Deepseek Ocr

# DeepSeek-OCR

> Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection.

DeepSeek-OCR is a vision-language model for Optical Character Recognition with "Contexts Optical Compression." It supports native and dynamic resolutions, multiple prompt modes (document-to-markdown, free OCR, figure parsing, grounding), and can be run via vLLM (high-throughput) or HuggingFace Transformers. It processes images and PDFs, outputting structured text or markdown.

---

## Installation

### Prerequisites
- CUDA 11.8+, PyTorch 2.6.0
- Python 3.12.9 (via conda recommended)

### Setup

```bash
git clone https://github.com/deepseek-ai/DeepSeek-OCR.git
cd DeepSeek-OCR

conda create -n deepseek-ocr python=3.12.9 -y
conda activate deepseek-ocr

# Install PyTorch with CUDA 11.8
pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 \
  --index-url https://download.pytorch.org/whl/cu118

# Download vllm-0.8.5 whl from https://github.com/vllm-project/vllm/releases/tag/v0.8.5
pip install vllm-0.8.5+cu118-cp38-abi3-manylinux1_x86_64.whl

pip install -r requirements.txt
pip install flash-attn==2.7.3 --no-build-isolation
```

### Alternative: upstream vLLM (nightly)

```bash
uv venv
source .venv/bin/activate
uv pip install -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
```

---

## Model Download

Model is available on HuggingFace: `deepseek-ai/DeepSeek-OCR`

```python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="deepseek-ai/DeepSeek-OCR")
```

---

## Inference: vLLM (Recommended for Production)

### Single Image — Streaming

```python
from vllm import LLM, SamplingParams
from vllm.model_executor.models.deepseek_ocr import NGramPerReqLogitsProcessor
from PIL import Image

llm = LLM(
    model="deepseek-ai/DeepSeek-OCR",
    enable_prefix_caching=False,
    mm_processor_cache_gb=0,
    logits_processors=[NGramPerReqLogitsProcessor]
)

image = Image.open("document.png").convert("RGB")
prompt = "<image>\nFree OCR."

sampling_params = SamplingParams(
    temperature=0.0,
    max_tokens=8192,
    extra_args=dict(
        ngram_size=30,
        window_size=90,
        whitelist_token_ids={128821, 128822},  # <td>, </td> for table support
    ),
    skip_special_tokens=False,
)

outputs = llm.generate(
    [{"prompt": prompt, "multi_modal_data": {"image": image}}],
    sampling_params
)

print(outputs[0].outputs[0].text)
```

### Batch Images

```python
from vllm import LLM, SamplingParams
from vllm.model_executor.models.deepseek_ocr import NGramPerReqLogitsProcessor
from PIL import Image

llm = LLM(
    model="deepseek-ai/DeepSeek-OCR",
    enable_prefix_caching=False,
    mm_processor_cache_gb=0,
    logits_processors=[NGramPerReqLogitsProcessor]
)

image_paths = ["page1.png", "page2.png", "page3.png"]
prompt = "<image>\n<|grounding|>Convert the document to markdown. "

model_input = [
    {
        "prompt": prompt,
        "multi_modal_data": {"image": Image.open(p).convert("RGB")}
    }
    for p in image_paths
]

sampling_params = SamplingParams(
    temperature=0.0,
    max_tokens=8192,
    extra_args=dict(
        ngram_size=30,
        window_size=90,
        whitelist_token_ids={128821, 128822},
    ),
    skip_special_tokens=False,
)

outputs = llm.generate(model_input, sampling_params)

for path, output in zip(image_paths, outputs):
    print(f"=== {path} ===")
    print(output.outputs[0].text)
```

### PDF Processing (via vLLM scripts)

```bash
cd DeepSeek-OCR-master/DeepSeek-OCR-vllm
# Edit config.py: set INP