Instructions to use sandeeprdy1729/thrisul-parser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sandeeprdy1729/thrisul-parser with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="sandeeprdy1729/thrisul-parser", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("sandeeprdy1729/thrisul-parser", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sandeeprdy1729/thrisul-parser with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sandeeprdy1729/thrisul-parser"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sandeeprdy1729/thrisul-parser",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/sandeeprdy1729/thrisul-parser

SGLang

How to use sandeeprdy1729/thrisul-parser with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "sandeeprdy1729/thrisul-parser" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sandeeprdy1729/thrisul-parser",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "sandeeprdy1729/thrisul-parser" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sandeeprdy1729/thrisul-parser",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use sandeeprdy1729/thrisul-parser with Docker Model Runner:
```
docker model run hf.co/sandeeprdy1729/thrisul-parser
```

thrisul-parser / README.md

sandeeprdy1729

updated model card

62bb8ea verified 14 days ago

preview code

Raw History Blame Contribute Delete

17.3 kB

---
license: other
license_name: openmdw-1.1
base_model: nvidia/Nemotron-3.5-Parse
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- document-parsing
- ocr
- tables
- charts
- ground-truth-sft
- grpo
- verifiable-rewards
- routed-refinement
- thrisul
---
# Thrisul-Parser (v2)

A derivative of [`nvidia/Nemotron-3.5-Parse`](https://huggingface.co/nvidia/Nemotron-3.5-Parse) (631M parameters).
v2 replaces v1's self-distillation with a **three-stage quality pipeline**: (1) **ground-truth SFT** whose targets are *exact annotations serialized in
the base model's own output format* (never the model's own outputs - this removes the imitation ceiling), (2) **GRPO with verifiable rewards**
(reward computed from ground truth: table TEDS, chart numeric-F1, format validity, bbox IoU), and (3) **verifier-routed refinement** re-labelled on the
final policy. Every P0 defect found in the v1 audit is addressed; the audit trail lives in the training notebook.

## What is new vs the base model
1. **GT-SFT (weights).** LoRA r=32 (decoder linears) trained on exact ground truth: procedurally rendered tables/charts/full pages with pixel-exact
   supervision, the **Thrisul-Doc D1-D4 custom corpora** (complex-table archetypes, hard charts, degradation curriculum, multi-column/financial/
   adversarial pages - all exact-GT by construction), 300 real ChartNet charts (CSV truth, CDLA-2.0), 0 FinTabNet.c financial tables (HTML
   truth, CDLA-2.0), and GT-serialized text-page replay. Targets were discovered to round-trip through the base model's own `postprocess_document`
   (mode: nemotron-parse) before training.
   Numeric tokens up-weighted (1+0.5x); inverse-sqrt length weighting; checkpoint selected on held-out structure scores, not "last".
2. **GRPO with verifiable rewards (weights).** 303 GRPO groups mined from hard buckets (lambda per blueprint 7.4). Group-normalized advantages (G rollouts/prompt), k3 KL to the SFT reference, prompts
   mined from the SFT policy's hard failures. Reward = (0.4, 0.25, 0.15, 0.15, 0.05) over (table, chart, structure validity, bbox IoU, EOS).
3. **Verifier-routed refinement (inference).** The parser runs once, a gradient-boosted router scores each table/chart region
   (decoder-token uncertainty + structure sanity + render-vs-crop similarity), and only regions predicted to gain are re-decoded at native
   resolution. Router labels were rebuilt on the FINAL policy (v1 fitted them on the base model - distribution shift fixed). Code ships with the
   model: `thrisul_pipeline.py` (`parse_page(..., mode="routed")`), `thrisul_router.joblib`.

## Training data (exact-GT or permissively licensed; NO benchmark pages were used for training)
- Procedurally rendered tables (merged cells, multi-level headers, numeric/currency/percent columns), charts (bar/hbar/line/pie/stacked), and
  full synthetic pages (title/sections/paragraphs/table/chart) with pixel-exact block annotations - generated by `thrisul_pipeline.py` itself.
- **Thrisul-Doc D1-D4 custom corpora (this repo, `thrisul_pipeline.py` v2.1, exact GT by construction, seeds disclosed in `thrisul_config.json`):**
  **D1** complex-table archetypes - 3-level header trees, colspan+rowspan merged cells, borderless layouts, unit rows, multi-index row labels,
  decimal look-alikes, zero/dash columns; **D2** hard charts - dual-axis bar+line, log-scale, 4-series crowded legends, negative bars, 100%-stacked
  (segments sum to exactly 100.0), irregular long categories; **D3** degradation curriculum - JPEG/blur/downscale/rotate/shadow/tone/occlusion/noise
  in tiers 1-3 applied to D1/D2/synthetic parents whose ground truth is inherited unchanged; **D4** multi-column + financial + adversarial pages -
  2-column reading-order ground truth, financial statements with parenthesized negatives and notes, decimal-shift look-alike traps, footer-lookalike
  text. Held-out eval slices for every D domain use seed ranges disjoint from training (61M/62M/63M/64M vs 6.1M-6.4M).
- Real charts from ChartNet (`ibm-granite/ChartNet`, CSV ground truth, CDLA-Permissive-2.0).
- Real financial tables from FinTabNet.c (`bsmock/FinTabNet.c`, HTML ground truth, CDLA-Permissive-2.0).
- Compute: Kaggle free tier (T4), LoRA, 1583 SFT optimizer steps + 303 GRPO groups. Targets: 6330 (~533 tokens mean).

