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: 8,430 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 | # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: OpenMDW-1.1
"""Prediction-only document rendering; no benchmark data or scorer dependencies."""
from dataclasses import dataclass
import math
import re
from . import tables
from .engine import markdown as adapter, policies
from .engine.header_inference_probe import infer_header_rows
from .engine.text_cleanup import (
normalize_text,
repair_empty_prefix,
dedupe_exact_elements,
)
from .engine.chart_caption import transform as chart_captions
from .engine.chart_context import add_native_subtitles
from .engine.caption_relocation import relocate_above_captions
@dataclass(frozen=True)
class PostprocessOptions:
"""Structural cleanup is shared; presentation heuristics can be disabled.
Numeric pair splitting adds Value/Percent column labels and is enabled by default.
Caption association is enabled by default; either presentation rule can be disabled.
"""
infer_headers: bool = True
render_charts: bool = True
split_chart_value_percent: bool = True
relocate_chart_captions: bool = True
normalize_text: bool = True
deduplicate: bool = True
def clean_raw_output(raw, *, deduplicate=True):
raw = repair_empty_prefix(raw or "")
return dedupe_exact_elements(raw) if deduplicate else raw
def extract_classes_bboxes(text):
"""Extract normalized boxes and literal bodies, keeping predicted classes."""
return adapter.extract_classes_bboxes(clean_raw_output(text))
def transform_bbox_to_original(
bbox,
original_width,
original_height,
target_w=1664,
target_h=2048,
padded_width=None,
padded_height=None,
pad_left=0,
pad_top=0,
):
"""Scale content-normalized coordinates; padding arguments are legacy-only."""
if original_width <= 0 or original_height <= 0:
raise ValueError("Image dimensions must be positive")
values = tuple(float(value) for value in bbox)
if len(values) != 4 or not all(math.isfinite(value) for value in values):
raise ValueError("Expected four finite bbox coordinates")
x1, x2 = sorted((values[0], values[2]))
y1, y2 = sorted((values[1], values[3]))
return tuple(
min(size, max(0.0, value * size))
for value, size in zip((x1, y1, x2, y2), (original_width, original_height) * 2)
)
def remove_nemotron_formatting(text):
return text.replace("<tbc>", "").replace(r"\<|unk|\>", "").replace(r"\unknown", "")
def convert_mmd_to_plain_text_ours(text):
"""Remove simple markup while protecting code, math, and literal hashes."""
protected = []
def protect(match):
protected.append(match[0])
return f"\x00{len(protected)-1}\x00"
text = re.sub(
r"```.*?```|`[^`\n]*`|\$\$.*?\$\$|(?<!\\)\$[^$\n]+\$|\\\(.*?\\\)|\\\[.*?\\\]",
protect,
text,
flags=re.S,
)
text = re.sub(r"<sup>(.*?)</sup>", lambda m: "^{" + m[1] + "}", text, flags=re.S)
text = re.sub(r"<sub>(.*?)</sub>", lambda m: "_{" + m[1] + "}", text, flags=re.S)
text = re.sub(r"<br\s*/?>", "\n", text, flags=re.I)
text = re.sub(r"(?m)^ {0,3}#{1,6}[ \t]+", "", text)
text = re.sub(r"\*\*(\S(?:[^\n]*?\S)?)\*\*", r"\1", text)
text = re.sub(r"(?<!\w)\*(\S(?:[^*\n]*?\S)?)\*(?!\w)", r"\1", text)
text = re.sub(r"(?<!\w)_(\S(?:[^_\n]*?\S)?)_(?!\w)", r"\1", text)
return re.sub(r"\x00(\d+)\x00", lambda m: protected[int(m[1])], text).strip()
def _table_html(text, options):
try:
text = tables.latex_table_to_html(text)
except (ValueError, IndexError):
return text # Preserve malformed source rather than inventing a table.
if options.infer_headers:
text = re.sub(
r"<table\b[^>]*>.*?</table>",
lambda m: infer_header_rows(m[0])[0],
text,
flags=re.S | re.I,
)
text = policies.promote_bold_header_row(text)
return text
def postprocess_text(
text,
cls="Text",
text_format="markdown",
table_format="latex",
blank_text_in_figures=False,
*,
options=None,
):
"""Process one block, retaining all previously supported export formats."""
options = options or PostprocessOptions()
if text_format not in ("markdown", "plain"):
raise ValueError("text_format must be markdown or plain")
formats = ("latex", "HTML", "markdown", "json", "json_hierarchical", "csv")
if table_format not in formats:
raise ValueError(f"table_format must be one of {formats}")
text = remove_nemotron_formatting(text)
if options.normalize_text:
text = normalize_text(cls, text)
if blank_text_in_figures and cls == "Picture":
return ""
if cls == "Table":
if table_format == "latex":
return text
html = _table_html(text, options)
converters = {
"HTML": lambda s: s,
"markdown": tables.convert_html_tables_to_markdown,
"json": tables.convert_html_table_to_json,
"json_hierarchical": tables.convert_html_table_to_json_hierarchical,
"csv": tables.convert_html_table_to_csv,
}
return converters[table_format](html)
if cls == "Chart" and options.render_charts:
text = render_markdown(
"<x_0><y_0>" + text + "<x_1><y_1><class_Chart>", options=options
)
return convert_mmd_to_plain_text_ours(text) if text_format == "plain" else text
def render_markdown(raw, *, options=None):
"""Render a page, preserving predicted block order and chart context."""
options = options or PostprocessOptions()
raw = clean_raw_output(raw, deduplicate=options.deduplicate)
def base(value):
if options.render_charts:
value = policies.chart_render(
value, panels=True, compound=options.split_chart_value_percent
)
def chart(match):
if match[4] != "Chart":
return match[0]
rendered = adapter.mmd_to_markdown(match[0])
rendered, count = chart_captions(rendered)
return match[1] + rendered + match[3] if count else match[0]
value = policies.BLOCK.sub(chart, value)
value = adapter.mmd_to_markdown(
value, text_transform=normalize_text if options.normalize_text else None
)
if options.infer_headers:
value = re.sub(
r"<table\b[^>]*>.*?</table>",
lambda m: infer_header_rows(m[0])[0],
value,
flags=re.S | re.I,
)
value = policies.promote_bold_header_row(value)
return value
text = base(raw)
if options.render_charts:
if options.relocate_chart_captions:
text = relocate_above_captions(raw, text, base)
text = add_native_subtitles(text)
return text
def postprocess_document(raw, *, image_size=None, options=None):
"""Return JSON-serializable Markdown, normalized blocks, and cleaned raw text.
Blocks preserve their model order and original per-block content; rendered
content is provided separately. Pixel boxes are added when image_size=(w,h).
"""
options = options or PostprocessOptions()
cleaned = clean_raw_output(raw, deduplicate=options.deduplicate)
classes, boxes, texts = adapter.extract_classes_bboxes(cleaned)
blocks = []
for cls, bbox, text in zip(classes, boxes, texts):
block = {
"class": cls,
"bbox": list(bbox),
"text": text,
"content": postprocess_text(
text, cls, table_format="HTML", options=options
),
}
if image_size is not None:
block["bbox_pixels"] = list(transform_bbox_to_original(bbox, *image_size))
blocks.append(block)
return {
"raw_out": cleaned,
"markdown": render_markdown(cleaned, options=options),
"blocks": blocks,
"warnings": [
{
"block_index": index,
"message": "Table markup could not be fully converted; original content preserved.",
}
for index, block in enumerate(blocks)
if block["class"] == "Table" and r"\begin{tabular}" in block["content"]
],
"coordinate_frame": "content_normalized",
}
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