Model Overview

Description:

NVIDIA Nemotron Parse 2.0 transforms document images into structured, machine-readable representations with text, layout classes, bounding boxes, and reading-order information. Given a Red, Green, Blue (RGB) document image and a task prompt, the model produces formatted text and spatial annotations for document elements such as titles, paragraphs, captions, tables, charts, page headers, page footers, footnotes, pictures, and bibliography entries. Compared with NVIDIA Nemotron Parse v1.2, NVIDIA Nemotron Parse 2.0 adds an approximately 20k-token vocabulary expansion for more efficient multilingual support, chart-aware document parsing with the <class_Chart> class token, and updated training coverage for chart/table-heavy documents. NVIDIA Nemotron Parse 2.0 is intended for document understanding, information retrieval, data extraction, and multimodal data-curation workflows.

This model is ready for commercial or non-commercial use.

License/Terms of Use:

Governing Terms: Your use of this model is governed by the NVIDIA Open Model License Agreement. 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 2.0 is designed for developers and teams building document intelligence, retrieval-augmented generation (RAG), curator, extractor, and agentic AI applications. It can be used to convert scanned or rendered PDFs, presentation slides, forms, reports, tables, and mixed-content document pages into structured outputs for downstream indexing, retrieval, analytics, model training-data creation, and human-in-the-loop review.

Capability Highlights:

Expanded multilingual OCR support, with substantial gains on CJK and Indic-script document text.
Improved handwritten-text extraction for document pages containing informal, handwritten, or note-like content.
Chart-to-table parsing that can identify chart regions and convert visible chart information into structured text for downstream use.
Improved table handling, including stronger table detection, structure recovery, and text extraction on table-heavy documents.

Release Date:

Hugging Face August 3, 2026 via URL

References(s):

Model Architecture:

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

Network Architecture:

Vision Encoder: ViT-H model based on NVIDIA C-RADIO
Adapter Layer: 1D convolutions and normalization layers that compress the vision latent sequence before decoding
Decoder: mBART decoder with 10 blocks
Auxiliary Prediction Head: One training-time decoder prediction head is preserved separately in auxiliary_prediction_heads.safetensors.extra for future multi-token prediction research. Standard generation uses the tied decoder input/output embeddings; the default model.safetensors, Transformers examples, and vLLM examples do not load this auxiliary head.
Tokenizer: The tokenizer contains 72,256 entries, including an approximately 20k-token expansion over NVIDIA Nemotron Parse v1.2 to improve multilingual token efficiency. The model also includes task/control tokens such as <predict_bbox>, <predict_classes>, <predict_text_in_pic>, and <predict_no_text_in_pic>, plus the chart class token <class_Chart>. Use of the tokenizer included in this model is governed by the CC-BY-4.0 license.
Number of Parameters: < 1B

Input(s):

Input Type(s): Image, Text

Input Format(s):

Image: Red, Green, Blue (RGB)
Text: Prompt string

Input Parameters:

Image: Two-Dimensional (2D)
Text: One-Dimensional (1D)

Other Properties Related to Input:

Recommended maximum input resolution (Width, Height): 1664, 2048
Recommended minimum input resolution (Width, Height): 1024, 1280
Channel Count: 3
Prompt format: one task prompt composed from the supported control tokens. The default prompt extracts bounding boxes, semantic classes, and text in markdown format: </s><s><predict_bbox><predict_classes><output_markdown><predict_no_text_in_pic>. The model can emit chart regions using <class_Chart> when chart content is detected.

Output(s):

Output Type(s): Text

Output Format(s): String

Output Parameters: One-Dimensional (1D)

Other Properties Related to Output: Nemotron Parse 2.0 returns a string encoding document text, semantic element classes, and bounding boxes. Postprocessing utilities in this repository can transform generated bounding boxes back to original image coordinates and convert table or chart-associated text into LaTeX, HTML, markdown, JSON, or CSV where supported.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA hardware and software frameworks, the model achieves faster training and inference times compared to CPU-only solutions.

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:

pip install accelerate==1.12.0\npip install albumentations==2.0.8\npip install transformers==5.6.1\npip install timm==1.0.22\npip install open_clip_torch==3.2.0\npip install einops==0.8.1\n

Usage example

~~"\n# task_prompt = "~~"\n\n# Process image\ninputs = processor(images=[image], text=task_prompt, return_tensors="pt", add_special_tokens=False).to(device)\n\ngeneration_config = GenerationConfig.from_pretrained(model_path, trust_remote_code=True)\n\n# Generate text\noutputs = model.generate(**inputs, generation_config=generation_config)\n\n# Decode the generated text\ngenerated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0]\n\">
import torch\nfrom PIL import Image, ImageDraw\nfrom transformers import AutoModel, AutoProcessor, AutoTokenizer, GenerationConfig\nfrom postprocessing import extract_classes_bboxes, transform_bbox_to_original, postprocess_text\n\n# Load model and processor\nmodel_path = \"nvidia/NVIDIA-Nemotron-Parse-2.0\" # Or use a local path\ndevice = \"cuda:0\"\n\nmodel = AutoModel.from_pretrained(\n model_path,\n trust_remote_code=True,\n torch_dtype=torch.bfloat16\n).to(device).eval()\ntokenizer = AutoTokenizer.from_pretrained(model_path)\nprocessor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)\n\n# Load image\nimage = Image.open(\"path/to/your/image.jpg\")\ntask_prompt = \"</s><s><predict_bbox><predict_classes><output_markdown><predict_no_text_in_pic>\"\n# task_prompt = \"</s><s><predict_bbox><predict_classes><output_markdown><predict_text_in_pic>\"\n\n# Process image\ninputs = processor(images=[image], text=task_prompt, return_tensors=\"pt\", add_special_tokens=False).to(device)\n\ngeneration_config = GenerationConfig.from_pretrained(model_path, trust_remote_code=True)\n\n# Generate text\noutputs = model.generate(**inputs, generation_config=generation_config)\n\n# Decode the generated text\ngenerated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0]\n