<!-- THRISUL-BENCH:BEGIN -->
## Official benchmark results (thrisul-parser)

Measured 2026-10-31 on Kaggle 2×T4 (Transformers, fp16, greedy) by this benchmark notebook. Run tag: `v1`. Coverage: ParseBench 60 pages/dimension (official total 2,078) · olmOCR-Bench 400 pages (official total = all unique bench pages) · OmniDocBench: packaged · Extra suites: IndicVisionBench=53.05217098342906, MOSCAR=pending.

> Sampling disclosure: these runs cover a **seeded stratified subset** unless the config covered every page. Partial coverage is labeled — never quoted as the full-suite official score. Base-model deltas on identical pages are in `results.json`.

### ParseBench (official evaluator, sampled)

| group | score |
|---|---|
| chart | 0.206 |
| table | 0.168 |
| text_formatting | 0.037 |
| text_content | 0.491 |
| text | 0.091 |
| overall_rule_avg | 0.145 |
| **mean of table/chart/text** | **0.155** |

### olmOCR-Bench (official scorer, covered subset)

| category | pass rate |
|---|---|
| Overall | 29.5 |
| CI95_half_width | 1.5 |
| absent | 61.2 |
| baseline | 98.5 |
| math | 4.9 |
| order | 14.0 |
| present | 15.1 |
| table | 18.0 |
| arxiv_math | 7.6 |
| headers_footers | 56.7 |
| long_tiny_text | 14.0 |
| multi_column | 13.5 |
| old_scans | 24.8 |
| old_scans_math | 2.8 |
| table_tests | 18.3 |
### IndicVisionBench OCR + other suites (our protocol, official data)
| suite | metric | n | score |
|---|---|---|---|
| IndicVisionBench | ANLS-C | 862 | 53.05 |
> Metrics computed by this notebook on the official data with the IndicVisionBench paper's OCR protocol (arXiv 2511.04727, following OCRBenchv2: ANLS-C/ANLS-W/CER/WER) — not the authors' official harness; per-language breakdown lives in `results.json` (`indicvisionbench.agg`).
### Context A — olmOCR-Bench (pass rates, higher is better)
| System | Protocol | arxiv_math | old_scans_math | tables | old_scans | headers_footers | multi_column | long_tiny_text | baseline | Overall |
|---|---|---|---|---|---|---|---|---|---|---|
| **thrisul-parser v2.1** | **this run, 400-page covered subset** | 7.6 | 2.8 | 18.3 | 24.8 | **56.7** | 13.5 | 14.0 | 98.5 | **29.5 ± 1.5** |
| Nemotron-3.5-Parse (base) | **this run, identical pages** | 42.6 | 10.9 | 85.8 | 36.5 | 18.3 | 79.6 | 69.4 | 99.8 | **55.4 ± 1.6** |
| Chandra OCR 0.1.0* | published, full corpus | 82.2 | 80.3 | 88.0 | 50.4 | 90.8 | 81.2 | 92.3 | 99.9 | 83.1 ± 0.9 |
| olmOCR v0.4.0 | published, full corpus | 83.0 | 82.3 | 84.9 | 47.7 | 96.1 | 83.7 | 81.9 | 99.7 | 82.4 ± 1.1 |
| Infinity-Parser 7B* | published, full corpus | 84.4 | 83.8 | 85.0 | 47.9 | 88.7 | 84.2 | 86.4 | 99.8 | 82.5 |
| PaddleOCR-VL* | published, full corpus | 85.7 | 71.0 | 84.1 | 37.8 | 97.0 | 79.9 | 85.7 | 98.5 | 80.0 ± 1.0 |
| Marker 1.10.1 | published, full corpus | 83.8 | 66.8 | 72.9 | 33.5 | 86.6 | 80.0 | 85.7 | 99.3 | 76.1 ± 1.1 |
| DeepSeek-OCR | published, full corpus | 77.2 | 73.6 | 80.2 | 33.3 | 96.1 | 66.4 | 79.4 | 99.8 | 75.7 ± 1.0 |
| MinerU 2.5.4* | published, full corpus | 76.6 | 54.6 | 84.9 | 33.7 | 96.6 | 78.2 | 83.5 | 93.7 | 75.2 ± 1.1 |
| Mistral OCR API | published, full corpus | 77.2 | 67.5 | 60.6 | 29.3 | 93.6 | 71.3 | 77.1 | 99.4 | 72.0 ± 1.1 |
| Nanonets-OCR2-3B | published, full corpus | 75.4 | 46.1 | 86.8 | 40.9 | 32.1 | 81.9 | 93.0 | 99.6 | 69.5 ± 1.1 |
Published rows: [allenai/olmocr README leaderboard](https://github.com/allenai/olmocr), table dated 2025-10-21 (olmOCR v0.4.0). `*` = not fully open source per that README. The two "this run" rows share one protocol and are comparable **only to each other**; the gap to the published rows mostly reflects the covered subset, T4-class hardware, and our candidate generation — it is **not** a claim that those systems are 45+ points ahead of the base model.
### Context B — ParseBench (published 0–100 aggregate, self-reported)
| System | ParseBench (published) |
|---|---|
| LlamaParse Agentic Plus (API) | 88.8 |
| LlamaParse Agentic (API) | 83.07 |
| Anthropic Opus 5.5 (Effort High) | 78.9 |
| oi-parser | 78.3 |
| LightOnOCR-3-4B | 75.1 |
| LightOnOCR-3-0.8B | 74.6 |
| Infinity-Parser2-Pro (35B) | 74.3 |
| Infinity-Parser2-Flash | 73.25 |
| **Nemotron-3.5-Parse (base of this model; single/double/triple pass)** | **72.74 / 74.92 / 75.20** |
| MinerU2.5-Pro-2605-1.2B | 72.78 |
| KDL-Frontier-Parser-nano | 72.38 |
| Chandra-ocr-2 | 70.1 |
| PaddleOCR-VL-1.6 | 67.43 |
| Nemotron-Parse-2.0 | 63.91 |
| Google Gemini 3 Flash (Thinking Minimal) | 71.04 |
| OpenAI GPT-5 Mini (Reasoning Medium) | 51.52 |
| **thrisul-parser v2.1** | sampled official-protocol run: 0.145 overall_rule_avg / 0.155 mean-of-3 (different aggregation — **not** the 0–100 leaderboard scale); official full-corpus run pending |
Sources: parsebench.ai leaderboard (self-reported), as captured in this repo's `results.json` (`published_parsebench`). The base model ranks #1 among sub-1B parsers on that leaderboard.

