Image-Text-to-Text
Transformers
Safetensors
nemotron_parse
text-generation
VLM
OCR
Parse
conversational
custom_code
Eval Results
Instructions to use nvidia/Nemotron-3.5-Parse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/Nemotron-3.5-Parse with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="nvidia/Nemotron-3.5-Parse", 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("nvidia/Nemotron-3.5-Parse", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nvidia/Nemotron-3.5-Parse with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nvidia/Nemotron-3.5-Parse" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nvidia/Nemotron-3.5-Parse", "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/nvidia/Nemotron-3.5-Parse
- SGLang
How to use nvidia/Nemotron-3.5-Parse 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 "nvidia/Nemotron-3.5-Parse" \ --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": "nvidia/Nemotron-3.5-Parse", "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 "nvidia/Nemotron-3.5-Parse" \ --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": "nvidia/Nemotron-3.5-Parse", "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 nvidia/Nemotron-3.5-Parse with Docker Model Runner:
docker model run hf.co/nvidia/Nemotron-3.5-Parse
Download reference/parsebench/pipeline/engine/chart_caption.py from nvidia/Nemotron-3.5-Parse: direct link, hf CLI and curl.
- Browser
- Download file 1.63 kB
-
https://huggingface.co/nvidia/Nemotron-3.5-Parse/resolve/main/reference/parsebench/pipeline/engine/chart_caption.py
- Command line
-
hf download hf://nvidia/Nemotron-3.5-Parse/reference/parsebench/pipeline/engine/chart_caption.py
-
curl -L -o chart_caption.py https://huggingface.co/nvidia/Nemotron-3.5-Parse/resolve/main/reference/parsebench/pipeline/engine/chart_caption.py
1.63 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 | |