\n
Postprocessing
\n
from PIL import ImageDraw\nfrom postprocessing import extract_classes_bboxes, transform_bbox_to_original, postprocess_text\n\nclasses, bboxes, texts = extract_classes_bboxes(generated_text)\nbboxes = [transform_bbox_to_original(bbox, image.width, image.height) for bbox in bboxes]\n\n# Specify output formats for postprocessing\ntable_format = \"latex\" # latex | HTML | markdown | json | json_hierarchical | csv\ntext_format = \"markdown\" # markdown | plain\nblank_text_in_figures = False # set True to remove text inside 'Picture' class\ntexts = [\n postprocess_text(\n text,\n cls=cls,\n table_format=table_format,\n text_format=text_format,\n blank_text_in_figures=blank_text_in_figures,\n )\n for text, cls in zip(texts, classes)\n]\n\nfor cl, bb, txt in zip(classes, bboxes, texts):\n print(cl, \": \", txt)\n\ndraw = ImageDraw.Draw(image)\nfor bbox in bboxes:\n draw.rectangle((bbox[0], bbox[1], bbox[2], bbox[3]), outline=\"red\")\n
\n
Table output formats
\n
Supported values for table_format: latex | HTML | markdown | json | json_hierarchical | csv
\n
Inference with vLLM
\n
Nemotron Parse 2.0 can be served with vLLM when using a vLLM build that includes Nemotron Parse remote-code support. On A100 and A10 systems, we recommend running vllm serve with --attention-backend=TRITON_ATTN.
\n
Install the following dependencies on top of the serving image:
\n
pip install albumentations timm open_clip_torch einops\n
\n
This export keeps lm_head.weight tied to decoder.embed_tokens.weight and does not materialize a duplicate output-head tensor. Current vLLM 0.20 Nemotron Parse builds create a separate output head unless patched. If your vLLM build does not already support tied Nemotron Parse output embeddings, fetch the included runtime patch and add it to PYTHONPATH before starting vLLM:
\n
PATCH_ROOT=$(python - <<'PY'\nfrom huggingface_hub import snapshot_download\nprint(snapshot_download(\n \"nvidia/NVIDIA-Nemotron-Parse-2.0\",\n allow_patterns=\"vllm_tied_patch/sitecustomize.py\",\n))\nPY\n)\nexport PYTHONPATH=\"${PATCH_ROOT}/vllm_tied_patch:${PYTHONPATH}\"\n
\n
vLLM Inference example
\n
Option 1: end-to-end Python inference
\n

~~")\n\n prompts = [\n {\n "prompt": "~~",\n "multi_modal_data": {\n "image": image,\n },\n },\n {\n "encoder_prompt": {\n "prompt": "",\n "multi_modal_data": {\n "image": image,\n },\n },\n "decoder_prompt": "",\n },\n ]\n\n outputs = llm.generate(prompts, sampling_params)\n\n for output in outputs:\n prompt = output.prompt\n generated_text = output.outputs[0].text\n print(f"Decoder prompt: {prompt!r}, Generated text: {generated_text!r}")\n\n\nif __name__ == "__main__":\n main()\n\">
from vllm import LLM, SamplingParams\nfrom PIL import Image\n\n\ndef main():\n sampling_params = SamplingParams(\n temperature=0,\n top_k=1,\n repetition_penalty=1.1,\n max_tokens=9000,\n skip_special_tokens=False,\n )\n\n llm = LLM(\n model=\"nvidia/NVIDIA-Nemotron-Parse-2.0\",\n max_num_seqs=64,\n limit_mm_per_prompt={\"image\": 1},\n dtype=\"bfloat16\",\n trust_remote_code=True,\n )\n\n image = Image.open(\"<YOUR-IMAGE-PATH>\")\n\n prompts = [\n {\n \"prompt\": \"</s><s><predict_bbox><predict_classes><output_markdown><predict_no_text_in_pic>\",\n \"multi_modal_data\": {\n \"image\": image,\n },\n },\n {\n \"encoder_prompt\": {\n \"prompt\": \"\",\n \"multi_modal_data\": {\n \"image\": image,\n },\n },\n \"decoder_prompt\": \"</s><s><predict_bbox><predict_classes><output_markdown><predict_no_text_in_pic>\",\n },\n ]\n\n outputs = llm.generate(prompts, sampling_params)\n\n for output in outputs:\n prompt = output.prompt\n generated_text = output.outputs[0].text\n print(f\"Decoder prompt: {prompt!r}, Generated text: {generated_text!r}\")\n\n\nif __name__ == \"__main__\":\n main()\n

\n
Option 2: vLLM serve
\n
vllm serve nvidia/NVIDIA-Nemotron-Parse-2.0 \\\n --dtype bfloat16 \\\n --max-num-seqs 8 \\\n --limit-mm-per-prompt '{\"image\": 1}' \\\n --trust-remote-code \\\n --port 8000 \\\n --chat-template chat_template.jinja\n
\n
Then run inference through the OpenAI-compatible API:
\n

