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
Download nemotron_parse/engine/markdown.py from sandeeprdy1729/thrisul-parser: direct link, hf CLI and curl.
- Browser
- Download file 2.09 kB
-
https://huggingface.co/sandeeprdy1729/thrisul-parser/resolve/main/nemotron_parse/engine/markdown.py
- Command line
-
hf download hf://sandeeprdy1729/thrisul-parser/nemotron_parse/engine/markdown.py
-
curl -L -o markdown.py https://huggingface.co/sandeeprdy1729/thrisul-parser/resolve/main/nemotron_parse/engine/markdown.py
2.09 kB
| # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. | |
| # SPDX-License-Identifier: OpenMDW-1.1 | |
| from __future__ import annotations | |
| import re | |
| _RE_EXTRACT_CLASS_BBOX = re.compile( | |
| r"<x_(\d+(?:\.\d+)?)><y_(\d+(?:\.\d+)?)>(.*?)<x_(\d+(?:\.\d+)?)><y_(\d+(?:\.\d+)?)><class_([^>]+)>", | |
| re.DOTALL, | |
| ) | |
| def extract_classes_bboxes( | |
| text: str, | |
| ) -> tuple[list[str], list[tuple[float, float, float, float]], list[str]]: | |
| classes: list[str] = [] | |
| bboxes: list[tuple[float, float, float, float]] = [] | |
| texts: list[str] = [] | |
| for match in _RE_EXTRACT_CLASS_BBOX.finditer(text or ""): | |
| x1, y1, body, x2, y2, cls = match.groups() | |
| classes.append(cls) | |
| bboxes.append((float(x1), float(y1), float(x2), float(y2))) | |
| texts.append(body) | |
| return classes, bboxes, texts | |
| def convert_latex_tables_in_markdown(markdown_text: str) -> str: | |
| if "\\begin{tabular}" not in markdown_text: | |
| return markdown_text | |
| try: | |
| from .convert.latex2html import latex_table_to_html | |
| return latex_table_to_html(markdown_text) | |
| except (ValueError, IndexError): | |
| return markdown_text | |
| def mmd_to_markdown(prediction: str, text_transform=None) -> str: | |
| classes, _boxes, texts = extract_classes_bboxes(prediction or "") | |
| if not texts: | |
| cleaned = re.sub(r"<x_[^>]+>|<y_[^>]+>|<class_[^>]+>", "", prediction or "") | |
| return convert_latex_tables_in_markdown(cleaned.replace("<tbc>", "").strip()) | |
| blocks = [] | |
| for cls, text in zip(classes, texts): | |
| if text_transform is not None: | |
| text = text_transform(cls, text) | |
| text = text.replace("<tbc>", "").strip() | |
| if not text: | |
| continue | |
| if cls in {"Table", "Chart"}: | |
| text = convert_latex_tables_in_markdown(text) | |
| elif cls == "Title" and not text.startswith("#"): | |
| text = f"# {text}" | |
| elif cls == "Section-header" and not text.startswith("#"): | |
| text = f"## {text}" | |
| blocks.append(text) | |
| return "\n\n".join(blocks).strip() | |