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: 4,874 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 | # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: OpenMDW-1.1
"""Strict rich_tabular_v3 reverse reader. Other LaTeX uses the legacy parser."""
import html, re
TAGS = {
"textstrong": "b",
"textem": "i",
"textbf": "b",
"textit": "i",
"underline": "u",
"sout": "s",
"textsuperscript": "sup",
"textsubscript": "sub",
}
LITERALS = {
"textbackslash": "\\",
"textasciitilde": "~",
"textasciicircum": "^",
"textless": "<",
"textgreater": ">",
}
def split_top(text, delimiter):
parts = []
start = 0
depth = 0
i = 0
while i < len(text):
if depth == 0 and text.startswith(delimiter, i):
parts.append(text[start:i])
i += len(delimiter)
start = i
continue
if text[i] == "\\":
i += 2
continue
if text[i] == "{":
depth += 1
elif text[i] == "}":
depth -= 1
if depth < 0:
raise ValueError("unbalanced_generated_latex")
i += 1
if depth:
raise ValueError("unbalanced_generated_latex")
parts.append(text[start:])
return parts
def argument(text, pos):
if pos >= len(text) or text[pos] != "{":
raise ValueError("expected_braced_argument")
start = pos + 1
depth = 1
pos += 1
while pos < len(text):
if text[pos] == "\\":
pos += 2
continue
if text[pos] == "{":
depth += 1
elif text[pos] == "}":
depth -= 1
if depth == 0:
return text[start:pos], pos + 1
pos += 1
raise ValueError("unbalanced_generated_latex")
def inline_html(text):
try:
from .hybrid_inline import inline_html as decode
except ImportError:
from hybrid_inline import inline_html as decode
return decode(text)
def cell_parts(text):
text = text.strip()
rs = cs = 1
while text.startswith((r"\multicolumn", r"\multirow")):
command = "multicolumn" if text.startswith(r"\multicolumn") else "multirow"
pos = len(command) + 1
count, pos = argument(text, pos)
_, pos = argument(text, pos)
body, pos = argument(text, pos)
if text[pos:].strip():
raise ValueError("trailing_span_content")
if command == "multicolumn":
cs = int(count)
else:
rs = int(count)
text = body
header = False
if text.startswith(r"\thead{"):
body, end = argument(text, len(r"\thead"))
if text[end:].strip():
raise ValueError("trailing_header_content")
text = body
header = True
return rs, cs, inline_html(text), header
def to_html(latex):
m = re.fullmatch(
r"\\begin\{tabular\}\{[clrx]+\}\n(.*?)\n\\end\{tabular\}", latex, re.S
)
if not m:
raise ValueError("unsupported_generated_tabular")
rows = split_top(m.group(1), r"\\")
if rows[-1].strip():
raise ValueError("missing_final_row_separator")
columns = len(re.match(r"\\begin\{tabular\}\{([clrx]+)\}", latex).group(1))
rows.pop()
occupied = set()
out = ["<table>"]
if not rows or len(rows) > 4096 or columns > 512:
raise ValueError("generated_grid_size")
for r, row in enumerate(rows):
out.append("<tr>")
c = 0
for part in split_top(row, "&"):
rs, cs, content, header = cell_parts(part)
if rs < 1 or cs < 1 or r + rs > len(rows) or c + cs > columns:
raise ValueError("invalid_generated_span")
if (r, c) in occupied:
if content or header or rs != 1:
raise ValueError("nonempty_span_placeholder")
if any((r, cc) not in occupied for cc in range(c, c + cs)):
raise ValueError("invalid_span_placeholder")
c += cs
continue
if any(
(rr, cc) in occupied
for rr in range(r, r + rs)
for cc in range(c, c + cs)
):
raise ValueError("overlapping_generated_span")
tag = "th" if header else "td"
out.append(
"<"
+ tag
+ ' rowspan="'
+ str(rs)
+ '" colspan="'
+ str(cs)
+ '">'
+ content
+ "</"
+ tag
+ ">"
)
for rr in range(r, r + rs):
for cc in range(c, c + cs):
occupied.add((rr, cc))
c += cs
if c != columns:
raise ValueError("generated_column_count")
out.append("</tr>")
return "".join(out) + "</table>"
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