~~", "rb") as f:\n img_b64 = base64.b64encode(f.read()).decode("utf-8")\n\nprompt_text = "~~"\n\nresp = client.chat.completions.create(\n model="nvidia/NVIDIA-Nemotron-Parse-2.0",\n messages=[\n {\n "role": "user",\n "content": [\n {\n "type": "text",\n "text": prompt_text,\n },\n {\n "type": "image_url",\n "image_url": {\n "url": f"data:image/png;base64,{img_b64}",\n },\n },\n ],\n }\n ],\n max_tokens=9000,\n temperature=0.0,\n extra_body={\n "repetition_penalty": 1.1,\n "top_k": 1,\n "skip_special_tokens": False,\n },\n)\nprint(resp.choices[0].message.content)\n\">
import base64\nfrom openai import OpenAI\n\nclient = OpenAI(\n base_url=\"http://localhost:8000/v1\",\n)\n\nwith open(\"<YOUR-IMAGE-PATH>\", \"rb\") as f:\n img_b64 = base64.b64encode(f.read()).decode(\"utf-8\")\n\nprompt_text = \"</s><s><predict_bbox><predict_classes><output_markdown><predict_no_text_in_pic>\"\n\nresp = client.chat.completions.create(\n model=\"nvidia/NVIDIA-Nemotron-Parse-2.0\",\n messages=[\n {\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": prompt_text,\n },\n {\n \"type\": \"image_url\",\n \"image_url\": {\n \"url\": f\"data:image/png;base64,{img_b64}\",\n },\n },\n ],\n }\n ],\n max_tokens=9000,\n temperature=0.0,\n extra_body={\n \"repetition_penalty\": 1.1,\n \"top_k\": 1,\n \"skip_special_tokens\": False,\n },\n)\nprint(resp.choices[0].message.content)\n

Benchmark	Metric	NVIDIA Nemotron Parse v1.2	NVIDIA Nemotron Parse 2.0	Change
ParseBench	Overall score	0.5782	0.6391	↑ +0.0609
OmniDocBench Notes (Handwriting)	Text edit distance (lower is better)	0.9739	0.3395	↓ -0.6343
IndicVisionBench	Overall ANLS character	0.0612	0.7203	↑ +0.6592
MOSCAR (Multilingual)	Overall BoC F1	0.4410	0.9102	↑ +0.4692

