        ocr_rect = BoundingBox(l=0, t=0, r=300, b=300, coord_origin=CoordOrigin.TOPLEFT)
+        text = "Line 1\nLine 2\nLine 3"
+        cells = model._parse_text_to_cells(text, ocr_rect)
+
+        assert len(cells) == 3
+        assert cells[0].text == "Line 1"
+        assert cells[1].text == "Line 2"
+        assert cells[2].text == "Line 3"
+
+    def test_parse_text_to_cells_empty(self):
+        """Test parsing empty text."""
+        options = DeepSeekOcrOptions()
+        accelerator_options = AcceleratorOptions(device=AcceleratorDevice.AUTO)
+
+        model = DeepSeekOcrModel(
+            enabled=False,
+            artifacts_path=None,
+            options=options,
+            accelerator_options=accelerator_options,
+        )
+        model.scale = 3
+
+        ocr_rect = BoundingBox(l=0, t=0, r=300, b=100, coord_origin=CoordOrigin.TOPLEFT)
+        cells = model._parse_text_to_cells("", ocr_rect)
+
+        assert len(cells) == 0
+
+    def test_parse_text_to_cells_whitespace_only(self):
+        """Test parsing whitespace-only text."""
+        options = DeepSeekOcrOptions()
+        accelerator_options = AcceleratorOptions(device=AcceleratorDevice.AUTO)
+
+        model = DeepSeekOcrModel(
+            enabled=False,
+            artifacts_path=None,
+            options=options,
+            accelerator_options=accelerator_options,
+        )
+        model.scale = 3
+
+        ocr_rect = BoundingBox(l=0, t=0, r=300, b=100, coord_origin=CoordOrigin.TOPLEFT)
+        cells = model._parse_text_to_cells("   \n\n   ", ocr_rect)
+
+        assert len(cells) == 0
+
+
+class TestDeepSeekOcrModelGetOptionsType:
+    """Test get_options_type class method."""
+
+    def test_get_options_type(self):
+        """Test that get_options_type returns DeepSeekOcrOptions."""
+        assert DeepSeekOcrModel.get_options_type() == DeepSeekOcrOptions
+
+
+class TestDeepSeekOcrModelImportError:
+    """Test import error handling."""
+
+    def test_missing_torch_raises_import_error(self, monkeypatch):
+        """Test that missing torch raises ImportError with helpful message."""
+        # We test the import error message by checking the code path
+        # The actual import error is raised in _init_model when torch/transformers
+        # are not available. We verify the error message format.
+        options = DeepSeekOcrOptions()
+        accelerator_options = AcceleratorOptions(device=AcceleratorDevice.AUTO)
+
+        # Mock the _init_model to raise the expected ImportError
+        def mock_init_model(self, accel_opts, artifacts_path):
+            raise ImportError(
+                "DeepSeek-OCR requires 'transformers' and 'torch' packages. "
+                "Please install them via `pip install docling[deepseecocr]` to use this OCR engine."
+            )
+
+        with patch.object(DeepSeekOcrModel, "_init_model", mock_init_model):
+            with pytest.raises(ImportError, match=r"transformers.*torch"):
+                DeepSeekOcrModel(
+                    enabled=True,
+                    artifacts_path=None,
+                    options=options,
+                    accelerator_options=accelerator_options,
+                )
diff --git a/tests/test_e2e_ocr_conversion.py b/tests/test_e2e_ocr_conversion.py
index 22c4673873..778f086c8d 100644
--- a/tests/test_e2e_ocr_conversion.py
+++ b/tests/test_e2e_ocr_conversion.py
@@ -1,3 +1,4 @@
+import os
 import sys
 from pathlib import Path
 from typing import List, Tuple
@@ -10,6 +11,7 @@
 from docling.datamodel.base_models import InputFormat
 from docling.datamodel.document import ConversionResult
 from docling.datamodel.pipeline_options import (
+    DeepSeekOcrOptions,
     EasyOcrOptions,
     OcrMacOptions,
     OcrOptions,
@@ -36,13 +38,15 @@ def get_pdf_paths():
     return pdf_files
 
 
-def get_converter(ocr_options: OcrOptions):
+def get_converter(
+    ocr_options: OcrOptions, device: AcceleratorDevice = AcceleratorDevice.CPU
+):
     pipeline_options = PdfPipelineOptions()
     pipeline_options.do_ocr = True
     pipeline_options.do_table_structure = True
     pipeline_options.table_structure_options.do_cell_matching = True
     pipeline_options.ocr_options = ocr_options
-    pipeline_options.accelerator_options.device = AcceleratorDevice.CPU
+    pipeline_options.accelerator_options.device = device
 
     converter = DocumentConverter(
         format_options={
@@ -97,11 +101,68 @@ def test_e2e_conversions():
         engines.append((OcrMacOptions(), True))
         engines.append((OcrMacOptions(force_full_page_ocr=True), True))
 
