NVIDIA Nemotron Parse v1.2 is designed to understand document semantics and extract text and tables elements with spatial grounding. Given an image, NVIDIA Nemotron Parse v1.2 produces structured annotations, including formatted text, bounding-boxes and the corresponding semantic classes, ordered according to the document's reading flow. It overcomes the shortcomings of traditional OCR technologies that struggle with complex document layouts with structural variability, and helps transform unstructured documents into actionable and machine-usable representations. This has several downstream benefits such as increasing the availability of training-data for Large Language Models (LLMs), improving the accuracy of extractor, curator, retriever and AI agentic applications, and enhancing document understanding pipelines.

This model is ready for commercial use.

Release Notes

Compared to NVIDIA-Nemotron-Parse v1.1 the following changes are made in v1.2:

Updated prompt interface

NVIDIA-Nemotron-Parse v1.2 extends the prompt interface with a fourth prompt token category — Text-in-picture prompts — controlling whether the model extracts text from embedded images (e.g. photographs, figures):

<predict_text_in_pic>: extract text found inside pictures.
<predict_no_text_in_pic>: skip text extraction from pictures, reducing generation time by avoiding unnecessary tokens.

The full v1.2 prompt now consists of four prompt tokens (one from each category):

</s><s><predict_bbox><predict_classes><output_markdown><predict_no_text_in_pic>

Important: NVIDIA-Nemotron-Parse v1.1 used a three-token prompt (e.g. </s><s><predict_bbox><predict_classes><output_markdown>). In v1.2, the fourth token is required. The prompt is not validated at runtime — the model will still generate output with the old three-token prompt — but because all v1.2 training data included four prompt tokens, generation quality will be significantly degraded without it.

Natural reading order for all classes

Output elements now follow the document's natural reading order across all semantic classes, including Footnotes, Page-Footers, Tables, Pictures, and Captions.

Image size

Note that input image size in v1.2 changed from 2048x1648 to 2048x1664 compared to v1.1

Quick Start

Install dependencies in your environment

You can use a public image nvcr.io/nvidia/pytorch:25.03-py3 with the following library versions installed on top:

bash

pip install accelerate==1.12.0
pip install albumentations==2.0.8
pip install transformers==5.6.1
pip install timm==1.0.22

Usage example

python

import torch
from PIL import Image, ImageDraw
from transformers import AutoModel, AutoProcessor, AutoTokenizer, AutoConfig, AutoImageProcessor, GenerationConfig
from postprocessing import extract_classes_bboxes, transform_bbox_to_original, postprocess_text

# Load model and processor
model_path = "nvidia/NVIDIA-Nemotron-Parse-v1.2"  # Or use a local path
device = "cuda:0"

model = AutoModel.from_pretrained(
    model_path,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16
).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained(model_path)
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)

# Load image
image = Image.open("path/to/your/image.jpg")
task_prompt = "</s><s><predict_bbox><predict_classes><output_markdown><predict_no_text_in_pic>"
# task_prompt = "</s><s><predict_bbox><predict_classes><output_markdown><predict_text_in_pic>"

# Process image
inputs = processor(images=[image], text=task_prompt, return_tensors="pt", add_special_tokens=False).to(device)

generation_config = GenerationConfig.from_pretrained(model_path, trust_remote_code=True)
# Generate text
outputs = model.generate(**inputs,  generation_config=generation_config)

# Decode the generated text
generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0]

Postprocessing

python

from PIL import Image, ImageDraw
from postprocessing import extract_classes_bboxes, transform_bbox_to_original, postprocess_text

classes, bboxes, texts = extract_classes_bboxes(generated_text)
bboxes = [transform_bbox_to_original(bbox, image.width, image.height) for bbox in bboxes]

# Specify output formats for postprocessing
table_format = 'latex' # latex | HTML | markdown | json | csv
text_format = 'markdown' # markdown | plain
blank_text_in_figures = False # remove text inside 'Picture' class
texts = [postprocess_text(text, cls = cls, table_format=table_format, text_format=text_format, blank_text_in_figures=blank_text_in_figures) for text, cls in zip(texts, classes)]

for cl, bb, txt in zip(classes, bboxes, texts):
    print(cl, ': ', txt)

draw = ImageDraw.Draw(image)
for bbox in bboxes:
  draw.rectangle((bbox[0], bbox[1], bbox[2], bbox[3]), outline="red")

Table output formats

Supported values for table_format: 'latex' | 'HTML' | 'markdown' | 'json' | 'csv'

Inference with VLLM

Nemotron-Parse-v1.2 is available in vllm main and can be found in vllm/vllm-openai:v0.14.1 docker image.