\n
Prompt and logits-processor options
\n
The recommended default prompt extracts bounding boxes, semantic classes, and text in markdown format:
\n
</s><s><predict_bbox><predict_classes><output_markdown><predict_no_text_in_pic>
\n
To extract text that appears inside embedded images or figures, use:
\n
</s><s><predict_bbox><predict_classes><output_markdown><predict_text_in_pic>
\n
If only bounding boxes and classes are needed, use:
\n
</s><s><predict_bbox><predict_classes><output_no_text><predict_no_text_in_pic>
\n
This repository includes two optional logits processors:
\n
\n
NemotronParseRepetitionStopProcessor: detects repeating n-grams during generation and forces the model to close the coordinate block when repeated structured output suggests a potential hallucination.
\n
NemotronParseTableInsertionLogitsProcessor: forces every block to follow a table structure, which can be useful when running the model on table image crops.
\n
\n
Please refer to example_with_processor.py for Python-model usage. With vLLM, export logitsprocs/ to PYTHONPATH and pass the desired processor to vllm serve, for example:
\n
vllm serve nvidia/NVIDIA-Nemotron-Parse-2.0 \\\n --dtype bfloat16 \\\n --max-num-seqs 4 \\\n --limit-mm-per-prompt '{\"image\": 1}' \\\n --attention-backend=TRITON_ATTN \\\n --trust-remote-code \\\n --logits-processors nemotron_parse_vllm_logitprocs:NemotronParseTableInsertionLogitsProcessor \\\n --port 8000\n
\n
An example of inference with the vLLM OpenAI-compatible server is available in vllm_example.py.
\n
Software Integration:
\n
Runtime Engine(s):
\n
\n
Transformers
\n
vLLM
\n
\n
Supported Hardware Microarchitecture Compatibility:
\n
\n
NVIDIA Ampere
\n
NVIDIA Blackwell
\n
NVIDIA Hopper
\n
NVIDIA Turing
\n
\n
Supported Operating System(s):
\n
\n
Linux
\n
\n
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 is essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
\n
Model Version(s):
\n
Nemotron Parse 2.0
\n
Training, Testing, and Evaluation Datasets:
\n
Training Dataset
\n
Data Modality:
\n
\n
Image
\n
Text
\n
\n
Image Training Data Size:
\n
\n
1 Million to 1 Billion Images
\n
\n
Text Training Data Size:
\n
\n
1 Billion to 10 Trillion Tokens
\n
\n
Data Collection Method by dataset:
\n
\n
Hybrid: Automated, Human, Synthetic
\n
\n
Labeling Method by dataset:
\n
\n
Hybrid: Automated, Human, Synthetic
\n
\n
Properties: The training set contains millions of image-text items aggregated across large document, table, and layout datasets. The data consists of document-page and table images paired with OCR text, bounding boxes, and layout labels. Sources include rendered digital documents, scientific papers, PDFs, Wikipedia-style pages, and synthetic document, table, word, and character renderings. Annotations come from OCR and layout models, third-party OCR services, synthetic-generation pipelines, and human labeling.
\n
Testing and Evaluation Dataset
\n
Testing and evaluation use internal and public document-understanding benchmarks that cover OCR quality, layout structure, table parsing, reading order, and visual grounding. Dataset collection and labeling methods are hybrid and include automated, model-derived, manually labeled, and synthetic annotations.
\n
Evaluation Results
\n
Benchmark coverage:
\n
\n
ParseBench evaluates document parsing across text fidelity, semantic formatting, tables, charts, and visual grounding with layout boxes/classes.
\n
IndicVisionBench evaluates OCR quality for Indic-language pages, including Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Tamil, and Telugu.
\n
MOSCAR evaluates multilingual synthetic OCR across broad script coverage, including Latin, Arabic, Cyrillic, Chinese, Hangul, Japanese, Indic scripts, Hebrew, Thai, Greek, and others.
\n
OmniDocBench Notes (Handwriting) evaluates text-block edit distance on note-style document pages from the data_source: note slice.
\n
\n
The following results compare NVIDIA Nemotron Parse 2.0 with NVIDIA Nemotron Parse v1.2 on internal and public evaluation benchmarks. NVIDIA Nemotron Parse 2.0 results use the equal-weight checkpoint soup from training checkpoints 58k, 60k, 62k, 64k, and 66k. Unless marked otherwise, metrics are higher-is-better; arrows indicate the direction of the 2.0 change relative to v1.2.
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
tr:nth-child(odd)]:bg-surface-sunken\">
Benchmark Metric NVIDIA Nemotron Parse v1.2 NVIDIA Nemotron Parse 2.0 Change
ParseBench Overall score 0.5782 0.6391 ↑ +0.0609
OmniDocBench Notes (Handwriting) Text edit distance (lower is better) 0.9739 0.3395 ↓ -0.6343
IndicVisionBench Overall ANLS character 0.0612 0.7203 ↑ +0.6592
MOSCAR (Multilingual) Overall BoC F1 0.4410 0.9102 ↑ +0.4692
\n
Inference:
\n
Acceleration Engine: Transformers, vLLM
\nTest Hardware:
\n
\n
H100
\n
A100
\n
\n
Get Help
\n
NVIDIA Developer Community Forum
\n
Get access to community knowledge base articles and support cases (https://forums.developer.nvidia.com/)
\n
Enterprise Support
\n
Get access to knowledge base articles and support cases or submit a ticket.
\n
Ethical Considerations:
\n
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. 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.
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Please make sure you have proper rights and permissions for all input image and video content. If an input image contains people, personal health information, confidential business data, or intellectual property, the model may extract or reproduce visible text from that content.
\n
For more detailed information on ethical considerations for this model, please see the Model Card++ subcards: Bias, Explainability, Safety & Security, and Privacy.
\nPlease report model quality, risk, security vulnerabilities, or NVIDIA AI Concerns here.
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\n\nThis model is ready for commercial or non-commercial use.
\n\n### License/Terms of Use:\nGoverning Terms: Your use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Use of the tokenizer included in this model is governed by the [CC-BY-4.0 license](https://creativecommons.org/licenses/by/4.0/).
\n\n### Deployment Geography:\nGlobal
