Update OpenMDW licensing and streamline vLLM inference

CONTRIBUTING.md

@@ -0,0 +1,65 @@
+# Contributing
+
+## Developer Certificate of Origin
+
+The Developer Certificate of Origin (DCO) is a way to certify that you wrote or otherwise have the right to submit the code you are contributing to the project.
+
+By signing off on your contribution, you certify the following DCO (Version 1.1):
+* Full text of the DCO (https://developercertificate.org/):
+  ```
+    Developer Certificate of Origin
+    Version 1.1
+    Copyright (C) 2004, 2006 The Linux Foundation and its contributors.
+    Everyone is permitted to copy and distribute verbatim copies of this
+    license document, but changing it is not allowed.
+    Developer's Certificate of Origin 1.1
+    By making a contribution to this project, I certify that:
+    (a) The contribution was created in whole or in part by me and I
+        have the right to submit it under the open source license
+        indicated in the file; or
+    (b) The contribution is based upon previous work that, to the best
+        of my knowledge, is covered under an appropriate open source
+        license and I have the right under that license to submit that
+        work with modifications, whether created in whole or in part
+        by me, under the same open source license (unless I am
+        permitted to submit under a different license), as indicated
+        in the file; or
+    (c) The contribution was provided directly to me by some other
+        person who certified (a), (b) or (c) and I have not modified
+        it.
+    (d) I understand and agree that this project and the contribution
+        are public and that a record of the contribution (including all
+        personal information I submit with it, including my sign-off) is
+        maintained indefinitely and may be redistributed consistent with
+        this project or the open source license(s) involved.
+  ```
+
+
+## Sign your work
+
+To certify the DCO, you must add a "Signed-off-by" line to your commit messages.
+
+**Important:** You must use your real name (no pseudonyms or anonymous contributions).
+
+**Example:**
+
+```text
+Fix typo in tokenizer config
+
+Signed-off-by: Example Contributor <contributor@example.org>
+```
+
+### Git Command
+
+If you are using the command line, you can sign off automatically by adding the `-s` flag:
+
+```bash
+git commit -s -m "Describe the contribution"
+```
+
+## Submitting changes
+
+1. **Fork the repository** on Hugging Face.
+2. **Create a branch** for your fix.
+3. **Commit your changes** with the `-s` flag to sign them.
+4. **Open a Pull Request** (PR) on the Community tab of the model page.

LICENSE

@@ -0,0 +1,56 @@
+All binary model files and source code files are licensed under the OpenMDW-1.1 License.
+The tokenizer included with this model is separately licensed under the
+Creative Commons Attribution 4.0 International license (CC-BY-4.0):
+https://creativecommons.org/licenses/by/4.0/
+
+------------
+Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+
+OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)
+
+By exercising rights granted to you under this agreement, you accept and agree
+to its terms.
+
+As used in this agreement, "Model Materials" means the materials provided to
+you under this agreement, consisting of: (1) one or more machine learning
+models (including architecture and parameters); and (2) all related artifacts
+(including associated data, documentation and software) that are provided to
+you hereunder.
+
+Subject to your compliance with this agreement, permission is hereby granted,
+free of charge, to deal in the Model Materials without restriction, including
+under all copyright, patent, database, and trade secret rights included or
+embodied therein.
+
+If you distribute any portion of the Model Materials, you shall retain in your
+distribution (1) a copy of this agreement, and (2) all copyright notices and
+other notices of origin included in the Model Materials that are applicable to
+your distribution.
+If you file, maintain, or voluntarily participate in a lawsuit against any
+person or entity asserting that the Model Materials directly or indirectly
+infringe any patent or copyright, then all rights and grants made to you
+hereunder are terminated, unless that lawsuit was in response to a
+corresponding lawsuit first brought against you.
+
+This agreement does not impose any restrictions or obligations with respect to
+any use, modification, or sharing of any outputs generated by using the Model
+Materials.
+
+THE MODEL MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE, TITLE, NONINFRINGEMENT, ACCURACY, OR THE
+ABSENCE OF LATENT OR OTHER DEFECTS OR ERRORS, WHETHER OR NOT DISCOVERABLE, ALL
+TO THE GREATEST EXTENT PERMISSIBLE UNDER APPLICABLE LAW.
+
+YOU ARE SOLELY RESPONSIBLE FOR (1) CLEARING RIGHTS OF OTHER PERSONS THAT MAY
+APPLY TO THE MODEL MATERIALS OR ANY USE THEREOF, INCLUDING WITHOUT LIMITATION
+ANY PERSON'S COPYRIGHTS OR OTHER RIGHTS INCLUDED OR EMBODIED IN THE MODEL
+MATERIALS; (2) OBTAINING ANY NECESSARY CONSENTS, PERMISSIONS OR OTHER RIGHTS
+REQUIRED FOR ANY USE OF THE MODEL MATERIALS; OR (3) PERFORMING ANY DUE
+DILIGENCE OR UNDERTAKING ANY OTHER INVESTIGATIONS INTO THE MODEL MATERIALS OR
+ANYTHING INCORPORATED OR EMBODIED THEREIN.
+IN NO EVENT SHALL THE PROVIDERS OF THE MODEL MATERIALS BE LIABLE FOR ANY CLAIM,
+DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
+OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE MODEL MATERIALS, THE
+USE THEREOF OR OTHER DEALINGS THEREIN.