Note: when running on A100/A10 we recommend running vllm serve with --attention-backend=TRITON_ATTN

You will need to install the following dependencies on top, and then follow the VLLM Inference example below:

pip install albumentations timm open_clip_torch

VLLM Inference example

Option 1: end-to-end python inference

python

from vllm import LLM, SamplingParams
from PIL import Image

def main():
    sampling_params = SamplingParams(
        temperature=0,
        top_k=1,
        repetition_penalty=1.1,
        max_tokens=9000,
        skip_special_tokens=False,
    )
    
    llm = LLM(
        model="nvidia/NVIDIA-Nemotron-Parse-v1.2",
        max_num_seqs=64,
        limit_mm_per_prompt={"image": 1},
        dtype="bfloat16",
        trust_remote_code=True,
    )
    
    image = Image.open("<YOUR-IMAGE-PATH>")
    
    prompts = [
        {  # Implicit prompt
            "prompt": "</s><s><predict_bbox><predict_classes><output_markdown><predict_no_text_in_pic>",
            "multi_modal_data": {
                "image": image
            },
        },
        {  # Explicit encoder/decoder prompt
            "encoder_prompt": {
                "prompt": "",
                "multi_modal_data": {
                    "image": image
                },
            },
            "decoder_prompt": "</s><s><predict_bbox><predict_classes><output_markdown><predict_no_text_in_pic>",
        },
    ]
    
    outputs = llm.generate(prompts, sampling_params)
    
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Decoder prompt: {prompt!r}, Generated text: {generated_text!r}")

if __name__ == "__main__":
    main()

Option 2: vllm serve

Alternatively, you can start a vllm server as:

bash

vllm serve nvidia/NVIDIA-Nemotron-Parse-v1.2 \
    --dtype bfloat16 \
    --max-num-seqs 8 \
    --limit-mm-per-prompt '{"image": 1}' \
    --trust-remote-code \
    --port 8000 \
    --chat-template chat_template.jinja

with chat_template.jinja provided in this repository. Then, you can run inference as:

python

import base64
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
)

# Read and base64-encode the image
with open(<your-image-path>, "rb") as f:
    img_b64 = base64.b64encode(f.read()).decode("utf-8")
prompt_text = "</s><s><predict_bbox><predict_classes><output_markdown><predict_no_text_in_pic>"

resp = client.chat.completions.create(
    model="nvidia/NVIDIA-Nemotron-Parse-v1.2",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": prompt_text,
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/png;base64,{img_b64}",
                    },
                },
            ],
        }
    ],
    max_tokens=9000,
    temperature=0.0,
    extra_body={
        "repetition_penalty": 1.1,
        "top_k": 1,
        "skip_special_tokens": False,
    },
)
print(resp.choices[0].message.content)

Note: we recommend using the default prompt that extracts bounding boxes, classes, and text in markdown formatting for all use cases (</s><s><predict_bbox><predict_classes><output_markdown><predict_no_text_in_pic> or </s><s><predict_bbox><predict_classes><output_markdown><predict_text_in_pic>). If necessary, optionally the prompt that omits text extraction and only outputs bounding boxes and classes could be used: </s><s><predict_bbox><predict_classes><output_no_text><predict_no_text_in_pic>.

Logits processors

With Nemotron-Parse-v1.2 we share 2 logits processors available in logitsprocessors/ dir for vllm and in hf_logits_processor.py for the python model. NemotronParseRepetitionStopProcessor - when used during generation, detects repeating n-grams and forces the model to close the <x_><y_> block when detecting potential hallucination. NemotronParseTableInsertionLogitsProcessor - forces every block to follow a table structure (useful if, e.g., you are running the model on table image crops)

Please refer to the example_with_processor.py for example usage with python model. With vllm, you can provide these as arguments to vllm serve, after exporting logitsprocs/ to PYTHONPATH, e.g.:

vllm serve nvidia/NVIDIA-Nemotron-Parse-v1.2 \
  --dtype bfloat16 \
  --max-num-seqs 4 \
  --limit-mm-per-prompt '{"image": 1}' \
  --attention-backend=TRITON_ATTN \
  --trust-remote-code \
  --logits-processors nemotron_parse_vllm_logitprocs:NemotronParseTableInsertionLogitsProcessor \
  --port 8000

An example of inference with vllm openai server is available in vllm_example.py

License/Terms of Use

Governing Terms: Your use of this model is governed by the: NVIDIA Nemotron Open Model License. Use of the tokenizer included in this model is governed by the CC-BY-4.0 license.