\n\n### Use Case:
\nNVIDIA Nemotron Parse 2.0 is designed for developers and teams building document intelligence, retrieval-augmented generation (RAG), curator, extractor, and agentic AI applications. It can be used to convert scanned or rendered PDFs, presentation slides, forms, reports, tables, and mixed-content document pages into structured outputs for downstream indexing, retrieval, analytics, model training-data creation, and human-in-the-loop review.
\n\n### Capability Highlights:
\n* Expanded multilingual OCR support, with substantial gains on CJK and Indic-script document text.
\n* Improved handwritten-text extraction for document pages containing informal, handwritten, or note-like content.
\n* Chart-to-table parsing that can identify chart regions and convert visible chart information into structured text for downstream use.
\n* Improved table handling, including stronger table detection, structure recovery, and text extraction on table-heavy documents.
\n\n### Release Date:
\nHugging Face August 3, 2026 via [URL](https://huggingface.co/nvidia/NVIDIA-Nemotron-Parse-2.0)
\n\n## References(s):\n* [Hugging Face Transformers mBART documentation](https://huggingface.co/docs/transformers/en/model_doc/mbart)
\n* [NVIDIA C-RADIO](https://huggingface.co/nvidia/C-RADIO)
\n\n## Model Architecture:\n**Architecture Type:** Transformer-based vision-encoder-decoder model
\n\n**Network Architecture:**
\n* Vision Encoder: ViT-H model based on NVIDIA C-RADIO
\n* Adapter Layer: 1D convolutions and normalization layers that compress the vision latent sequence before decoding
\n* Decoder: mBART decoder with 10 blocks
\n* Auxiliary Prediction Head: One training-time decoder prediction head is preserved separately in `auxiliary_prediction_heads.safetensors.extra` for future multi-token prediction research. Standard generation uses the tied decoder input/output embeddings; the default `model.safetensors`, Transformers examples, and vLLM examples do not load this auxiliary head.
\n* Tokenizer: The tokenizer contains 72,256 entries, including an approximately 20k-token expansion over NVIDIA Nemotron Parse v1.2 to improve multilingual token efficiency. The model also includes task/control tokens such as ``, ``, ``, and ``, plus the chart class token ``. Use of the tokenizer included in this model is governed by the [CC-BY-4.0 license](https://creativecommons.org/licenses/by/4.0/).
\n* Number of Parameters: < 1B
\n\n## Input(s):
\n**Input Type(s):** Image, Text
\n\n**Input Format(s):**
\n* Image: Red, Green, Blue (RGB)
\n* Text: Prompt string
\n\n**Input Parameters:**
\n* Image: Two-Dimensional (2D)
\n* Text: One-Dimensional (1D)
\n\n**Other Properties Related to Input:**
\n* Recommended maximum input resolution (Width, Height): 1664, 2048
\n* Recommended minimum input resolution (Width, Height): 1024, 1280
\n* Channel Count: 3
\n* Prompt format: one task prompt composed from the supported control tokens. The default prompt extracts bounding boxes, semantic classes, and text in markdown format: ``. The model can emit chart regions using `` when chart content is detected.
\n\n## Output(s):
\n**Output Type(s):** Text
\n\n**Output Format(s):** String
\n\n**Output Parameters:** One-Dimensional (1D)
\n\n**Other Properties Related to Output:** Nemotron Parse 2.0 returns a string encoding document text, semantic element classes, and bounding boxes. Postprocessing utilities in this repository can transform generated bounding boxes back to original image coordinates and convert table or chart-associated text into LaTeX, HTML, markdown, JSON, or CSV where supported.
\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA hardware and software frameworks, the model achieves faster training and inference times compared to CPU-only solutions.
\n\n## Quick Start\n\n### Install dependencies in your environment\n\nYou can use a public image _nvcr.io/nvidia/pytorch:25.03-py3_ with the following library versions installed on top:\n\n```bash\npip install accelerate==1.12.0\npip install albumentations==2.0.8\npip install transformers==5.6.1\npip install timm==1.0.22\npip install open_clip_torch==3.2.0\npip install einops==0.8.1\n```\n\n### Usage example\n\n```python\nimport torch\nfrom PIL import Image, ImageDraw\nfrom transformers import AutoModel, AutoProcessor, AutoTokenizer, GenerationConfig\nfrom postprocessing import extract_classes_bboxes, transform_bbox_to_original, postprocess_text\n\n# Load model and processor\nmodel_path = \"nvidia/NVIDIA-Nemotron-Parse-2.0\" # Or use a local path\ndevice = \"cuda:0\"\n\nmodel = AutoModel.from_pretrained(\n model_path,\n trust_remote_code=True,\n torch_dtype=torch.bfloat16\n).to(device).eval()\ntokenizer = AutoTokenizer.from_pretrained(model_path)\nprocessor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)\n\n# Load image\nimage = Image.open(\"path/to/your/image.jpg\")\ntask_prompt = \"~~\"\n# task_prompt = \"~~\"\n\n# Process image\ninputs = processor(images=[image], text=task_prompt, return_tensors=\"pt\", add_special_tokens=False).to(device)\n\ngeneration_config = GenerationConfig.from_pretrained(model_path, trust_remote_code=True)\n\n# Generate text\noutputs = model.generate(**inputs, generation_config=generation_config)\n\n# Decode the generated text\ngenerated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0]\n```\n\n### Postprocessing\n\n```python\nfrom PIL import ImageDraw\nfrom postprocessing import extract_classes_bboxes, transform_bbox_to_original, postprocess_text\n\nclasses, bboxes, texts = extract_classes_bboxes(generated_text)\nbboxes = [transform_bbox_to_original(bbox, image.width, image.height) for bbox in bboxes]\n\n# Specify output formats for postprocessing\ntable_format = \"latex\" # latex | HTML | markdown | json | json_hierarchical | csv\ntext_format = \"markdown\" # markdown | plain\nblank_text_in_figures = False # set True to remove text inside 'Picture' class\ntexts = [\n postprocess_text(\n text,\n cls=cls,\n table_format=table_format,\n text_format=text_format,\n blank_text_in_figures=blank_text_in_figures,\n )\n for text, cls in zip(texts, classes)\n]\n\nfor cl, bb, txt in zip(classes, bboxes, texts):\n print(cl, \": \", txt)\n\ndraw = ImageDraw.Draw(image)\nfor bbox in bboxes:\n draw.rectangle((bbox[0], bbox[1], bbox[2], bbox[3]), outline=\"red\")\n```\n\n#### Table output formats\n\nSupported values for `table_format`: `latex` | `HTML` | `markdown` | `json` | `json_hierarchical` | `csv`\n\n## Inference with vLLM\n\nNemotron Parse 2.0 can be served with vLLM when using a vLLM build that includes Nemotron Parse remote-code support. On A100 and A10 systems, we recommend running `vllm serve` with `--attention-backend=TRITON_ATTN`.