README.md

@@ -1,8 +1,5 @@
 ---
-license: other
-license_name: nvidia-open-model-license
-license_link: >-
-  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
+license: openmdw-1.1
 pipeline_tag: image-text-to-text
 tags:
 - VLM
@@ -18,7 +15,9 @@ NVIDIA Nemotron Parse 2.0 transforms document images into structured, machine-re
 This model is ready for commercial or non-commercial use. <br>
 
 ### License/Terms of Use:
-Governing 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/).<br>
+This model and its associated configuration files are licensed under the [OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)](https://openmdw.ai/license/1-1/). Use of the tokenizer included in this model is governed by the [CC-BY-4.0 license](https://creativecommons.org/licenses/by/4.0/).<br>
+
+This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use. Contributions are accepted under the policy in [CONTRIBUTING.md](CONTRIBUTING.md).<br>
 
 ### Deployment Geography:
 Global<br>
@@ -33,7 +32,7 @@ NVIDIA Nemotron Parse 2.0 is designed for developers and teams building document
 * Improved table handling, including stronger table detection, structure recovery, and text extraction on table-heavy documents.<br>
 
 ### Release Date:  <br>
-Hugging Face August 3, 2026 via [URL](https://huggingface.co/nvidia/NVIDIA-Nemotron-Parse-2.0) <br>
+Hugging Face August 3, 2026 on the [NVIDIA Nemotron Parse 2.0 model page](https://huggingface.co/nvidia/NVIDIA-Nemotron-Parse-2.0) <br>
 
 ## References(s):
 * [Hugging Face Transformers mBART documentation](https://huggingface.co/docs/transformers/en/model_doc/mbart) <br>
@@ -80,19 +79,21 @@ Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated sys
 
 ## Quick Start
 
-### Install dependencies in your environment
+### Direct Transformers inference dependencies
 
-You can use a public image _nvcr.io/nvidia/pytorch:25.03-py3_ with the following library versions installed on top:
+This installation is only for the direct Transformers example in the next section. It is not needed for the vLLM container workflow below. You can use the 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
 pip install open_clip_torch==3.2.0
 pip install einops==0.8.1
+pip install beautifulsoup4
 ```
 
+`open_clip_torch` is currently needed only by the direct Transformers path because C-RADIO's remote-code validation inspects an optional OpenCLIP adaptor. Nemotron Parse does not configure or execute that adaptor. Albumentations is not used by the Nemotron Parse 2.0 processor.
+
 ### Usage example
 
 ```python
@@ -110,11 +111,11 @@ model = AutoModel.from_pretrained(
     trust_remote_code=True,
     torch_dtype=torch.bfloat16
 ).to(device).eval()
-tokenizer = AutoTokenizer.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
 processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
 
 # Load image
-image = Image.open("path/to/your/image.jpg")
+image = Image.open("document.png")
 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>"
 
@@ -168,15 +169,9 @@ Supported values for `table_format`: `latex` | `HTML` | `markdown` | `json` | `j
 
 ## Inference with vLLM
 
-**Tested vLLM versions:** v0.20-v0.26.
+**Supported vLLM versions:** v0.20-v0.26. The container-only examples below were validated with v0.20.0.
 
-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`.
-
-Install the following dependencies on top of the serving image:
-
-```bash
-pip install albumentations timm open_clip_torch einops
-```
+Nemotron Parse 2.0 can be served directly from a standard vLLM container that includes Nemotron Parse support. No additional `albumentations` or `open_clip_torch` installation is required for this vLLM path. The model's lightweight encoder configuration prevents vLLM startup from recursively importing C-RADIO's unused OpenCLIP adaptor. This container-only dependency path was validated with vLLM v0.20. On A100 and A10 systems, we recommend running `vllm serve` with `--attention-backend=TRITON_ATTN`.
 
