Image-Text-to-Text
Transformers
Safetensors
English
Chinese
internvl_chat
feature-extraction
visual-language
paddleocr
document-parse
HPD-Parsing
speculative-decoding
P-MTP
eval results
conversational
custom_code
Instructions to use PaddlePaddle/HPD-Parsing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PaddlePaddle/HPD-Parsing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="PaddlePaddle/HPD-Parsing", 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)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("PaddlePaddle/HPD-Parsing", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use PaddlePaddle/HPD-Parsing with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PaddlePaddle/HPD-Parsing" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PaddlePaddle/HPD-Parsing", "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/PaddlePaddle/HPD-Parsing
- SGLang
How to use PaddlePaddle/HPD-Parsing 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 "PaddlePaddle/HPD-Parsing" \ --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": "PaddlePaddle/HPD-Parsing", "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 "PaddlePaddle/HPD-Parsing" \ --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": "PaddlePaddle/HPD-Parsing", "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 PaddlePaddle/HPD-Parsing with Docker Model Runner:
docker model run hf.co/PaddlePaddle/HPD-Parsing
File size: 8,368 Bytes
7325252 | 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 | """Convert HPD-Parsing predictions (JSON) into per-page markdown for OmniDocBench.
Input : JSON, a list of ``{img_path, pred}`` (pred is the ``<BLOCK> <type> [bbox]
<CHILD> <content>`` stream from ``document parsing with fork.``).
Output: a folder of ``<image_stem>.md`` files matching the OmniDocBench GT paths.
python hpd_to_markdown.py --input preds.json --out-md pred_md/ \
--simplify-left-paren --clean-formula-tail --norm-formula-flag --wrap-cjk-arith
"""
import argparse
import json
import os
import re
from pathlib import Path
_TALL = re.compile(
r'\\d?frac|\\tfrac|\\cfrac|\\binom|\\sqrt'
r'|\\sum|\\prod|\\coprod|\\int|\\iint|\\iiint|\\oint'
r'|\\bigcup|\\bigcap|\\bigoplus|\\bigotimes|\\bigsqcup'
r'|\\begin\{'
r'|\\overbrace|\\underbrace|\\overset|\\underset|\\stackrel'
r'|\\substack|\\atop|\\\\'
)
def _scan_delims(s):
out = []
for m in re.finditer(r'\\(left|right)\s*', s):
dm = re.match(r'\\[a-zA-Z]+|\\.|.', s[m.end():])
if not dm:
continue
out.append({'kind': m.group(1), 'delim': dm.group(0),
'start': m.start(), 'end': m.end() + dm.end()})
return out
def simplify_left_right(s: str) -> str:
"""Downgrade `\\left( ... \\right)` with no tall inner structure to plain `( )`."""
if '\\left' not in s:
return s
stack, pairs = [], []
for d in _scan_delims(s):
if d['kind'] == 'left':
stack.append(d)
elif stack:
pairs.append((stack.pop(), d))
edits = []
for L, R in pairs:
if L['delim'] == '(' and R['delim'] == ')' and not _TALL.search(s[L['end']:R['start']]):
edits.append((L['start'], L['end'], '('))
edits.append((R['start'], R['end'], ')'))
for st, en, rep in sorted(edits, key=lambda x: x[0], reverse=True):
s = s[:st] + rep + s[en:]
return s
_ELLIPSIS = r'(?:\\dots|\\cdots|\\ldots|\\dotsb|\\dotsc)'
_CLOSER = r'(?:\\right\s*[.\}\]\)]|\\end\s*\{(?:array|matrix|cases|bmatrix|pmatrix|vmatrix|smallmatrix)\})'
_TAIL_WRAP = re.compile(r'^(?P<core>.*?)(?P<wrap>\s*(?:\\\]|\\\)|\$\$))?\s*$', re.DOTALL)
def clean_formula_tail(s: str) -> str:
"""Strip degenerate formula tails (repeated/dangling ellipses, stray `\\quad`)."""