### Context C — the wider parser landscape (qualitative)

| Parser | Access | Scale | License | Notes |
|---|---|---|---|---|
| **thrisul-parser v2.1** | open weights | 631M (+2.8M LoRA) | OpenMDW 1.1 (via base) | GT-SFT + GRPO + verifier-routed refinement; this repo |
| Nemotron-3.5-Parse (base) | open weights | 631M | OpenMDW 1.1 | bbox + classes + markdown output; #1 sub-1B on ParseBench |
| Nougat | open weights | ~250M–350M | MIT | academic PDFs; LaTeX output; no layout coordinates |
| GOT-OCR 2.0 | open weights | ~580M | MIT | "general OCR theory"; unified formulas/tables/text |
| olmOCR | open weights | ~7B | Apache-2.0 | PDF → plain markdown; strong on olmOCR-Bench by construction |
| Marker | open weights | pipeline+DL | GPL-3.0 | fast PDF→markdown; commercial use restricted |
| MinerU 2.5 | open weights | 1.2B (Pro) | AGPL-3.0 | layout+recognition pipeline; strong OmniDocBench scores |
| Docling | open toolkit | — | MIT | IBM; layout models + table structure; heavy CPU-friendly pipeline |
| Surya | open weights | — | GPL-3.0 | OCR/layout/reading-order toolkit (Datalab) |
| DeepSeek-OCR | open weights | ~3B | MIT | "contexts optical compression"; long-doc focus |
| PaddleOCR-VL | open weights | 0.9B | Apache-2.0 | strong olmOCR-Bench full-corpus score |
| Chandra | open weights | — | see repo¹ | Datalab; top published olmOCR-Bench row above |
| Mistral OCR | API only | — | commercial | strong tables; per-page pricing |
| LlamaParse | API only | — | commercial | multiple modes; ParseBench head rows above |
- Google Gemini 3 Flash (Thinking Minimal): 71.04
- 
<!-- THRISUL-BENCH:END -->

## Results (read the caveats)
All numbers below were measured by us on **held-out synthetic data and a seeded ParseBench-proxy sample on a T4 with Transformers** (no vLLM).
They are **not** the official ParseBench score and are **not comparable** to H100/vLLM numbers anywhere below. Headline deltas carry bootstrap 95% CIs.

Held-out synthetic regions (page-scale input; `routed` re-decodes only router-selected regions):

| subset | system | score | refine_frac | n |
|---|---|---|---|---|
| all | thr_routed | 0.952 | 1.000 | 226 |
| all | base_page | 0.766 | 0.000 | 226 |
| all | base_native | 0.939 | 1.000 | 226 |
| all | thr_page | 0.722 | 0.000 | 226 |
| all | thr_native | 0.952 | 1.000 | 226 |
| table | thr_routed | 0.964 | 1.000 | 128 |
| table | base_page | 0.813 | 0.000 | 128 |
| table | base_native | 0.975 | 1.000 | 128 |
| table | thr_page | 0.722 | 0.000 | 128 |
| table | thr_native | 0.964 | 1.000 | 128 |
| chart | thr_routed | 0.936 | 1.000 | 98 |
| chart | base_page | 0.704 | 0.000 | 98 |
| chart | base_native | 0.892 | 1.000 | 98 |
| chart | thr_page | 0.721 | 0.000 | 98 |
| chart | thr_native | 0.936 | 1.000 | 98 |
| d1_complex_tables | thr_routed | 0.912 | 1.000 | 30 |
| d1_complex_tables | base_page | 0.743 | 0.000 | 30 |
| d1_complex_tables | base_native | 0.937 | 1.000 | 30 |
| d1_complex_tables | thr_page | 0.678 | 0.000 | 30 |
| d1_complex_tables | thr_native | 0.912 | 1.000 | 30 |
| d2_hard_charts | thr_routed | 0.951 | 1.000 | 30 |
| d2_hard_charts | base_page | 0.594 | 0.000 | 30 |
| d2_hard_charts | base_native | 0.909 | 1.000 | 30 |
| d2_hard_charts | thr_page | 0.600 | 0.000 | 30 |
| d2_hard_charts | thr_native | 0.951 | 1.000 | 30 |
| d3_degraded | thr_routed | 0.863 | 1.000 | 16 |
| d3_degraded | base_page | 0.720 | 0.000 | 16 |
| d3_degraded | base_native | 0.911 | 1.000 | 16 |
| d3_degraded | thr_page | 0.653 | 0.000 | 16 |
| d3_degraded | thr_native | 0.863 | 1.000 | 16 |