\n\nInstall the following dependencies on top of the serving image:\n\n```bash\npip install albumentations timm open_clip_torch einops\n```\n\nThis export keeps `lm_head.weight` tied to `decoder.embed_tokens.weight` and does not materialize a duplicate output-head tensor. Current vLLM 0.20 Nemotron Parse builds create a separate output head unless patched. If your vLLM build does not already support tied Nemotron Parse output embeddings, fetch the included runtime patch and add it to `PYTHONPATH` before starting vLLM:\n\n```bash\nPATCH_ROOT=$(python - <<'PY'\nfrom huggingface_hub import snapshot_download\nprint(snapshot_download(\n \"nvidia/NVIDIA-Nemotron-Parse-2.0\",\n allow_patterns=\"vllm_tied_patch/sitecustomize.py\",\n))\nPY\n)\nexport PYTHONPATH=\"${PATCH_ROOT}/vllm_tied_patch:${PYTHONPATH}\"\n```\n\n### vLLM Inference example\n\n#### Option 1: end-to-end Python inference\n\n```python\nfrom vllm import LLM, SamplingParams\nfrom PIL import Image\n\n\ndef main():\n sampling_params = SamplingParams(\n temperature=0,\n top_k=1,\n repetition_penalty=1.1,\n max_tokens=9000,\n skip_special_tokens=False,\n )\n\n llm = LLM(\n model=\"nvidia/NVIDIA-Nemotron-Parse-2.0\",\n max_num_seqs=64,\n limit_mm_per_prompt={\"image\": 1},\n dtype=\"bfloat16\",\n trust_remote_code=True,\n )\n\n image = Image.open(\"\")\n\n prompts = [\n {\n \"prompt\": \"\",\n \"multi_modal_data\": {\n \"image\": image,\n },\n },\n {\n \"encoder_prompt\": {\n \"prompt\": \"\",\n \"multi_modal_data\": {\n \"image\": image,\n },\n },\n \"decoder_prompt\": \"\",\n },\n ]\n\n outputs = llm.generate(prompts, sampling_params)\n\n for output in outputs:\n prompt = output.prompt\n generated_text = output.outputs[0].text\n print(f\"Decoder prompt: {prompt!r}, Generated text: {generated_text!r}\")\n\n\nif __name__ == \"__main__\":\n main()\n```\n\n#### Option 2: vLLM serve\n\n```bash\nvllm serve nvidia/NVIDIA-Nemotron-Parse-2.0 \\\n --dtype bfloat16 \\\n --max-num-seqs 8 \\\n --limit-mm-per-prompt '{\"image\": 1}' \\\n --trust-remote-code \\\n --port 8000 \\\n --chat-template chat_template.jinja\n```\n\nThen run inference through the OpenAI-compatible API:\n\n```python\nimport base64\nfrom openai import OpenAI\n\nclient = OpenAI(\n base_url=\"http://localhost:8000/v1\",\n)\n\nwith open(\"\", \"rb\") as f:\n img_b64 = base64.b64encode(f.read()).decode(\"utf-8\")\n\nprompt_text = \"\"\n\nresp = client.chat.completions.create(\n model=\"nvidia/NVIDIA-Nemotron-Parse-2.0\",\n messages=[\n {\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": prompt_text,\n },\n {\n \"type\": \"image_url\",\n \"image_url\": {\n \"url\": f\"data:image/png;base64,{img_b64}\",\n },\n },\n ],\n }\n ],\n max_tokens=9000,\n temperature=0.0,\n extra_body={\n \"repetition_penalty\": 1.1,\n \"top_k\": 1,\n \"skip_special_tokens\": False,\n },\n)\nprint(resp.choices[0].message.content)\n```\n\n### Prompt and logits-processor options\n\nThe recommended default prompt extracts bounding boxes, semantic classes, and text in markdown format:\n\n`~~`\n\nTo extract text that appears inside embedded images or figures, use:\n\n``\n\nIf only bounding boxes and classes are needed, use:\n\n`~~`\n\nThis repository includes two optional logits processors:\n* `NemotronParseRepetitionStopProcessor`: detects repeating n-grams during generation and forces the model to close the coordinate block when repeated structured output suggests a potential hallucination.\n* `NemotronParseTableInsertionLogitsProcessor`: forces every block to follow a table structure, which can be useful when running the model on table image crops.\n\nPlease refer to `example_with_processor.py` for Python-model usage. With vLLM, export `logitsprocs/` to `PYTHONPATH` and pass the desired processor to `vllm serve`, for example:\n\n```bash\nvllm serve nvidia/NVIDIA-Nemotron-Parse-2.0 \\\n --dtype bfloat16 \\\n --max-num-seqs 4 \\\n --limit-mm-per-prompt '{\"image\": 1}' \\\n --attention-backend=TRITON_ATTN \\\n --trust-remote-code \\\n --logits-processors nemotron_parse_vllm_logitprocs:NemotronParseTableInsertionLogitsProcessor \\\n --port 8000\n```\n\nAn example of inference with the vLLM OpenAI-compatible server is available in `vllm_example.py`.\n\n## Software Integration:\n**Runtime Engine(s):**\n* Transformers
\n* vLLM
\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Ampere
\n* NVIDIA Blackwell
\n* NVIDIA Hopper
\n* NVIDIA Turing
\n\n**Supported Operating System(s):**\n* Linux
\n\nThe 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 is essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
\n\n## Model Version(s):\nNemotron Parse 2.0
\n\n## Training, Testing, and Evaluation Datasets:\n\n### Training Dataset\n\n**Data Modality:**
\n* Image
\n* Text
\n\n**Image Training Data Size:**
\n* 1 Million to 1 Billion Images
\n\n**Text Training Data Size:**
\n* 1 Billion to 10 Trillion Tokens
\n\n**Data Collection Method by dataset:**
\n* Hybrid: Automated, Human, Synthetic
\n\n**Labeling Method by dataset:**
\n* Hybrid: Automated, Human, Synthetic
\n\n**Properties:** The training set contains millions of image-text items aggregated across large document, table, and layout datasets. The data consists of document-page and table images paired with OCR text, bounding boxes, and layout labels. Sources include rendered digital documents, scientific papers, PDFs, Wikipedia-style pages, and synthetic document, table, word, and character renderings. Annotations come from OCR and layout models, third-party OCR services, synthetic-generation pipelines, and human labeling.