if not s:
return s
m = _TAIL_WRAP.match(s)
core, wrap = m.group('core'), m.group('wrap') or ''
prev = None
while prev != core:
prev = core
core = re.sub(r'(' + _ELLIPSIS + r')(?:\s*' + _ELLIPSIS + r')+', r'\1', core)
core = re.sub(r'(?P<keep>' + _CLOSER + r')\s*(?:\\q?quad\s*)*' + _ELLIPSIS + r'\s*$',
lambda mm: mm.group('keep'), core)
core = re.sub(r'(?:\s*\\q?quad)+\s*' + _ELLIPSIS + r'\s*$', '', core)
core = re.sub(r'(?:\s*\\q?quad)+\s*$', '', core)
core = core.rstrip()
return core + wrap
_OP_MAP = {
'≈': r'\approx', '≠': r'\neq', '≤': r'\leq', '≥': r'\geq', '×': r'\times',
'÷': r'\div', '±': r'\pm', '∓': r'\mp', '·': r'\cdot', '∙': r'\cdot',
'⋅': r'\cdot', '∗': '*', '−': '-', '≡': r'\equiv', '∝': r'\propto',
'∞': r'\infty', '√': r'\sqrt', '→': r'\to', '≪': r'\ll', '≫': r'\gg',
}
_ARITH_ALLOWED = re.compile(r'^[0-9A-Za-z\s=+\-*/^_().,:;<>|%!\u4e00-\u9fff' + ''.join(_OP_MAP.keys()) + r']+$')
_ARITH_HASOP = re.compile(r'[=+\-*/' + ''.join(_OP_MAP.keys()) + r']')
_KNOWN_FUNCS = {'sin', 'cos', 'tan', 'cot', 'sec', 'csc', 'log', 'ln', 'exp',
'lim', 'max', 'min', 'det', 'mod', 'arcsin', 'arccos', 'arctan', 'sqrt'}
_CJK_RUN = re.compile(r'[\u4e00-\u9fff]+')
_MATH_SPAN = re.compile(r'(\\\[.*?\\\]|\$\$.*?\$\$|\\\(.*?\\\)|\$.*?\$)', re.DOTALL)
WRAP_CJK_IN_ARITH = True
def _convert_unicode_ops(s: str) -> str:
for k, v in _OP_MAP.items():
s = s.replace(k, (v + ' ') if v.startswith('\\') else v)
if WRAP_CJK_IN_ARITH:
s = _CJK_RUN.sub(lambda m: r'\text{' + m.group(0) + '}', s)
return re.sub(r'[ \t]{2,}', ' ', s)
def _is_pure_arith_line(line: str) -> bool:
t = line.strip()
if not t or '\\(' in t or '\\[' in t or '$' in t or '<' in t:
return False
if not WRAP_CJK_IN_ARITH and re.search(r'[\u4e00-\u9fff]', t):
return False
if not _ARITH_ALLOWED.match(t) or not _ARITH_HASOP.search(t):
return False
return all(w.lower() in _KNOWN_FUNCS for w in re.findall(r'[A-Za-z]{2,}', t))
def normalize_arith(text: str) -> str:
"""Normalize Unicode operators to LaTeX and wrap pure-arithmetic lines as `\\( .. \\)`."""
if not text:
return text
text = _MATH_SPAN.sub(lambda m: _convert_unicode_ops(m.group(0)), text)
out = []
for line in text.split('\n'):
if _is_pure_arith_line(line):
out.append('\\( ' + _convert_unicode_ops(line.strip()) + ' \\)')
else:
out.append(line)
return '\n'.join(out)
def remove_block_fork_tags(result, simplify_left_paren=True, clean_formula_tail_flag=True,
norm_formula_flag=True):
"""Split on `<BLOCK>`, keep the text after each `<CHILD>`, and join in reading order."""
seg_pattern = re.compile(r'[^<]*<CHILD>(.*)', re.DOTALL)
lines = []
for seg in result.split('<BLOCK>')[1:]:
cat_m = re.match(r'\s*([a-zA-Z_]+)', seg)
if cat_m and cat_m.group(1).lower() in ['chart', 'seal']:
continue
m = seg_pattern.match(seg)
if not m:
continue
text = m.group(1).strip()
text = re.sub(r'\b\w+\s*\[\s*[-\d.,\s]+\]\s*<(?:FORK|CHILD|BLOCK)>', '', text)
text = re.sub(r'<(?:FORK|CHILD|BLOCK)>', '', text).strip()
text = text.replace('The image is too blurry to recognize any text content.', '').strip()
text = text.replace("The image contains no text or characters. It is a graphical element (a horizontal line with a vertical line) and does not contain any chart, graph, or data points that can be extracted. Therefore, the correct OCR output is an empty string.", "").strip()
if not text or text == '[Non-Text]':
continue
if text.startswith('\\[') and not text.endswith('\n\\]'):
text += '\n\\]'
if text.startswith('<table>') and not text.endswith('</table>'):
text += '</table>'
if '\\[\n' in text and '\\\\' not in text:
text = text.replace('\\[\n', '\\(').replace('\n\\]', '\\)')
text = text.replace('\\) \\(', '\\)\n\n\\(')
if '÷' in text and '\\(' not in text:
text = '\\( ' + text + ' \\)'
text = re.sub(r'\\tag\s*\{[^{}]*\}', '', text)
text = text.replace('\\supset', '\\sqsupset')
if simplify_left_paren:
text = simplify_left_right(text)
if clean_formula_tail_flag:
text = clean_formula_tail(text)
if norm_formula_flag:
text = normalize_arith(text)
lines.append(text)
return '\n\n'.join(lines).strip()
def basename_to_md_name(img_path: str) -> str:
return os.path.splitext(os.path.basename(img_path))[0] + ".md"
def convert_json(in_path, out_md_dir, simplify_left_paren=True,
clean_formula_tail_flag=True, norm_formula_flag=True) -> int:
with open(in_path, "r", encoding="utf-8") as f:
rows = json.load(f)
os.makedirs(out_md_dir, exist_ok=True)
n = 0
for row in rows:
img_path = row.get("img_path") or row.get("image_path")
pred = row.get("pred") or row.get("prediction") or ""
if not img_path:
continue
md = remove_block_fork_tags(pred, simplify_left_paren, clean_formula_tail_flag, norm_formula_flag)
with open(os.path.join(out_md_dir, basename_to_md_name(img_path)), "w", encoding="utf-8") as f:
f.write(md)
n += 1
return n
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--input", required=True, help="json path (list of {img_path, pred})")
ap.add_argument("--out-md", required=True, help="output markdown folder")
args = ap.parse_args()
if Path(args.input).suffix.lower() != ".json":
raise SystemExit(f"unsupported extension: {Path(args.input).suffix} (expects .json)")
n = convert_json(args.input, args.out_md)
print(f"[ok] wrote {n} markdown files -> {args.out_md}")
if __name__ == "__main__":
main()
|