Full synthetic pages through the real pipeline (table score, mean-over-tables blended with best-table):

| system | table_score | sec_per_page | pages_per_s | n |
|---|---|---|---|---|
| base_single | 0.995 | 5.878 | 0.170 | 32 |
| base_double | 0.991 | 8.488 | 0.118 | 32 |
| thr_single | 0.914 | 8.985 | 0.111 | 32 |
| thr_routed | 0.740 | 13.248 | 0.075 | 32 |
ParseBench-proxy on real enterprise PDFs (seeded stratified sample; crude proxy, small n):
not run (dataset load failed in this session)
Verifier ablation (out-of-fold, final policy):
| features | spearman | auc | refine_frac_for_90pct_gain | gain_captured_at_30pct |
|---|---|---|---|---|
| entropy | 0.591 | 0.813 | 0.876 | 0.656 |
| all (entropy+structure+render) | 0.595 | 0.814 | 0.823 | 0.634 |
| entropy+structure | 0.588 | 0.821 | 0.783 | 0.629 |
| render only | 0.390 | 0.678 | 0.916 | 0.531 |
| structure | 0.108 | 0.569 | 0.960 | 0.355 |

Pre-registered verdicts measured this run:

FAIL  E1' quality: Thrisul single-pass > base single-pass (held-out regions)
PASS  E2' efficiency: routed ≈ base double-pass quality at partial refine cost
FAIL  E1'' quality: Thrisul single-pass > base single-pass (full pages)

Text regression check (Thrisul-single vs base-single page markdown similarity): 77.2 %.

### Published reference numbers (self-reported by their authors; different protocol/hardware - context only)
- LlamaParse Agentic Plus (API): 88.8
- Anthropic Opus 5.5 (Effort High): 78.9
- oi-parser: 78.3
- Nemotron-3.5-Parse (single / double / triple pass) = Thrisul base: 72.74 / 74.92 / 75.20
- LightOnOCR-3-4B: 75.1
- LightOnOCR-3-0.8B: 74.6
- Infinity-Parser2-Pro (35B): 74.3
- Infinity-Parser2-Flash: 73.25
- MinerU2.5-Pro-2605-1.2B: 72.78
- KDL-Frontier-Parser-nano: 72.38
- Chandra-ocr-2: 70.1
- PaddleOCR-VL-1.6: 67.43
- PaddleOCR-VL-1.5: 65.95
- Nemotron-Parse-2.0: 63.91

### Leaderboard template row (fill `Official score` after the locked external ParseBench run)
| Field | Value |
|---|---|
| Model version | sandeeprdy1729/thrisul-parser (merged sha in `thrisul_config.json`) |
| Base checkpoint | nvidia/Nemotron-3.5-Parse @ 4035ffb1d8 |
| Params / trainable | 631M / 2.8M LoRA |
| Inference settings | single vs routed vs double; max_side=1664; greedy; rep-pen 1.1; max_new_tokens 3072 |
| Official score | _to fill after external locked run_ |
| Measured here | region/page/proxy tables above (T4, Transformers) |
| Date | 2026-10-30 |

## Usage
```python
import torch, sys
from huggingface_hub import snapshot_download
from transformers import AutoModel, AutoProcessor, GenerationConfig
path = snapshot_download("sandeeprdy1729/thrisul-parser")
sys.path.insert(0, path)
from postprocessing import postprocess_document          # shipped from the base model
import thrisul_pipeline as tp
model = AutoModel.from_pretrained(path, trust_remote_code=True, dtype=torch.bfloat16).cuda().eval()   # use float16 on T4
processor = AutoProcessor.from_pretrained(path, trust_remote_code=True)
gen_cfg = GenerationConfig.from_pretrained(path)
router = tp.Router.load(path + "/thrisul_router.joblib")
post = lambda text, image_size: postprocess_document(text, image_size=image_size)
out = tp.parse_page(model, processor, gen_cfg, image, post, mode="routed", router=router)   # or mode="single" / "all"
print(out["md"])
```

## Limitations (honest)
- Real-document gains beyond the proxy above are unproven; the official ParseBench/OmniDocBench/olmOCR-Bench runs are external and pending.
- The router is fitted on synthetic crops including the D1-D3 domains plus ChartNet/FinTabNet; distribution shift on unseen real layouts remains
  possible (pages where region mapping fails silently fall back to single pass - counted in `parse_page` info).
- GRPO was time-boxed on free T4s; longer reward optimization is the most promising next lever (see the notebook's backlog).
- Coordinates in the base model are content-normalized; the GT serializer's round-trip is verified in-repo (`thrisul_config.json` records the mode).
- Evaluated on a T4 with Transformers; throughput numbers do not transfer to H100/vLLM.

## License and attribution
Derivative work of NVIDIA Nemotron 3.5 Parse; use is governed by the base model's license (OpenMDW 1.1) and the third-party notices in this
repository. Training data: own synthetic renderers and the Thrisul-Doc D1-D4 corpora (this repo), ChartNet and FinTabNet.c under
CDLA-Permissive-2.0. See `LICENSE` and the notice files copied from the base repo.

	---
	license: other
	license_name: openmdw-1.1
	base_model: nvidia/Nemotron-3.5-Parse
	library_name: transformers
	pipeline_tag: image-text-to-text
	tags:
	- document-parsing
	- ocr
	- tables
	- charts
	- ground-truth-sft
	- grpo
	- verifiable-rewards
	- routed-refinement
	- thrisul
	---
	# Thrisul-Parser (v2)

	A derivative of [`nvidia/Nemotron-3.5-Parse`](https://huggingface.co/nvidia/Nemotron-3.5-Parse) (631M parameters).
	v2 replaces v1's self-distillation with a three-stage quality pipeline: (1) ground-truth SFT whose targets are *exact annotations serialized in
	the base model's own output format* (never the model's own outputs - this removes the imitation ceiling), (2) GRPO with verifiable rewards
	(reward computed from ground truth: table TEDS, chart numeric-F1, format validity, bbox IoU), and (3) verifier-routed refinement re-labelled on the
	final policy. Every P0 defect found in the v1 audit is addressed; the audit trail lives in the training notebook.

