# DeepSeek-OCR 2: How to Run & Fine-tune Guide
**DeepSeek-OCR 2** is the new 3B-parameter model for SOTA vision and document understanding released on Jan 27, 2026 by DeepSeek. The model focuses on image-to-text with stronger visual reasoning, not just text extraction.
DeepSeek-OCR 2 introduces DeepEncoder V2, which enables the model to 'see' an image in the same logical order as a human.
Unlike traditional vision LLMs that scan images in a fixed grid (top-left β bottom-right), DeepEncoder V2 builds a global understanding first, then learns a human-like reading order, what to attend to first, next, and so on. This boosts OCR on complex layouts by better following columns, linking labels to values, reading tables coherently, and handling mixed text + structure.
You can now fine-tune DeepSeek-OCR 2 in Unsloth via our [**free fine-tuning notebook**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\).ipynb)**.** We demonstrated a [88.6% improvement](#fine-tuning-deepseek-ocr) for language understanding.
Running DeepSeek-OCR 2Fine-tuning DeepSeek-OCR 2
## π₯οΈ **Running DeepSeek-OCR 2**
In order to run the model, like the first model, DeepSeek-OCR 2 was edited to enable inference & training on the latest transformers (no accuracy change). You can find [it here](https://huggingface.co/unsloth/DeepSeek-OCR-2).
To run the model in [transformers](#transformers-run-deepseek-ocr-2-tutorial) or [Unsloth](#unsloth-run-deepseek-ocr-tutorial), here are the recommended settings:
### :gear: Recommended Settings
DeepSeek recommends these settings:
* **Temperature = 0.0**
* `max_tokens = 8192`
* `ngram_size = 30`
* `window_size = 90`
**Support Modes - Dynamic resolution:**
* Default: (0-6)Γ768Γ768 + 1Γ1024Γ1024 β (0-6)Γ144 + 256 visual tokens
**Prompts examples:**
```
# document: \n<|grounding|>Convert the document to markdown.
# other image: \n<|grounding|>OCR this image.
# without layouts: \nFree OCR.
# figures in document: \nParse the figure.
# general: \nDescribe this image in detail.
# rec: \nLocate <|ref|>xxxx<|/ref|> in the image.
```
Turns any document into markdown using Visual Causal Flow.
### π¦₯ Unsloth: Run DeepSeek-OCR 2 Tutorial
1. Obtain the latest `unsloth` via `pip install --upgrade unsloth` . If you already have Unsloth, update it via `pip install --upgrade --force-reinstall --no-deps --no-cache-dir unsloth unsloth_zoo`
2. Then use the code below to run DeepSeek-OCR 2:
{% code overflow="wrap" %}
```python
from unsloth import FastVisionModel
import torch
from transformers import AutoModel
import os
os.environ["UNSLOTH_WARN_UNINITIALIZED"] = '0'
from huggingface_hub import snapshot_download
snapshot_download("unsloth/DeepSeek-OCR-2", local_dir = "deepseek_ocr")
model, tokenizer = FastVisionModel.from_pretrained(
"./deepseek_ocr",
load_in_4bit = False, # Use 4bit to reduce memory use. False for 16bit LoRA.
auto_model = AutoModel,
trust_remote_code = True,
unsloth_force_compile = True,
use_gradient_checkpointing = "unsloth", # True or "unsloth" for long context
)
prompt = "\nFree OCR. "
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 = 640, crop_mode=True, save_results = True, test_compress = False)
```
{% endcode %}
### π€ Transformers: Run DeepSeek-OCR 2 Tutorial
Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.12.9 + CUDA11.8:
```bash
torch==2.6.0
transformers==4.46.3
tokenizers==0.20.3
einops
addict
easydict
pip install flash-attn==2.7.3 --no-build-isolation
```
```python
from transformers import AutoModel, AutoTokenizer
import torch
import os
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
model_name = 'unsloth/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 = "\nFree OCR. "
prompt = "\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)
```
## π¦₯ **Fine-tuning DeepSeek-OCR 2**
Unsloth now supports fine-tuning of DeepSeek-OCR 2. Like the first model, you'll need to use our [custom upload](https://huggingface.co/unsloth/DeepSeek-OCR-2) for it to work on `transformers` (no accuracy change). Like the first model, Unsloth trains DeepSeek-OCR-2 1.4x faster with 40% less VRAM and 5x longer context lengths with no accuracy degradation.\
\
You can now fine-tune DeepSeek-OCR 2 via our free Colab notebook.
* DeepSeek-OCR 2: [Fine-tuning only notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_2_\(3B\).ipynb)
See below for CER (Character Error Rate) accuracy improvements on the Persian language:
#### Per-sample CER (10 samples)
| idx | OCR1 before | OCR1 after | OCR2 before | OCR2 after |
| ---- | ----------: | ---------: | ----------: | ---------: |
| 1520 | 1.0000 | 0.8000 | 10.4000 | 1.0000 |
| 1521 | 0.0000 | 0.0000 | 2.6809 | 0.0213 |
| 1522 | 2.0833 | 0.5833 | 4.4167 | 1.0000 |
| 1523 | 0.2258 | 0.0645 | 0.8710 | 0.0968 |
| 1524 | 0.0882 | 0.1176 | 2.7647 | 0.0882 |
| 1525 | 0.1111 | 0.1111 | 0.9444 | 0.2222 |
| 1526 | 2.8571 | 0.8571 | 4.2857 | 0.7143 |
| 1527 | 3.5000 | 1.5000 | 13.2500 | 1.0000 |
| 1528 | 2.7500 | 1.5000 | 1.0000 | 1.0000 |
| 1529 | 2.2500 | 0.8750 | 1.2500 | 0.8750 |
#### Average CER (10 samples)
* **OCR1:** before **1.4866**, after **0.6409** (**-57%**)
* **OCR2:** before **4.1863**, after **0.6018** (**-86%**)
## π Benchmarks
Benchmarks for DeepSeek-OCR 2 model are derived from the official research paper.
**Table 1:** Comprehensive evaluation of document reading on OmniDocBench v1.5. V-tokenπππ₯\
represents the maximum number of visual tokens used per page in this benchmark. R-order\
denotes reading order. Except for DeepSeek OCR and DeepSeek OCR 2, all other model results\
in this table are sourced from the OmniDocBench repository.
**Table 2:** Edit Distances for different categories of document-elements in OmniDocBench v1.5.\
V-tokenπππ₯ denotes the lowest maximum number of visual tokens.
