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/chart_caption.py from sandeeprdy1729/thrisul-parser: direct link, hf CLI and curl.
- Browser
- Download file 2.36 kB
-
https://huggingface.co/sandeeprdy1729/thrisul-parser/resolve/main/nemotron_parse/engine/chart_caption.py
- Command line
-
hf download hf://sandeeprdy1729/thrisul-parser/nemotron_parse/engine/chart_caption.py
-
curl -L -o chart_caption.py https://huggingface.co/sandeeprdy1729/thrisul-parser/resolve/main/nemotron_parse/engine/chart_caption.py
2.36 kB
| # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. | |
| # SPDX-License-Identifier: OpenMDW-1.1 | |
| """Render titles explicitly declared by native chart metadata as table captions.""" | |
| import re | |
| from html import escape | |
| def transform(text): | |
| changed = 0 | |
| def chart(m): | |
| nonlocal changed | |
| body = m[2] | |
| if "<chart" in body: | |
| return m[0] | |
| title = re.search(r"(?m)^title:[ \t]*([^\n]*)$", body) | |
| if not title: | |
| return m[0] | |
| title_text = title[1].strip() | |
| if not title_text: | |
| return m[0] | |
| count = 0 | |
| pattern = re.compile( | |
| r"(?m)^\|[^\n]*\|[ \t]*\n\|[ \t:|\-]+\|[ \t]*\n(?:\|[^\n]*\|[ \t]*(?:\n|$))+" | |
| ) | |
| def table(t): | |
| nonlocal count | |
| raw = t[0] | |
| if r"\|" in raw or "`" in raw: | |
| return raw | |
| lines = [a.strip() for a in raw.strip().splitlines()] | |
| cells = [[c.strip() for c in line.strip("|").split("|")] for line in lines] | |
| if ( | |
| len(cells) < 3 | |
| or any(len(row) != len(cells[0]) for row in cells) | |
| or any( | |
| not re.fullmatch(r":?-+:?", c.replace(" ", "")) for c in cells[1] | |
| ) | |
| ): | |
| return raw | |
| count += 1 | |
| result = ( | |
| "<table><caption>" | |
| + escape(title_text) | |
| + "</caption><thead><tr>" | |
| + "".join('<th scope="col">' + escape(c) + "</th>" for c in cells[0]) | |
| + "</tr></thead><tbody>" | |
| ) | |
| result += ( | |
| "".join( | |
| "<tr>" | |
| + "".join("<td>" + escape(c) + "</td>" for c in row) | |
| + "</tr>" | |
| for row in cells[2:] | |
| ) | |
| + "</tbody></table>\n" | |
| ) | |
| return result | |
| new = pattern.sub(table, body) | |
| if not count: | |
| return m[0] | |
| # The title is rendered in its associated captions, retaining text and roles. | |
| new = re.sub(r"(?m)^title:[ \t]*[^\n]*\n?", "", new, count=1) | |
| changed += count | |
| return m[1] + new + m[3] | |
| out = re.sub(r"(<chart\b[^>]*>)(.*?)(</chart>)", chart, text, flags=re.S) | |
| return out, changed | |