Deployment Geography:

Global

Use Case:

NVIDIA Nemotron Parse v1.2 will be capable of comprehensive text understanding and document structure understanding. It will be used in retriever and curator solutions. Its text extraction datasets and capabilities will help with LLM and VLM training, as well as improve run-time inference accuracy of VLMs. The NVIDIA Nemotron Parse v1.2 model will perform text extraction from PDF and PPT documents. The NVIDIA Nemotron Parse v1.2 can classify the objects (title, section, caption, index, footnote, lists, tables, bibliography, image) in a given document, and provide bounding boxes with coordinates.

Release Date:

Hugging Face [02/17/2026] via [URL]

References(s):

https://huggingface.co/docs/transformers/en/model_doc/mbart

Model Architecture:

Architecture Type: Transformer-based vision-encoder-decoder model

Network Architecture:

Vision Encoder: ViT-H model (https://huggingface.co/nvidia/C-RADIO)
Adapter Layer: 1D convolutions & norms to compress dimensionality and sequence length of the latent space (1280 tokens to 320 tokens)
Decoder: mBart [1] 10 blocks
Tokenizer: Use of the tokenizer included in this model is governed by the CC-BY-4.0 license
Number of Parameters: < 1B

Input:

Input Type: Image, Text
Input Type(s): Red, Green, Blue (RGB) + Prompt (String)
Input Parameters: Two-Dimensional (2D), One-Dimensional (1D)
Other Properties Related to Input:
- Max Input Resolution (Width, Height): 1664, 2048
- Min Input Resolution (Width, Height): 1024, 1280
Channel Count: 3

Output:

Output Type: Text
Output Format: String
Output Parameters: One-Dimensional (1D)
Other Properties Related to Output: Nemotron-parse output format is a string which encodes text content (formatted or not) as well as bounding boxes and class attributes.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration:

Runtime Engine(s):

TensorRT-LLM
vLLM

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Blackwell
NVIDIA Hopper
NVIDIA Turing

Supported Operating System(s):

[Linux]

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s):

Nemotron Parse 1.2

Training, Testing, and Evaluation Datasets:

Training Dataset

** Image Training Data Size

[1 Million to 1 Billion Images]

** Text Training Data Size

[1 Billion to 10 Trillion Tokens]

** Data Collection Method by dataset

Hybrid: Automated, Human, Synthetic

** Labeling Method by dataset

Hybrid: Automated, Human, Synthetic

Properties (Quantity, Dataset Descriptions, Sensor(s)): The training set contains millions of image–text items, aggregated across many large document and table datasets totaling several terabytes of data. The data consists of document-page and table images paired with OCR text, bounding boxes, and layout labels, drawn from real-world sources (scientific papers, PDFs, Wikipedia pages) as well as fully synthetic tables and word/character renderings. Modalities are primarily images plus associated text and structural annotations; content spans public-domain resources, and synthetic data. Images are obtained by rendering digital documents or generating synthetic layouts, and annotations come from OCR/layout models, third-party OCR services, and human labeling.

Inference:

Acceleration Engine: Tensor(RT)-LLM, vLLM
Test Hardware:

H100
A100

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.

For more detailed information on ethical considerations for this model, please see the Model Card++ Bias, Explainability, Safety & Security, and Privacy Subcards Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

Source Model Repo

This model is derived from nvidia/NVIDIA-Nemotron-Parse-v1.2.
See the upstream repository for the original safetensors weights, training data, and the full upstream model card.

SAS Modifications

SAS has not made any modifications to the source model weights or file artifacts.

SAS Product Integration

This repo is consumed by the SAS Document Analysis product offering.

Model Security Scan

Model weights were scanned on 2026-04-21. All checks passed. Safetensor file received a LOW risk level assessment.

sassoftware/NVIDIA-Nemotron-Parse-v1.2

Model Overview

Description:

Release Notes

Updated prompt interface

Natural reading order for all classes

Image size

Quick Start

Install dependencies in your environment

Usage example

Postprocessing

Table output formats

Inference with VLLM

VLLM Inference example

Option 1: end-to-end python inference

Option 2: vllm serve

Logits processors

License/Terms of Use

Deployment Geography:

Use Case:

Release Date:

References(s):

Model Architecture:

Input:

Output:

Software Integration:

Model Version(s):

Training, Testing, and Evaluation Datasets:

Training Dataset

Inference:

Ethical Considerations:

Source Model Repo

SAS Modifications

SAS Product Integration

Model Security Scan

sassoftware/
NVIDIA-Nemotron-Parse-v1.2