unsloth/DeepSeek-OCR-2

image-text-to-texttransformersUpstream checksumstransformerssafetensorsdeepseek_vl_v2feature-extractiondeepseekunsloth

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Read our Guide How to: Run & Fine-tune DeepSeek-OCR 2.

This DeepSeek-OCR 2 upload was edited to enable inference & fine-tuning on the latest transformers (no accuracy change).

✨ Read our DeepSeek-OCR 2 Guide here!

Fine-tune DeepSeek-OCR 2 for free using our Google Colab notebook
View the rest of our notebooks in our docs here.

🌟 Github | 📥 Model Download | 📄 Paper Link | 📄 Arxiv Paper Link |

DeepSeek-OCR 2: Visual Causal Flow [blocked]

[Image blocked: No description]

Explore more human-like visual encoding. [blocked]

Usage

Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.12.9 + CUDA11.8：

python

from transformers import AutoModel, AutoTokenizerimport torchimport osos.environ["CUDA_VISIBLE_DEVICES"] = '0'model_name = 'deepseek-ai/DeepSeek-OCR-2'
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)model = AutoModel.from_pretrained(model_name, _attn_implementation='flash_attention_2', trust_remote_code=True, use_safetensors=True)model = model.eval().cuda().to(torch.bfloat16)
# prompt = "<image>\nFree OCR. "prompt = "<image>\n<|grounding|>Convert the document to markdown. "image_file = 'your_image.jpg'output_path = 'your/output/dir'

res = model.infer(tokenizer, prompt=prompt, image_file=image_file, output_path = output_path, base_size = 1024, image_size = 768, crop_mode=True, save_results = True)

vLLM

Refer to 🌟GitHub for guidance on model inference acceleration and PDF processing, etc.

Support-Modes

Dynamic resolution
- Default: (0-6)×768×768 + 1×1024×1024 — (0-6)×144 + 256 visual tokens ✅

Prompts examples

python

# document: <image>\n<|grounding|>Convert the document to markdown.# other image: <image>\n<|grounding|>OCR this image.# without layouts: <image>\nFree OCR.# figures in document: <image>\nParse the figure.# general: <image>\nDescribe this image in detail.# rec: <image>\nLocate <|ref|>xxxx<|/ref|> in the image.

Acknowledgement

We would like to thank DeepSeek-OCR, Vary, GOT-OCR2.0, MinerU, PaddleOCR for their valuable models and ideas.

We also appreciate the benchmark OmniDocBench.

Citation

Read our Guide How to: Run & Fine-tune DeepSeek-OCR 2.

This DeepSeek-OCR 2 upload was edited to enable inference & fine-tuning on the latest transformers (no accuracy change).

✨ Read our DeepSeek-OCR 2 Guide here!

Fine-tune DeepSeek-OCR 2 for free using our Google Colab notebook

View the rest of our notebooks in our docs here.

Usage

Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.12.9 + CUDA11.8：

python

from transformers import AutoModel, AutoTokenizerimport torchimport osos.environ["CUDA_VISIBLE_DEVICES"] = '0'model_name = 'deepseek-ai/DeepSeek-OCR-2'
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)model = AutoModel.from_pretrained(model_name, _attn_implementation='flash_attention_2', trust_remote_code=True, use_safetensors=True)model = model.eval().cuda().to(torch.bfloat16)
# prompt = "<image>\nFree OCR. "prompt = "<image>\n<|grounding|>Convert the document to markdown. "image_file = 'your_image.jpg'output_path = 'your/output/dir'

res = model.infer(tokenizer, prompt=prompt, image_file=image_file, output_path = output_path, base_size = 1024, image_size = 768, crop_mode=True, save_results = True)

from transformers import AutoModel, AutoTokenizerimport torchimport osos.environ["CUDA_VISIBLE_DEVICES"] = '0'model_name = 'deepseek-ai/DeepSeek-OCR-2'
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)model = AutoModel.from_pretrained(model_name, _attn_implementation='flash_attention_2', trust_remote_code=True, use_safetensors=True)model = model.eval().cuda().to(torch.bfloat16)
# prompt = "<image>\nFree OCR. "prompt = "<image>\n<|grounding|>Convert the document to markdown. "image_file = 'your_image.jpg'output_path = 'your/output/dir'

res = model.infer(tokenizer, prompt=prompt, image_file=image_file, output_path = output_path, base_size = 1024, image_size = 768, crop_mode=True, save_results = True)

Prompts examples

python

# document: <image>\n<|grounding|>Convert the document to markdown.# other image: <image>\n<|grounding|>OCR this image.# without layouts: <image>\nFree OCR.# figures in document: <image>\nParse the figure.# general: <image>\nDescribe this image in detail.# rec: <image>\nLocate <|ref|>xxxx<|/ref|> in the image.

# document: <image>\n<|grounding|>Convert the document to markdown.# other image: <image>\n<|grounding|>OCR this image.# without layouts: <image>\nFree OCR.# figures in document: <image>\nParse the figure.# general: <image>\nDescribe this image in detail.# rec: <image>\nLocate <|ref|>xxxx<|/ref|> in the image.