Image-Text-to-Text
Transformers
Joblib
Safetensors
nemotron_parse
text-generation
document-parsing
ocr
tables
charts
ground-truth-sft
grpo
verifiable-rewards
routed-refinement
thrisul
conversational
custom_code
Instructions to use sandeeprdy1729/thrisul-parser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 10,003 Bytes
37fb2b9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 | # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: OpenMDW-1.1
import csv
import io
import json
from bs4 import BeautifulSoup
import pytest
from nemotron_parse import PostprocessOptions, postprocess_document, render_markdown
from nemotron_parse.postprocessing import (
extract_classes_bboxes,
transform_bbox_to_original,
postprocess_text,
convert_mmd_to_plain_text_ours,
)
from nemotron_parse import tables
def block(text, cls="Text", box=(0.1, 0.1, 0.9, 0.9)):
return f"<x_{box[0]}><y_{box[1]}>{text}<x_{box[2]}><y_{box[3]}><class_{cls}>"
def tabular(*rows, cols="cc"):
return (
"\\begin{tabular}{"
+ cols
+ "}\n"
+ "\n".join(row + r" \\" for row in rows)
+ "\n\\end{tabular}"
)
def test_multiline_cell_and_trailing_empty_preserved():
text = tabular(r"\thead{Name} & \thead{Note}", r"A & first\newline second", "B & ")
html = postprocess_text(text, "Table", table_format="HTML")
soup = BeautifulSoup(html, "html.parser")
rows = soup.find_all("tr")
assert len(rows) == 3
assert [len(row.find_all(["th", "td"])) for row in rows] == [2, 2, 2]
assert rows[1].find_all("td")[1].find("br")
assert rows[2].find_all("td")[1].get_text() == ""
def test_rich_inline_and_literal_escapes():
raw = tabular(
r"\thead{Item} & \thead{Note}",
r"A & \textstrong{Bold} \textsuperscript{2} \# \textasciitilde{} \textasciicircum{}",
)
html = postprocess_text(raw, "Table", table_format="HTML")
assert "<b>Bold</b>" in html and "<sup>2</sup>" in html
assert "# ~ ^" in html
assert r"\thead" not in html
def test_data_rows_not_promoted_to_headers():
raw = tabular("A & 10", "B & 20")
html = postprocess_text(raw, "Table", table_format="HTML")
assert "<th" not in html
records = json.loads(postprocess_text(raw, "Table", table_format="json"))
assert [list(row.values()) for row in records] == [["A", "10"], ["B", "20"]]
assert records[0] == {"column_0": "A", "column_1": "10"}
def test_bold_header_inference_configurable():
raw = tabular("**Name** & **Value**", "A & 10")
assert "<th" in postprocess_text(raw, "Table", table_format="HTML")
assert "<th" not in postprocess_text(
raw,
"Table",
table_format="HTML",
options=PostprocessOptions(infer_headers=False),
)
def test_spans_and_hierarchical_export():
raw = tabular(r"\thead{\multirow{2}{*}{Region}} & \thead{Value}", r" & A", "B & 10")
# Use the supported outer-span/inner-header native syntax.
raw = raw.replace(
r"\thead{\multirow{2}{*}{Region}}", r"\multirow{2}{*}{\thead{Region}}"
)
html = postprocess_text(
raw,
"Table",
table_format="HTML",
options=PostprocessOptions(infer_headers=False),
)
soup = BeautifulSoup(html, "html.parser")
assert soup.find("th")["rowspan"] == "2"
obj = json.loads(tables.convert_html_table_to_json_hierarchical(html))
assert obj["headers"][0][0]["rowspan"] == 2
assert len(obj["data"]) == 2
def test_rowspan_grid_does_not_drop_later_columns():
html = '<table><tr><td rowspan="2">A</td></tr><tr><td>B</td><td>C</td></tr></table>'
records = json.loads(tables.convert_html_table_to_json(html))
assert list(records[1].values()) == ["A", "B", "C"]
@pytest.mark.parametrize(
"names",
[["A", "A", "A_1"], ["column", "", "column_1", ""], ["x", "x_1", "x", "x_2", "x"]],
)
def test_collision_free_json_names(names):
html = "<table><tr>" + "".join("<th>" + n + "</th>" for n in names) + "</tr><tr>"
values = list(map(str, range(len(names))))
html += "".join("<td>" + v + "</td>" for v in values) + "</tr></table>"
record = json.loads(tables.convert_html_table_to_json(html))[0]
assert len(record) == len(names)
assert list(record.values()) == values
def test_csv_and_markdown_keep_headerless_first_row():
raw = tabular("A & 10", "B & 20")
rows = list(
csv.reader(io.StringIO(postprocess_text(raw, "Table", table_format="csv")))
)
assert rows == [["column_0", "column_1"], ["A", "10"], ["B", "20"]]
md = postprocess_text(raw, "Table", table_format="markdown")
assert "| A | 10 |" in md and "| B | 20 |" in md
@pytest.mark.parametrize(
"source, expected",
[
("<sup>2</sup> and <sub>i</sub>", "^{2} and _{i}"),
("C# developer", "C# developer"),
("x * y * z", "x * y * z"),
("# Heading\nC# developer", "Heading\nC# developer"),
("`C# **code**` and $x_1 * y$", "`C# **code**` and $x_1 * y$"),
],
)
def test_plain_text(source, expected):
assert convert_mmd_to_plain_text_ours(source) == expected
def test_prefix_repair_and_exact_duplicate_cleanup():
