Run jinaai/jina-ocr-v1 locally as a document OCR model using Transformers with the custom model code from the Hugging Face snapshot. You will install the Python dependencies, load the model on a GPU if available (otherwise CPU), run OCR on a sample image, and confirm the model produced readable text.
Run jinaai/jina-ocr-v1 locally as a document OCR model using Transformers with the custom model code from the Hugging Face snapshot. You will install the Python dependencies, load the model on a GPU if available (otherwise CPU), run OCR on a sample image, and confirm the model produced readable text.
This setup uses the package documented for this task. Review its source and supported platforms before starting.
The publisher has not provided a complete memory target for this task. Check its hardware guidance before starting.
This setup uses Transformers. The exact checkpoint format, context, and runtime overhead determine its memory need; a weight-file size alone is not a GPU recommendation.
Pick your operating system. Every command below is for the selected package and runtime.
The publisher supplies Python code but does not specify an operating system. Linux is a practical starting environment, not a publisher-tested machine. Run the Python snippets in one session, in order.
Install transformers, torch, torchvision, and Pillow in the Python environment you will use. These packages provide the AutoProcessor/AutoModelForCausalLM loading path and image loading used by the model's custom code.
Load the model and processor from the official repository so the snapshot includes both the weights and the custom modeling code. Use trust_remote_code=True so the repository's own modeling files are used; there is no separate training or serving package to install.
Save a document page as document.png in your working directory. This script loads the model onto CUDA when available (otherwise CPU), prepares the OCR inputs, generates up to 4096 new tokens greedily, and decodes the OCR text. Keep the eos/pad settings from generation_config.json and do not pass a fresh GenerationConfig.
import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor
MODEL_ID = 'jinaai/jina-ocr-v1'
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID, dtype=torch.bfloat16, trust_remote_code=True,
).to(device)
image = Image.open('document.png').convert('RGB')
inputs = processor.prepare_ocr_inputs(image, device=device)
output = model.generate(**inputs, max_new_tokens=4096, do_sample=False)
print(processor.decode_ocr(output, inputs['input_ids']))Look at the printed output of decode_ocr. It should contain text from document.png rather than an empty string or a repetition loop. For a chat-style first task, pass your own instruction through prepare_ocr_inputs, for example processor.prepare_ocr_inputs(image, prompt='Transcribe the provided document image into a clean Markdown format, preserving the natural reading order.', device=device), and check that the printed decoded output follows that instruction.
Ask a short question with a known answer. Confirm the selected local model responds and verify the answer yourself before using it for private work.
This is a source-linked setup, not a YouRunAI hardware test. Confirm your exact runtime version, package, and output before relying on it.
generate() attaches SlidingWindowNoRepeatNgramProcessor with no_repeat_ngram_size=35 and ngram_window=1024 by default. If the repetition whitelist causes issues with your document, override the keyword arguments in model.generate(...) with your own values, or disable the processor entirely with no_repeat_ngram_size=0.
Ensure you installed transformers, torch, torchvision, and Pillow, and that you pass trust_remote_code=True to both AutoProcessor.from_pretrained and AutoModelForCausalLM.from_pretrained. The repository ships the custom code needed to load the weights.
Load with a smaller dtype or run on CPU. The device is selected with torch.device('cuda' if torch.cuda.is_available() else 'cpu'); forcing CPU avoids GPU memory limits when the model cannot fit on the available device.
Original instructions, model files, and compatibility notes behind this setup.
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