NVIDIA Nemotron Parse v1.2

Official Resources

• NVIDIA Nemotron Parse v1.2 model

• C-RADIOv2-H model dependency

Role In Tabulus

NVIDIA Nemotron Parse v1.2 is a document vision-language model used by Tabulus for Step 2 table reconstruction from canonical MinerU table crops. The Tabulus adapter uses the model directly through Hugging Face Transformers and does not run a separate OCR engine or a page-layout pipeline.

NVIDIA Nemotron Parse v1.2 is a document parsing VLM for extracting text, tables, semantic classes, and spatial grounding from document images. The model card describes reading-flow ordering plus bounding boxes for document objects such as titles, tables, figures, footnotes, and bibliography regions. Tabulus uses the Table-class output and NVIDIA postprocessing helpers to derive HTML for the shared parser.

The registered Tabulus adapter is:

nemotron-parse-v1-2

The exact model checkpoint used by Tabulus is nvidia/NVIDIA-Nemotron-Parse-v1.2 at revision 2bd0189bffd6cdded6280d9f22a4077b25a504e3. The model uses the transitive nvidia/C-RADIOv2-H implementation; Tabulus verifies at runtime that the loaded C-RADIO code resolves to revision 0d8f4c18c877166eda07ddae1386bcad256b7a6a.

NVIDIA Nemotron Parse v1.2 consumes canonical MinerU table crops through the shared Step 2 adapter contract. Generated bounding boxes are preserved as provenance only and are not used to recrop the image.

Invocation

Run NVIDIA Nemotron Parse v1.2 through the shared reconstruction CLI:

export CUDA_VISIBLE_DEVICES=0

tabulus reconstruct-tables \
  --crops <canonical-crop-directory> \
  --adapter nemotron-parse-v1-2 \
  --device gpu:0

For multiple canonical crop roots:

tabulus reconstruct-tables \
  --crops-folder <table-crops-root> \
  --adapter nemotron-parse-v1-2 \
  --device gpu:0

NVIDIA Nemotron Parse v1.2 operates on the canonical table-crop handoff produced earlier in the Tabulus pipeline. It does not perform PDF profiling, table localization, or canonical crop generation.

Settings Used By Tabulus

The validated integration uses:

• model: nvidia/NVIDIA-Nemotron-Parse-v1.2

• model revision: 2bd0189bffd6cdded6280d9f22a4077b25a504e3

• resolved model class: NemotronParseForConditionalGeneration

• C-RADIO dependency: nvidia/C-RADIOv2-H

• expected C-RADIO revision: 0d8f4c18c877166eda07ddae1386bcad256b7a6a

• runtime: direct Hugging Face Transformers inference

• model dtype: bfloat16

• attention implementation: SDPA

• model image canvas: [2048, 1664]

• prompt: </s><s><predict_bbox><predict_classes><output_markdown><predict_no_text_in_pic>

• max_new_tokens=9000

• do_sample=False

• num_beams=1

• repetition_penalty=1.1

• registry capability: GPU only

The adapter uses NVIDIA generation-time processors from the pinned Nemotron helper code:

• TableInsertionLogitsProcessor with table prefix \begin{tabular}

• RepetitionStopProcessor with max_repetitions=10, ngram_sizes=[3, 4, 5, 6], and window_size=500

The helper files hf_logits_processor.py, postprocessing.py, and latex2html.py are loaded from the pinned Nemotron model revision and must already be available in the local Hugging Face cache. The adapter does not silently fetch those helper files during inference.

Native Output

NVIDIA Nemotron Parse v1.2 produces grounded semantic objects containing class, bounding-box, and text information. Table-class object text is represented natively as LaTeX/tabular content. Tabulus preserves the generated objects, bounding boxes, raw output, clean output, model and C-RADIO revisions, runtime versions, prompt, generation settings, source image size, and parser provenance as native evidence.

Tabulus converts Table-class output to HTML with NVIDIA’s pinned postprocess_text(table_format='HTML') helper and then sends the HTML to the existing shared parser:

tabulus.table_ocr.parsing:parse_table_text

There is no Nemotron-specific semantic repair pass. Tabulus does not correct cell contents, interpret bounding boxes as a new crop, infer missing structure from domain knowledge, merge continued tables, or perform reference-resolution heuristics during reconstruction.

The adapter writes the standard reconstruction artifact layers:

<crop-root>/
  reconstructions/
    nemotron-parse-v1-2/
      native/
      parsed/
      predictions/
      batch_summary.json

Prediction CSV creation follows the shared Tabulus rule: the adapter result must be ok and exactly one structured table must parse from the crop. If multiple Table objects or multiple structured tables are returned, Tabulus preserves the native and parsed evidence without arbitrarily choosing or merging one.

Validated Configuration

The validated software environment used Python 3.12, Transformers 5.6.1, Accelerate 1.12.0, albumentations 2.0.8, timm 1.0.22, einops 0.8.2, open-clip-torch 3.3.0, opencv-python-headless 5.0.0.93, beautifulsoup4 4.15.0, PyTorch 2.6.0+cu124, and torchvision 0.21.0+cu124. These are implementation and reproducibility details, not reconstruction-accuracy claims.

Limitations

This adapter reconstructs one canonical MinerU crop at a time. It does not independently locate or crop tables from the source PDF, run a separate OCR engine, run page-level layout detection, semantically correct cell contents, merge continued tables, classify reference tables, extract bibliographies, match references, resolve DOI values, or write final resolved CSV files.