raw = "<x_0.01><y_0.01><class_Title>" + block("Hello") * 2
result = postprocess_document(raw)
assert result["markdown"] == "Hello" and len(result["blocks"]) == 1
assert "<class_Title>" not in result["blocks"][0]["text"]
def test_distinct_coordinates_or_code_whitespace_not_deduplicated():
raw = block("Hello") + block("Hello", box=(0.1, 0.2, 0.9, 1.0))
assert len(postprocess_document(raw)["blocks"]) == 2
raw = block("a b", "Code") + block("a b", "Code")
assert len(postprocess_document(raw)["blocks"]) == 2
def test_page_number_and_formula_preserved():
result = postprocess_document(
block("1", "Page-number") + block("$a+b$", "Inline-formula")
)
assert [b["class"] for b in result["blocks"]] == ["Page-number", "Inline-formula"]
assert "1" in result["markdown"] and "$a+b$" in result["markdown"]
def test_text_cleanup_protects_code_and_math():
assert postprocess_text("inter\u00adnational<tbc>") == "international"
assert postprocess_text("• item", "List-item") == "- item"
assert postprocess_text("a\u00adb", "Code") == "a\u00adb"
assert postprocess_text("$a\u00adb$") == "$a\u00adb$"
@pytest.mark.parametrize(
"box", [(float("nan"), 0, 1, 1), (0, 0, float("inf"), 1), (0, 0, 1)]
)
def test_invalid_boxes_rejected(box):
with pytest.raises(ValueError):
transform_bbox_to_original(box, 100, 100)
def test_reversed_and_clipped_boxes():
assert transform_bbox_to_original((0.9, 1.2, -0.1, 0.2), 100, 200) == (
0.0,
40.0,
90.0,
200.0,
)
def test_chart_presentation_controls():
chart = "<chart>\ntitle: Sales\nsubtitle: FY2026\n| Item | Revenue |\n| --- | --- |\n| A | 32 (16.6%) |\n| B | 48 (25%) |\n</chart>"
raw = block(chart, "Chart")
ordinary = render_markdown(
raw, options=PostprocessOptions(split_chart_value_percent=False)
)
assert "Sales — FY2026" in ordinary and "32 (16.6%)" in ordinary
assert ">Percent<" not in ordinary
split = render_markdown(
raw, options=PostprocessOptions(split_chart_value_percent=True)
)
assert ">Percent<" in split and "<td>32</td>" in split and "<td>16.6%</td>" in split
assert (
render_markdown(raw, options=PostprocessOptions(render_charts=False)) == chart
)
def test_empty_chart_title_does_not_consume_metadata():
chart = (
"<chart>\ntitle:\nsubtitle: Keep\n| A | B |\n| --- | --- |\n| x | 1 |\n</chart>"
)
assert "subtitle: Keep" in render_markdown(block(chart, "Chart"))
def test_headings_and_original_text_exposed():
document = postprocess_document(
block("Title", "Title") + block("Section", "Section-header"),
image_size=(100, 200),
)
assert document["markdown"] == "# Title\n\n## Section"
assert document["blocks"][0]["text"] == "Title"
assert document["blocks"][0]["bbox_pixels"] == [10.0, 20.0, 90.0, 180.0]
def test_existing_top_level_imports():
import postprocessing
import latex2html
assert postprocessing.postprocess_text("text") == "text"
assert callable(latex2html.convert_html_table_to_json_hierarchical)
def test_unknown_class_is_not_dropped():
assert render_markdown(block("Future content", "Future-role")) == "Future content"
def test_presentation_caption_relocation_is_opt_in():
raw = block("Sales figure", "Caption", (0.1, 0.05, 0.9, 0.09)) + block(
"<chart>\n| Item | Value |\n| --- | --- |\n| A | 1 |\n</chart>",
"Chart",
(0.1, 0.1, 0.9, 0.9),
)
default = render_markdown(
raw, options=PostprocessOptions(relocate_chart_captions=False)
)
assert default.startswith("Sales figure\n\n")
moved = render_markdown(
raw, options=PostprocessOptions(relocate_chart_captions=True)
)
assert "<caption>Sales figure</caption>" in moved
assert moved.count("Sales figure") == 1
def test_body_spans_exposed_in_hierarchical_json():
html = (
'<table><tr><th>A</th><th>B</th></tr><tr><td colspan="2">Both</td></tr></table>'
)
obj = json.loads(tables.convert_html_table_to_json_hierarchical(html))
assert obj["body_cells"][0][0]["colspan"] == 2
assert obj["data"] == [["Both"]]
def test_malformed_table_is_preserved_instead_of_lossy_fallback():
raw = tabular(r"\multirow{2}{*}{\thead{missing brace} & x", " & y")
result = render_markdown(block(raw, "Table"))
assert raw in result
def test_document_preserves_malformed_table_and_reports_warning():
raw = tabular(r"\multirow{2}{*}{\thead{missing brace} & x", " & y")
document = postprocess_document(block(raw, "Table"))
assert document["blocks"][0]["content"] == raw
assert document["warnings"][0]["block_index"] == 0
@pytest.mark.parametrize("role", ["Code", "Formula"])
def test_literal_tabular_in_code_or_formula_is_not_converted(role):
text = tabular("A & 10", "B & 20")
assert render_markdown(block(text, role)) == text
def test_prose_discussing_tabular_is_not_reinterpreted_as_table():
text = "The LaTeX example uses " + tabular("A & 10", "B & 20") + " in its source."
assert render_markdown(block(text, "Text")) == text
|