Text / jinaai

jina ocr v1

jina-ocr-v1 is an end-to-end document parsing model built on DeepSeek-OCR, using a DeepEncoder vision tower and a 3B-parameter mixture-of-experts decoder with about 570M active parameters per token. It includes a FastMTP speculative decoding head. The repository includes model weights and custom code for loading. Reported scores include 91.14 overall on OmniDocBench v1.6 and 83.4 overall on olmOCR-Bench. It is licensed cc-by-nc-4.0, tagged multilingual, and supports an image-text-to-text pipeline.

EstimatedRepository opened Sep 1, 2026Source checked 9/24/2026Version: 904f815d
LOCALRENTED GPUOPEN WEIGHTS
Freshness 77 out of 100. Repository age: 68 days old, Sep 1, 2026. Measures age, not quality.
Popularity 46 out of 100. 1,738 downloads in 30 days; 205 lifetime likes. Checked Nov 7, 2026. Measures adoption, not quality.
What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.

Check the date source
Popularity · adoption

Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

Check the statistics source
Scores as of Nov 8, 2026. Statistics checked Nov 7, 2026. Statistics are marked for refresh after 72 hours.Full scoring method

At a glance

Parameters
3.37B
Architecture
deepseek_vl_v2
Context length
32,768
License
cc-by-nc-4.0
Disk space
6.3 GB
Software
Transformers
View the model source
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Best for

  • Document parsing
  • Optical character recognition (OCR)
  • Multilingual document intelligence
  • Vision-language document understanding
  • End-to-end document text extraction

Tradeoffs

  • License is cc-by-nc-4.0, which restricts commercial use
  • The model card states inference: false
  • Requires trust_remote_code=True to load custom code
  • vLLM requires a one-time architecture registration from deepseek_ocr_mtp.py
  • No separate training or serving package is provided

Ways to run it

Transformers · See official guide

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

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']))
Official instructions

Explore its uses

SAFETENSORSMULTILINGUALIMAGE-TEXT-TO-TEXT

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jina ocr v1: hardware, VRAM & setup | YouRunAI