	## What is new vs the base model
	1. GT-SFT (weights). LoRA r=32 (decoder linears) trained on exact ground truth: procedurally rendered tables/charts/full pages with pixel-exact
	supervision, the Thrisul-Doc D1-D4 custom corpora (complex-table archetypes, hard charts, degradation curriculum, multi-column/financial/
	adversarial pages - all exact-GT by construction), 300 real ChartNet charts (CSV truth, CDLA-2.0), 0 FinTabNet.c financial tables (HTML
	truth, CDLA-2.0), and GT-serialized text-page replay. Targets were discovered to round-trip through the base model's own `postprocess_document`
	(mode: nemotron-parse) before training.
	Numeric tokens up-weighted (1+0.5x); inverse-sqrt length weighting; checkpoint selected on held-out structure scores, not "last".
	2. GRPO with verifiable rewards (weights). 303 GRPO groups mined from hard buckets (lambda per blueprint 7.4). Group-normalized advantages (G rollouts/prompt), k3 KL to the SFT reference, prompts
	mined from the SFT policy's hard failures. Reward = (0.4, 0.25, 0.15, 0.15, 0.05) over (table, chart, structure validity, bbox IoU, EOS).
	3. Verifier-routed refinement (inference). The parser runs once, a gradient-boosted router scores each table/chart region
	(decoder-token uncertainty + structure sanity + render-vs-crop similarity), and only regions predicted to gain are re-decoded at native
	resolution. Router labels were rebuilt on the FINAL policy (v1 fitted them on the base model - distribution shift fixed). Code ships with the
	model: `thrisul_pipeline.py` (`parse_page(..., mode="routed")`), `thrisul_router.joblib`.

	## Training data (exact-GT or permissively licensed; NO benchmark pages were used for training)
	- Procedurally rendered tables (merged cells, multi-level headers, numeric/currency/percent columns), charts (bar/hbar/line/pie/stacked), and
	full synthetic pages (title/sections/paragraphs/table/chart) with pixel-exact block annotations - generated by `thrisul_pipeline.py` itself.
	- Thrisul-Doc D1-D4 custom corpora (this repo, `thrisul_pipeline.py` v2.1, exact GT by construction, seeds disclosed in `thrisul_config.json`):
	D1 complex-table archetypes - 3-level header trees, colspan+rowspan merged cells, borderless layouts, unit rows, multi-index row labels,
	decimal look-alikes, zero/dash columns; D2 hard charts - dual-axis bar+line, log-scale, 4-series crowded legends, negative bars, 100%-stacked
	(segments sum to exactly 100.0), irregular long categories; D3 degradation curriculum - JPEG/blur/downscale/rotate/shadow/tone/occlusion/noise
	in tiers 1-3 applied to D1/D2/synthetic parents whose ground truth is inherited unchanged; D4 multi-column + financial + adversarial pages -
	2-column reading-order ground truth, financial statements with parenthesized negatives and notes, decimal-shift look-alike traps, footer-lookalike
	text. Held-out eval slices for every D domain use seed ranges disjoint from training (61M/62M/63M/64M vs 6.1M-6.4M).
	- Real charts from ChartNet (`ibm-granite/ChartNet`, CSV ground truth, CDLA-Permissive-2.0).
	- Real financial tables from FinTabNet.c (`bsmock/FinTabNet.c`, HTML ground truth, CDLA-Permissive-2.0).
	- Compute: Kaggle free tier (T4), LoRA, 1583 SFT optimizer steps + 303 GRPO groups. Targets: 6330 (~533 tokens mean).

	<!-- THRISUL-BENCH:BEGIN -->
	## Official benchmark results (thrisul-parser)

	Measured 2026-10-31 on Kaggle 2×T4 (Transformers, fp16, greedy) by this benchmark notebook. Run tag: `v1`. Coverage: ParseBench 60 pages/dimension (official total 2,078) · olmOCR-Bench 400 pages (official total = all unique bench pages) · OmniDocBench: packaged · Extra suites: IndicVisionBench=53.05217098342906, MOSCAR=pending.

	> Sampling disclosure: these runs cover a seeded stratified subset unless the config covered every page. Partial coverage is labeled — never quoted as the full-suite official score. Base-model deltas on identical pages are in `results.json`.

	### ParseBench (official evaluator, sampled)

	\| group \| score \|
	\|---\|---\|
	\| chart \| 0.206 \|
	\| table \| 0.168 \|
	\| text_formatting \| 0.037 \|
	\| text_content \| 0.491 \|
	\| text \| 0.091 \|
	\| overall_rule_avg \| 0.145 \|
	\| mean of table/chart/text \| 0.155 \|

	### olmOCR-Bench (official scorer, covered subset)

	\| category \| pass rate \|
	\|---\|---\|
	\| Overall \| 29.5 \|
	\| CI95_half_width \| 1.5 \|
	\| absent \| 61.2 \|
	\| baseline \| 98.5 \|
	\| math \| 4.9 \|
	\| order \| 14.0 \|
	\| present \| 15.1 \|
	\| table \| 18.0 \|
	\| arxiv_math \| 7.6 \|
	\| headers_footers \| 56.7 \|
	\| long_tiny_text \| 14.0 \|
	\| multi_column \| 13.5 \|
	\| old_scans \| 24.8 \|
	\| old_scans_math \| 2.8 \|
	\| table_tests \| 18.3 \|

	### IndicVisionBench OCR + other suites (our protocol, official data)

	\| suite \| metric \| n \| score \|
	\|---\|---\|---\|---\|
	\| IndicVisionBench \| ANLS-C \| 862 \| 53.05 \|

	> Metrics computed by this notebook on the official data with the IndicVisionBench paper's OCR protocol (arXiv 2511.04727, following OCRBenchv2: ANLS-C/ANLS-W/CER/WER) — not the authors' official harness; per-language breakdown lives in `results.json` (`indicvisionbench.agg`).