\n\n### Testing and Evaluation Dataset\n\nTesting and evaluation use internal and public document-understanding benchmarks that cover OCR quality, layout structure, table parsing, reading order, and visual grounding. Dataset collection and labeling methods are hybrid and include automated, model-derived, manually labeled, and synthetic annotations.
\n\n### Evaluation Results\n\nBenchmark coverage:
\n* ParseBench evaluates document parsing across text fidelity, semantic formatting, tables, charts, and visual grounding with layout boxes/classes.
\n* IndicVisionBench evaluates OCR quality for Indic-language pages, including Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Tamil, and Telugu.
\n* MOSCAR evaluates multilingual synthetic OCR across broad script coverage, including Latin, Arabic, Cyrillic, Chinese, Hangul, Japanese, Indic scripts, Hebrew, Thai, Greek, and others.
\n* OmniDocBench Notes (Handwriting) evaluates text-block edit distance on note-style document pages from the `data_source: note` slice.
\n\nThe following results compare NVIDIA Nemotron Parse 2.0 with NVIDIA Nemotron Parse v1.2 on internal and public evaluation benchmarks. NVIDIA Nemotron Parse 2.0 results use the equal-weight checkpoint soup from training checkpoints 58k, 60k, 62k, 64k, and 66k. Unless marked otherwise, metrics are higher-is-better; arrows indicate the direction of the 2.0 change relative to v1.2.
\n\n| Benchmark | Metric | NVIDIA Nemotron Parse v1.2 | NVIDIA Nemotron Parse 2.0 | Change |\n| :---- | :---- | :---- | :---- | :---- |\n| ParseBench | Overall score | 0.5782 | 0.6391 | ↑ +0.0609 |\n| OmniDocBench Notes (Handwriting) | Text edit distance (lower is better) | 0.9739 | 0.3395 | ↓ -0.6343 |\n| IndicVisionBench | Overall ANLS character | 0.0612 | 0.7203 | ↑ +0.6592 |\n| MOSCAR (Multilingual) | Overall BoC F1 | 0.4410 | 0.9102 | ↑ +0.4692 |\n\n## Inference:\n**Acceleration Engine:** Transformers, vLLM
\n**Test Hardware:**
\n* H100
\n* A100
\n \n## **Get Help**\n\n### NVIDIA Developer Community Forum\nGet access to community knowledge base articles and support cases (https://forums.developer.nvidia.com/)\n\n### Enterprise Support\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. 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.
\n\nPlease make sure you have proper rights and permissions for all input image and video content. If an input image contains people, personal health information, confidential business data, or intellectual property, the model may extract or reproduce visible text from that content.
\n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ subcards: [Bias](https://huggingface.co/nvidia/NVIDIA-Nemotron-Parse-2.0/blob/main/bias.md), [Explainability](https://huggingface.co/nvidia/NVIDIA-Nemotron-Parse-2.0/blob/main/explainability.md), [Safety & Security](https://huggingface.co/nvidia/NVIDIA-Nemotron-Parse-2.0/blob/main/safety.md), and [Privacy](https://huggingface.co/nvidia/NVIDIA-Nemotron-Parse-2.0/blob/main/privacy.md).
\nPlease report model quality, risk, security vulnerabilities, or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
","displayName","NVIDIA Nemotron-Parse v2.0",["D",1785951553348],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nim/nvidia/models/nemotron-parse-v2.0","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms",{"_267":268,"_269":270,"_271":272,"_273":34,"_274":275},"isMultinodeEnabled","isMultiArchitecture","bias","## Bias\n\nField | Response\n:---- | :----\nParticipation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None\nMeasures taken to mitigate against unwanted bias: | Dataset curation, filtering, and evaluation include review across document types, layouts, languages, and scripts where available. Users should evaluate performance on their target document domains before deployment.\nBias Metric (If Measured): | Not measured as a demographic fairness metric. Model quality is evaluated through document parsing, OCR, layout, table, chart, and reading-order metrics.\n(For GPAI Models) Which characteristic (feature) show(s) the greatest difference in performance?: | Pending final release evaluation. Expected variation should be assessed across language, script, image quality, document domain, table complexity, and chart type.\n(For GPAI Models): Which feature(s) have have the worst performance overall? | Pending final release evaluation. Known risk areas include low-resolution scans, handwriting, dense tables, chart-heavy pages, rare scripts, decorative fonts, and unusual layouts.\n(For GPAI Models): If using internal data, description of methods implemented in data acquisition or processing, if any, to address the prevalence of identifiable biases in the training, testing, and validation data: | Training and evaluation data are curated to cover diverse document layouts, document domains, languages, scripts, and synthetic/rendered document conditions where available.\n(For GPAI Models): Tools used to assess statistical imbalances and highlight patterns that may introduce bias into AI models: | Internal dataset audits, benchmark stratification, OCR/layout evaluation, ParseBench, IndicVisionBench, MOSCAR, and qualitative review.\n(For GPAI Models): Tools used to assess statistical imbalances and highlight patterns that may introduce bias into AI models: | The datasets used for document parsing evaluation do not collectively or exhaustively represent all document domains, languages, scripts, layouts, or image-quality conditions. To mitigate this, users should evaluate NVIDIA Nemotron Parse 2.0 on representative target documents and consider additional evaluation, fine-tuning, filtering, or human review for their intended workflow.","explainability","## Explainability\n\nField | Response\n:---- | :----\nIntended Task/Domain: | Document understanding, OCR, layout parsing, table extraction, and text extraction with spatial grounding.\nModel Type: | Transformer-based vision-encoder-decoder model.\nIntended Users: | Developers and teams building document intelligence, retrieval-augmented generation (RAG), data-curation, extraction, and multimodal agent workflows.\nOutput: | Structured text containing document element classes, bounding boxes, reading-order information, chart/table annotations, and extracted text.