Modelpublic

strangervisionhf/deepseek-ocr-2-transformers-v4.57.1

sourceHugging Faceapache-2.0updated 7mo agoView on Hugging Face

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Model Card

[!IMPORTANT] This is a copy of the model weights from the https://huggingface.co/deepseek-ai/DeepSeek-OCR-2 model. These weights cannot be used for other purposes. If you wish to do so, please visit the original model page.

Previously, inference with the model https://huggingface.co/deepseek-ai/DeepSeek-OCR-2 ran smoothly on transformers==4.46.3. However, running it on newer versions of transformers caused compatibility issues. We have identified and fixed the problem, and the model now runs smoothly with the latest `transformers (v4.57.1)` and other compatible versions.

This page includes the updated model weights and corrected configuration, which resolve the issue and allow transformers inference to run smoothly.

[!note]

Last updated: 11:50 AM (IST), February 12, 2026.

Quick Start with Transformers

Install the required packages

torch
torchvision
transformers==4.57.1
accelerate
matplotlib 
einops
addict 
easydict

Usage

python

from transformers import AutoModel, AutoTokenizer
import torch
import os
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
model_name = 'strangervisionhf/deepseek-ocr-2-transformers-v4.57.1'

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)