	### Context A — olmOCR-Bench (pass rates, higher is better)

	\| System \| Protocol \| arxiv_math \| old_scans_math \| tables \| old_scans \| headers_footers \| multi_column \| long_tiny_text \| baseline \| Overall \|
	\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|
	\| thrisul-parser v2.1 \| this run, 400-page covered subset \| 7.6 \| 2.8 \| 18.3 \| 24.8 \| 56.7 \| 13.5 \| 14.0 \| 98.5 \| 29.5 ± 1.5 \|
	\| Nemotron-3.5-Parse (base) \| this run, identical pages \| 42.6 \| 10.9 \| 85.8 \| 36.5 \| 18.3 \| 79.6 \| 69.4 \| 99.8 \| 55.4 ± 1.6 \|
	\| Chandra OCR 0.1.0* \| published, full corpus \| 82.2 \| 80.3 \| 88.0 \| 50.4 \| 90.8 \| 81.2 \| 92.3 \| 99.9 \| 83.1 ± 0.9 \|
	\| olmOCR v0.4.0 \| published, full corpus \| 83.0 \| 82.3 \| 84.9 \| 47.7 \| 96.1 \| 83.7 \| 81.9 \| 99.7 \| 82.4 ± 1.1 \|
	\| Infinity-Parser 7B* \| published, full corpus \| 84.4 \| 83.8 \| 85.0 \| 47.9 \| 88.7 \| 84.2 \| 86.4 \| 99.8 \| 82.5 \|
	\| PaddleOCR-VL* \| published, full corpus \| 85.7 \| 71.0 \| 84.1 \| 37.8 \| 97.0 \| 79.9 \| 85.7 \| 98.5 \| 80.0 ± 1.0 \|
	\| Marker 1.10.1 \| published, full corpus \| 83.8 \| 66.8 \| 72.9 \| 33.5 \| 86.6 \| 80.0 \| 85.7 \| 99.3 \| 76.1 ± 1.1 \|
	\| DeepSeek-OCR \| published, full corpus \| 77.2 \| 73.6 \| 80.2 \| 33.3 \| 96.1 \| 66.4 \| 79.4 \| 99.8 \| 75.7 ± 1.0 \|
	\| MinerU 2.5.4* \| published, full corpus \| 76.6 \| 54.6 \| 84.9 \| 33.7 \| 96.6 \| 78.2 \| 83.5 \| 93.7 \| 75.2 ± 1.1 \|
	\| Mistral OCR API \| published, full corpus \| 77.2 \| 67.5 \| 60.6 \| 29.3 \| 93.6 \| 71.3 \| 77.1 \| 99.4 \| 72.0 ± 1.1 \|
	\| Nanonets-OCR2-3B \| published, full corpus \| 75.4 \| 46.1 \| 86.8 \| 40.9 \| 32.1 \| 81.9 \| 93.0 \| 99.6 \| 69.5 ± 1.1 \|

	Published rows: [allenai/olmocr README leaderboard](https://github.com/allenai/olmocr), table dated 2025-10-21 (olmOCR v0.4.0). `` = not fully open source per that README. The two "this run" rows share one protocol and are comparable only to each other; the gap to the published rows mostly reflects the covered subset, T4-class hardware, and our candidate generation — it is not* a claim that those systems are 45+ points ahead of the base model.

	### Context B — ParseBench (published 0–100 aggregate, self-reported)

	\| System \| ParseBench (published) \|
	\|---\|---\|
	\| LlamaParse Agentic Plus (API) \| 88.8 \|
	\| LlamaParse Agentic (API) \| 83.07 \|
	\| Anthropic Opus 5.5 (Effort High) \| 78.9 \|
	\| oi-parser \| 78.3 \|
	\| LightOnOCR-3-4B \| 75.1 \|
	\| LightOnOCR-3-0.8B \| 74.6 \|
	\| Infinity-Parser2-Pro (35B) \| 74.3 \|
	\| Infinity-Parser2-Flash \| 73.25 \|
	\| Nemotron-3.5-Parse (base of this model; single/double/triple pass) \| 72.74 / 74.92 / 75.20 \|
	\| MinerU2.5-Pro-2605-1.2B \| 72.78 \|
	\| KDL-Frontier-Parser-nano \| 72.38 \|
	\| Chandra-ocr-2 \| 70.1 \|
	\| PaddleOCR-VL-1.6 \| 67.43 \|
	\| Nemotron-Parse-2.0 \| 63.91 \|
	\| Google Gemini 3 Flash (Thinking Minimal) \| 71.04 \|
	\| OpenAI GPT-5 Mini (Reasoning Medium) \| 51.52 \|
	\| thrisul-parser v2.1 \| sampled official-protocol run: 0.145 overall_rule_avg / 0.155 mean-of-3 (different aggregation — not the 0–100 leaderboard scale); official full-corpus run pending \|

	Sources: parsebench.ai leaderboard (self-reported), as captured in this repo's `results.json` (`published_parsebench`). The base model ranks #1 among sub-1B parsers on that leaderboard.