+    # DeepSeek OCR - requires GPU (CUDA or MPS) and transformers
+    # Only run if explicitly enabled via environment variable
+    # Set DOCLING_TEST_DEEPSEECOCR=true to include DeepSeek-OCR tests
+    deepseecocr_engines: List[Tuple[OcrOptions, bool, AcceleratorDevice]] = []
+    if os.environ.get("DOCLING_TEST_DEEPSEECOCR", "").lower() == "true":
+        try:
+            import torch
+
+            # Check for CUDA (preferred) or MPS (Apple Silicon fallback)
+            if torch.cuda.is_available():
+                deepseecocr_engines.append(
+                    (DeepSeekOcrOptions(), False, AcceleratorDevice.CUDA)
+                )
+                deepseecocr_engines.append(
+                    (
+                        DeepSeekOcrOptions(force_full_page_ocr=True),
+                        False,
+                        AcceleratorDevice.CUDA,
+                    )
+                )
+            elif torch.backends.mps.is_built() and torch.backends.mps.is_available():
+                # MPS requires PyTorch 2.7.0+ for DeepSeek-OCR
+                # The model will auto-switch to MPS-compatible version
+                deepseecocr_engines.append(
+                    (DeepSeekOcrOptions(), False, AcceleratorDevice.MPS)
+                )
+                deepseecocr_engines.append(
+                    (
+                        DeepSeekOcrOptions(force_full_page_ocr=True),
+                        False,
+                        AcceleratorDevice.MPS,
+                    )
+                )
+        except ImportError:
+            pass  # Skip if torch is not available
+
+    # Run CPU-based OCR engines
     for ocr_options, supports_rotation in engines:
         print(
             f"Converting with ocr_engine: {ocr_options.kind}, language: {ocr_options.lang}"
         )
-        converter = get_converter(ocr_options=ocr_options)
+        converter = get_converter(ocr_options=ocr_options, device=AcceleratorDevice.CPU)
+        for pdf_path in pdf_paths:
+            if not supports_rotation and "rotated" in pdf_path.name:
+                continue
+            print(f"converting {pdf_path}")
+
+            doc_result: ConversionResult = converter.convert(pdf_path)
+
+            verify_conversion_result_v2(
+                input_path=pdf_path,
+                doc_result=doc_result,
+                generate=GENERATE_V2,
+                fuzzy=True,
+            )
+
+    # Run GPU-based DeepSeek-OCR tests (requires CUDA or MPS)
+    for ocr_options, supports_rotation, device in deepseecocr_engines:
+        print(
+            f"Converting with ocr_engine: {ocr_options.kind}, device: {device}, language: {ocr_options.lang}"
+        )
+        converter = get_converter(ocr_options=ocr_options, device=device)
         for pdf_path in pdf_paths:
             if not supports_rotation and "rotated" in pdf_path.name:
                 continue
diff --git a/uv.lock b/uv.lock
index d9fc41d83c..450d717176 100644
--- a/uv.lock
+++ b/uv.lock
@@ -67,6 +67,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/77/85/85951bc0f9843e2c10baaa1b6657227056095de08f4d1eea7d8b423a6832/accelerate-1.11.0-py3-none-any.whl", hash = "sha256:a628fa6beb069b8e549460fc449135d5bd8d73e7a11fd09f0bc9fc4ace7f06f1", size = 375777, upload-time = "2025-10-20T14:42:23.256Z" },
 ]
 
+[[package]]
+name = "addict"
+version = "2.4.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/85/ef/fd7649da8af11d93979831e8f1f8097e85e82d5bfeabc8c68b39175d8e75/addict-2.4.0.tar.gz", hash = "sha256:b3b2210e0e067a281f5646c8c5db92e99b7231ea8b0eb5f74dbdf9e259d4e494", size = 9186, upload-time = "2020-11-21T16:21:31.416Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6a/00/b08f23b7d7e1e14ce01419a467b583edbb93c6cdb8654e54a9cc579cd61f/addict-2.4.0-py3-none-any.whl", hash = "sha256:249bb56bbfd3cdc2a004ea0ff4c2b6ddc84d53bc2194761636eb314d5cfa5dfc", size = 3832, upload-time = "2020-11-21T16:21:29.588Z" },
+]
+
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
@@ -988,6 +997,247 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d2/81/e3073017a8f5c75169e79108eda209e6089e3f96c9f197d307cbda7df71c/compressed_tensors-0.11.0-py3-none-any.whl", hash = "sha256:e1cbc46e1ae032b7ceea915fe18c8d2de5a54d3a50a607969b6bdfe703b6cb83", size = 179951, upload-time = "2025-08-19T18:59:29.308Z" },
 ]
 
+[[package]]
+name = "contourpy"
+version = "1.3.0"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version < '3.10'",
+]
+dependencies = [
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f5/f6/31a8f28b4a2a4fa0e01085e542f3081ab0588eff8e589d39d775172c9792/contourpy-1.3.0.tar.gz", hash = "sha256:7ffa0db17717a8ffb127efd0c95a4362d996b892c2904db72428d5b52e1938a4", size = 13464370, upload-time = "2024-08-27T21:00:03.328Z" }
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+    { name = "transformers" },
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+    { name = "einops", marker = "extra == 'deepseecocr'" },
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+provides-extras = ["easyocr", "tesserocr", "ocrmac", "deepseecocr", "vlm", "rapidocr", "asr"]
 
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