 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:
 
@@ -218,7 +213,7 @@ def main():
         trust_remote_code=True,
     )
 
-    image = Image.open("<YOUR-IMAGE-PATH>")
+    image = Image.open("document.png")
 
     prompts = [
         {
@@ -258,8 +253,7 @@ vllm serve nvidia/NVIDIA-Nemotron-Parse-2.0 \
     --max-num-seqs 8 \
     --limit-mm-per-prompt '{"image": 1}' \
     --trust-remote-code \
-    --port 8000 \
-    --chat-template chat_template.jinja
+    --port 8000
 ```
 
 Then run inference through the OpenAI-compatible API:
@@ -270,9 +264,10 @@ from openai import OpenAI
 
 client = OpenAI(
     base_url="http://localhost:8000/v1",
+    api_key="EMPTY",
 )
 
-with open("<YOUR-IMAGE-PATH>", "rb") as f:
+with open("document.png", "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>"
@@ -296,7 +291,7 @@ resp = client.chat.completions.create(
             ],
         }
     ],
-    max_tokens=9000,
+    max_tokens=8192,
     temperature=0.0,
     extra_body={
         "repetition_penalty": 1.1,
@@ -325,9 +320,19 @@ This repository includes two optional logits processors:
 * `NemotronParseRepetitionStopProcessor`: detects repeating n-grams during generation and forces the model to close the coordinate block when repeated structured output suggests a potential hallucination.
 * `NemotronParseTableInsertionLogitsProcessor`: forces every block to follow a table structure, which can be useful when running the model on table image crops.
 
-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:
+Please refer to `example_with_processor.py` for Python-model usage. With vLLM, add the model repository's `logitsprocs/` directory to `PYTHONPATH` and pass the desired processor to `vllm serve`:
 
 ```bash
+PROCESSOR_ROOT=$(python - <<'PY'
+from huggingface_hub import snapshot_download
+print(snapshot_download(
+    "nvidia/NVIDIA-Nemotron-Parse-2.0",
+    allow_patterns="logitsprocs/nemotron_parse_vllm_logitprocs.py",
+))
+PY
+)
+export PYTHONPATH="${PROCESSOR_ROOT}/logitsprocs:${PYTHONPATH}"
+
 vllm serve nvidia/NVIDIA-Nemotron-Parse-2.0 \
   --dtype bfloat16 \
   --max-num-seqs 4 \

VLLM_COMPATIBILITY.md

@@ -4,6 +4,8 @@ This directory is an inference-only Nemotron Parse export. Training-only `decode
 
 The removed auxiliary tensors are preserved in `auxiliary_prediction_heads.safetensors.extra` using safetensors format. The non-standard suffix keeps current vLLM weight discovery from loading these future-use tensors during standard inference.
 
+The vLLM path uses the lightweight RADIO configuration embedded in this model repository. It does not import C-RADIO's optional OpenCLIP adaptor, and the Nemotron Parse 2.0 processor does not use Albumentations. A standard supported vLLM container therefore does not need either `open_clip_torch` or `albumentations` installed. Direct Transformers inference still resolves the upstream C-RADIO remote implementation and currently requires `open_clip_torch` during remote-code validation.
+
 - Kept tensors: `666`
 - Dropped extra-head tensors: `4`
 - Auxiliary tensors preserved in sidecar: `4`

explainability.md

@@ -13,4 +13,4 @@ Technical Limitations & Mitigation: | Performance can vary for low-resolution sc
 Verified to have met prescribed NVIDIA quality standards: | Yes
 Performance Metrics: | OCR accuracy, layout/class accuracy, table extraction quality, chart extraction quality, reading-order quality, grounding quality, latency, throughput, and qualitative visual inspection.
 Potential 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.
-Licensing: | 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/).
+Licensing: | Use of this model is governed by the [OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)](https://openmdw.ai/license/1-1/). Use of the tokenizer included in this model is governed by the [CC-BY-4.0 license](https://creativecommons.org/licenses/by/4.0/).

hf_nemotron_parse_config.py

@@ -3,8 +3,6 @@ from quopri import decodestring
 from transformers import PretrainedConfig
 from typing import List, Optional
 
-from transformers.dynamic_module_utils import get_class_from_dynamic_module
-
 class NemotronParseTextConfig(PretrainedConfig):
     """
     Configuration class for NemotronParse text decoder (mBART-based).
@@ -69,6 +67,17 @@ class NemotronParseTextConfig(PretrainedConfig):
         self.max_sequence_length = max_sequence_length
 