	### Context C — the wider parser landscape (qualitative)

	\| Parser \| Access \| Scale \| License \| Notes \|
	\|---\|---\|---\|---\|---\|
	\| thrisul-parser v2.1 \| open weights \| 631M (+2.8M LoRA) \| OpenMDW 1.1 (via base) \| GT-SFT + GRPO + verifier-routed refinement; this repo \|
	\| Nemotron-3.5-Parse (base) \| open weights \| 631M \| OpenMDW 1.1 \| bbox + classes + markdown output; #1 sub-1B on ParseBench \|
	\| Nougat \| open weights \| ~250M–350M \| MIT \| academic PDFs; LaTeX output; no layout coordinates \|
	\| GOT-OCR 2.0 \| open weights \| ~580M \| MIT \| "general OCR theory"; unified formulas/tables/text \|
	\| olmOCR \| open weights \| ~7B \| Apache-2.0 \| PDF → plain markdown; strong on olmOCR-Bench by construction \|
	\| Marker \| open weights \| pipeline+DL \| GPL-3.0 \| fast PDF→markdown; commercial use restricted \|
	\| MinerU 2.5 \| open weights \| 1.2B (Pro) \| AGPL-3.0 \| layout+recognition pipeline; strong OmniDocBench scores \|
	\| Docling \| open toolkit \| — \| MIT \| IBM; layout models + table structure; heavy CPU-friendly pipeline \|
	\| Surya \| open weights \| — \| GPL-3.0 \| OCR/layout/reading-order toolkit (Datalab) \|
	\| DeepSeek-OCR \| open weights \| ~3B \| MIT \| "contexts optical compression"; long-doc focus \|
	\| PaddleOCR-VL \| open weights \| 0.9B \| Apache-2.0 \| strong olmOCR-Bench full-corpus score \|
	\| Chandra \| open weights \| — \| see repo¹ \| Datalab; top published olmOCR-Bench row above \|
	\| Mistral OCR \| API only \| — \| commercial \| strong tables; per-page pricing \|
	\| LlamaParse \| API only \| — \| commercial \| multiple modes; ParseBench head rows above \|
	- Google Gemini 3 Flash (Thinking Minimal): 71.04
	-
	<!-- THRISUL-BENCH:END -->

	## Results (read the caveats)
	All numbers below were measured by us on held-out synthetic data and a seeded ParseBench-proxy sample on a T4 with Transformers (no vLLM).
	They are not the official ParseBench score and are not comparable to H100/vLLM numbers anywhere below. Headline deltas carry bootstrap 95% CIs.

	Held-out synthetic regions (page-scale input; `routed` re-decodes only router-selected regions):

	\| subset \| system \| score \| refine_frac \| n \|
	\|---\|---\|---\|---\|---\|
	\| all \| thr_routed \| 0.952 \| 1.000 \| 226 \|
	\| all \| base_page \| 0.766 \| 0.000 \| 226 \|
	\| all \| base_native \| 0.939 \| 1.000 \| 226 \|
	\| all \| thr_page \| 0.722 \| 0.000 \| 226 \|
	\| all \| thr_native \| 0.952 \| 1.000 \| 226 \|
	\| table \| thr_routed \| 0.964 \| 1.000 \| 128 \|
	\| table \| base_page \| 0.813 \| 0.000 \| 128 \|
	\| table \| base_native \| 0.975 \| 1.000 \| 128 \|
	\| table \| thr_page \| 0.722 \| 0.000 \| 128 \|
	\| table \| thr_native \| 0.964 \| 1.000 \| 128 \|
	\| chart \| thr_routed \| 0.936 \| 1.000 \| 98 \|
	\| chart \| base_page \| 0.704 \| 0.000 \| 98 \|
	\| chart \| base_native \| 0.892 \| 1.000 \| 98 \|
	\| chart \| thr_page \| 0.721 \| 0.000 \| 98 \|
	\| chart \| thr_native \| 0.936 \| 1.000 \| 98 \|
	\| d1_complex_tables \| thr_routed \| 0.912 \| 1.000 \| 30 \|
	\| d1_complex_tables \| base_page \| 0.743 \| 0.000 \| 30 \|
	\| d1_complex_tables \| base_native \| 0.937 \| 1.000 \| 30 \|
	\| d1_complex_tables \| thr_page \| 0.678 \| 0.000 \| 30 \|
	\| d1_complex_tables \| thr_native \| 0.912 \| 1.000 \| 30 \|
	\| d2_hard_charts \| thr_routed \| 0.951 \| 1.000 \| 30 \|
	\| d2_hard_charts \| base_page \| 0.594 \| 0.000 \| 30 \|
	\| d2_hard_charts \| base_native \| 0.909 \| 1.000 \| 30 \|
	\| d2_hard_charts \| thr_page \| 0.600 \| 0.000 \| 30 \|
	\| d2_hard_charts \| thr_native \| 0.951 \| 1.000 \| 30 \|
	\| d3_degraded \| thr_routed \| 0.863 \| 1.000 \| 16 \|
	\| d3_degraded \| base_page \| 0.720 \| 0.000 \| 16 \|
	\| d3_degraded \| base_native \| 0.911 \| 1.000 \| 16 \|
	\| d3_degraded \| thr_page \| 0.653 \| 0.000 \| 16 \|
	\| d3_degraded \| thr_native \| 0.863 \| 1.000 \| 16 \|