\n(For GPAI Models): Tools used to evaluate datasets to identify synthetic data and ensure data authenticity. | Internal dataset provenance checks, dataset audits, synthetic-data source tracking, and qualitative review.\nDescribe how the model works: | NVIDIA Nemotron Parse 2.0 encodes an input document image with a vision encoder, compresses visual features with an adapter, and uses a text decoder conditioned on task prompt tokens to generate structured document annotations. The 2.0 tokenizer includes an approximately 20k-token multilingual vocabulary expansion and chart-aware output support through the `` class token.\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable\nTechnical Limitations & Mitigation: | Performance can vary for low-resolution scans, motion blur, handwriting, decorative fonts, dense tables, unusual layouts, unsupported languages or scripts, and documents with heavy compression artifacts. Users should evaluate outputs on representative documents and use human review for high-impact workflows.\nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | OCR accuracy, layout/class accuracy, table extraction quality, chart extraction quality, reading-order quality, grounding quality, latency, throughput, and qualitative visual inspection.\nPotential Known Risks: | The model may miss text, hallucinate structure, assign incorrect classes, produce inaccurate bounding boxes, or incorrectly order elements. Downstream systems should treat output as model-generated extraction results and apply validation, confidence checks, and human review where appropriate.\nLicensing: | Use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Use of the tokenizer included in this model is governed by the [CC-BY-4.0 license](https://creativecommons.org/licenses/by/4.0/).","privacy","## Privacy\n\nField | Response\n:---- | :----\nGeneratable or reverse engineerable personal data? | No\nPersonal data used to create this model? | Yes\nWas consent obtained for any personal data used? | Unknown (Externally-Sourced Data)\nHow often is dataset reviewed? | Before release and as needed in response to dataset, model, or policy changes.\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | Unknown (Externally-Sourced Data)\nIf personal data was collected for the development of the model, was it collected directly by NVIDIA? | No\nIf personal data was collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable\nIf personal data was collected for the development of this AI model, was it minimized to only what was required? | Yes\nWas data from user interactions with the AI model (e.g. user input and prompts) used to train the model? | No\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes\nIs data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally sourced data.\nApplicable Privacy Policy | [NVIDIA Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/)","safetyAndSecurity","## Safety\n\nField | Response\n:---- | :----\nModel Application Field(s): | Document intelligence, information retrieval, enterprise automation, data extraction, multimodal data curation, and retrieval-augmented generation workflows.\nDescribe the life critical impact (if present). | Not Applicable. This model is not intended to make life-critical, legal, medical, financial, or safety-critical decisions without downstream validation and human oversight.\n(For GPAI Models): Description of methods implemented in data acquisition or processing, if any, to address other types of potentially harmful data in the training, testing, and validation data: | Dataset curation, filtering, provenance tracking, and review are applied where applicable to reduce harmful or inappropriate content exposure.\n(For GPAI Models): Description of any methods implemented in data acquisition or processing, if any, to address illegal or harmful content in the training data, including, but not limited to, child sexual abuse material (CSAM) and non-consensual intimate imagery (NCII) | Dataset handling follows NVIDIA data governance and safety review processes. The model is intended for document parsing and is not designed to generate illegal or harmful content.\nUse Case Restrictions: | Use of this model must comply with the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Users are responsible for ensuring they have rights and permissions for input documents and images.\nModel and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.\n\n(For GPAI Models): This AI model was developed based on our policies to ensure responsible data handling and risk mitigation. The datasets used for training have been reviewed for harmful content and illegal content consistent with our policies. Ongoing review and monitoring mechanisms are in place based on our policies and to maintain data integrity. NVIDIA Nemotron Parse 2.0 is intended for document extraction and structure parsing. Developers are responsible for safe system integration, input-data permissions, output validation, and use-case-specific controls.\n\n**You are responsible for ensuring that your use of NVIDIA-provided models complies with all applicable laws.**","featureLabels",[258,259],"displayLabels",[247,248],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_211":256,"_250":256,"_251":257,"_253":254},{"_211":249,"_250":249,"_251":252,"_253":254},"NSPECT-YUTC-X5GX","value","filter",{"_255":254,"_250":249},"category","label","field","False",{"_255":254,"_250":256},{"_211":265,"_250":265,"_251":266,"_253":263},{"_211":260,"_250":261,"_251":262,"_253":263},"NVIDIA NIM","nimmcro_nvidia_nim",{"_255":264,"_250":261},"feature","nvidia_nim","NVIDIA AI Enterprise Supported",{"_255":254,"_250":265},"governingTerms","
GOVERNING DOWNLOAD TERMS: Use of the model is governed by the OpenMDW License Agreement, version 1.1.
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GOVERNING DOWNLOAD TERMS: Use of the model is governed by the OpenMDW License Agreement, version 1.1.
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GOVERNING HOSTING TERMS: Use of this trial service is governed by the NVIDIA API Trial Terms of Service. Use of the model is governed by the OpenMDW License Agreement, version 1.1.
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