 
+class NemotronParseEncoderConfig(PretrainedConfig):
+    """Lightweight RADIO config used by vLLM without importing C-RADIO code."""
+
+    model_type = "radio"
+
+    def __init__(self, **kwargs):
+        kwargs.pop("model_type", None)
+        super().__init__(**kwargs)
+        self.model_type = self.__class__.model_type
+
+
 class NemotronParseConfig(PretrainedConfig):
     """
     Configuration class for NemotronParse model.
@@ -110,8 +119,7 @@ class NemotronParseConfig(PretrainedConfig):
             
         if encoder is not None:
             assert "auto_map" in encoder and "AutoConfig" in encoder["auto_map"]
-            vision_auto_config = get_class_from_dynamic_module(*encoder["auto_map"]["AutoConfig"].split("--")[::-1])
-            self.encoder = vision_auto_config(**encoder)
+            self.encoder = NemotronParseEncoderConfig(**encoder)
         else:
             self.encoder = PretrainedConfig()
 

hf_nemotron_parse_modeling.py

@@ -15,6 +15,7 @@ from transformers.modeling_outputs import BaseModelOutput
 from transformers.models.encoder_decoder.modeling_encoder_decoder import shift_tokens_right
 from .hf_nemotron_parse_config import NemotronParseConfig
 from transformers import AutoModel
+from transformers.dynamic_module_utils import get_class_from_dynamic_module
 import time
 from transformers.modeling_attn_mask_utils import (
     _prepare_4d_attention_mask,
@@ -381,6 +382,17 @@ class RadioWithNeck(nn.Module):
     
     def __init__(self, config):
         super().__init__()
+        # The outer config intentionally keeps the RADIO configuration local
+        # and lightweight so vLLM serving does not recursively inspect the
+        # unused C-RADIO OpenCLIP adaptor. Transformers inference still needs
+        # the concrete remote RADIO config before constructing AutoModel.
+        auto_config = getattr(config, "auto_map", {}).get("AutoConfig")
+        if auto_config and "--" in auto_config:
+            vision_auto_config = get_class_from_dynamic_module(
+                *auto_config.split("--")[::-1]
+            )
+            config = vision_auto_config(**config.to_dict())
+
         self.config = config
         self.processor_normalizes = bool(getattr(config, "processor_normalizes", False))
         self._input_conditioner_externalized = False

logitsprocs/nemotron_parse_vllm_logitprocs.py

@@ -23,7 +23,7 @@ from vllm.v1.sample.logits_processor.interface import BatchUpdate, LogitsProcess
 
 
 def _strip_trailing_negative_token_ids(token_ids: List[int]) -> List[int]:
-    """vLLM v1 keeps a trailing -1 placeholder in output_token_ids."""
+    """Remove vLLM v1 trailing negative sentinel IDs from output_token_ids."""
     i = len(token_ids)
     while i > 0 and token_ids[i - 1] < 0:
         i -= 1
@@ -344,4 +344,3 @@ class NemotronParseRepetitionStopLogitsProcessor(LogitsProcessor):
 
         return logits
 
-

postprocessing.py

@@ -18,7 +18,7 @@ def extract_classes_bboxes(text: str):
         bboxes.append((float(x1), float(y1), float(x2), float(y2)))
         texts.append(text)
 
-    # TODO: Remove when fixed
+    # Normalize the legacy inline-formula class alias.
     classes = [
         "Formula" if cls == "Inline-formula" else cls for cls in classes
     ]

pyproject.toml

@@ -6,18 +6,19 @@ requires-python = ">=3.10"
 dependencies = [
     "transformers==5.6.1",
     "accelerate==1.12.0",
-    "albumentations==2.0.8",
     "timm==1.0.22",
     "einops",
     "Pillow",
     "numpy",
     "opencv-python-headless",
     "beautifulsoup4",
-    "open-clip-torch==3.2.0",
     "pytest>=9.0.3",
 ]
 
 [project.optional-dependencies]
+# Direct Transformers inference currently loads C-RADIO remote code, whose
+# optional OpenCLIP adaptor is inspected during module validation.
+transformers-inference = ["open-clip-torch==3.2.0"]
 # vLLM serving (install separately in the serving container).
 vllm = ["openai"]
 # Development / testing.

safety.md

@@ -6,7 +6,7 @@ Model Application Field(s): | Document intelligence, information retrieval, ente
 Describe 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.
 (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.
 (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.
-Use 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.
+Use Case Restrictions: | Use of this model must comply with the [OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)](https://openmdw.ai/license/1-1/). Users are responsible for ensuring they have rights and permissions for input documents and images.
 Model 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.
 
 (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.

safety_security.md

@@ -10,7 +10,7 @@ Not Applicable
 
 ## Use Case Restrictions:
 
-Abide by NVIDIA Open Model License Agreement
+Abide by the [OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)](https://openmdw.ai/license/1-1/).
 
 ## Model and dataset restrictions:
 

test_golden.py

@@ -8,7 +8,7 @@ WORKFLOW
 --------
 Step 1 — capture (run once against pinned deps, e.g. transformers>=5.6.1):
 
-    python test_golden.py --capture [--model-path /path/to/model]
+    python test_golden.py --capture [--model-path .]
 
     This writes golden_outputs.json next to this file.
 

uv.lock

@@ -30,41 +30,6 @@ wheels = [
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