	Full synthetic pages through the real pipeline (table score, mean-over-tables blended with best-table):

	\| system \| table_score \| sec_per_page \| pages_per_s \| n \|
	\|---\|---\|---\|---\|---\|
	\| base_single \| 0.995 \| 5.878 \| 0.170 \| 32 \|
	\| base_double \| 0.991 \| 8.488 \| 0.118 \| 32 \|
	\| thr_single \| 0.914 \| 8.985 \| 0.111 \| 32 \|
	\| thr_routed \| 0.740 \| 13.248 \| 0.075 \| 32 \|

	ParseBench-proxy on real enterprise PDFs (seeded stratified sample; crude proxy, small n):

	not run (dataset load failed in this session)

	Verifier ablation (out-of-fold, final policy):

	\| features \| spearman \| auc \| refine_frac_for_90pct_gain \| gain_captured_at_30pct \|
	\|---\|---\|---\|---\|---\|
	\| entropy \| 0.591 \| 0.813 \| 0.876 \| 0.656 \|
	\| all (entropy+structure+render) \| 0.595 \| 0.814 \| 0.823 \| 0.634 \|
	\| entropy+structure \| 0.588 \| 0.821 \| 0.783 \| 0.629 \|
	\| render only \| 0.390 \| 0.678 \| 0.916 \| 0.531 \|
	\| structure \| 0.108 \| 0.569 \| 0.960 \| 0.355 \|

	Pre-registered verdicts measured this run:

	FAIL E1' quality: Thrisul single-pass > base single-pass (held-out regions)
	PASS E2' efficiency: routed ≈ base double-pass quality at partial refine cost
	FAIL E1'' quality: Thrisul single-pass > base single-pass (full pages)

	Text regression check (Thrisul-single vs base-single page markdown similarity): 77.2 %.

	### Published reference numbers (self-reported by their authors; different protocol/hardware - context only)
	- LlamaParse Agentic Plus (API): 88.8
	- Anthropic Opus 5.5 (Effort High): 78.9
	- oi-parser: 78.3
	- Nemotron-3.5-Parse (single / double / triple pass) = Thrisul base: 72.74 / 74.92 / 75.20
	- LightOnOCR-3-4B: 75.1
	- LightOnOCR-3-0.8B: 74.6
	- Infinity-Parser2-Pro (35B): 74.3
	- Infinity-Parser2-Flash: 73.25
	- MinerU2.5-Pro-2605-1.2B: 72.78
	- KDL-Frontier-Parser-nano: 72.38
	- Chandra-ocr-2: 70.1
	- PaddleOCR-VL-1.6: 67.43
	- PaddleOCR-VL-1.5: 65.95
	- Nemotron-Parse-2.0: 63.91

	### Leaderboard template row (fill `Official score` after the locked external ParseBench run)
	\| Field \| Value \|
	\|---\|---\|
	\| Model version \| sandeeprdy1729/thrisul-parser (merged sha in `thrisul_config.json`) \|
	\| Base checkpoint \| nvidia/Nemotron-3.5-Parse @ 4035ffb1d8 \|
	\| Params / trainable \| 631M / 2.8M LoRA \|
	\| Inference settings \| single vs routed vs double; max_side=1664; greedy; rep-pen 1.1; max_new_tokens 3072 \|
	\| Official score \| _to fill after external locked run_ \|
	\| Measured here \| region/page/proxy tables above (T4, Transformers) \|
	\| Date \| 2026-10-30 \|

	## Usage
	```python
	import torch, sys
	from huggingface_hub import snapshot_download
	from transformers import AutoModel, AutoProcessor, GenerationConfig
	path = snapshot_download("sandeeprdy1729/thrisul-parser")
	sys.path.insert(0, path)
	from postprocessing import postprocess_document # shipped from the base model
	import thrisul_pipeline as tp
	model = AutoModel.from_pretrained(path, trust_remote_code=True, dtype=torch.bfloat16).cuda().eval() # use float16 on T4
	processor = AutoProcessor.from_pretrained(path, trust_remote_code=True)
	gen_cfg = GenerationConfig.from_pretrained(path)
	router = tp.Router.load(path + "/thrisul_router.joblib")
	post = lambda text, image_size: postprocess_document(text, image_size=image_size)
	out = tp.parse_page(model, processor, gen_cfg, image, post, mode="routed", router=router) # or mode="single" / "all"
	print(out["md"])
	```

	## Limitations (honest)
	- Real-document gains beyond the proxy above are unproven; the official ParseBench/OmniDocBench/olmOCR-Bench runs are external and pending.
	- The router is fitted on synthetic crops including the D1-D3 domains plus ChartNet/FinTabNet; distribution shift on unseen real layouts remains
	possible (pages where region mapping fails silently fall back to single pass - counted in `parse_page` info).
	- GRPO was time-boxed on free T4s; longer reward optimization is the most promising next lever (see the notebook's backlog).
	- Coordinates in the base model are content-normalized; the GT serializer's round-trip is verified in-repo (`thrisul_config.json` records the mode).
	- Evaluated on a T4 with Transformers; throughput numbers do not transfer to H100/vLLM.

	## License and attribution
	Derivative work of NVIDIA Nemotron 3.5 Parse; use is governed by the base model's license (OpenMDW 1.1) and the third-party notices in this
	repository. Training data: own synthetic renderers and the Thrisul-Doc D1-D4 corpora (this repo), ChartNet and FinTabNet.c under
	CDLA-Permissive-2.0. See `LICENSE` and the notice files copied from the base repo.