Instructions to use nvidia/Nemotron-3.5-Parse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

Nemotron-3.5-Parse / reference /parsebench /pipeline /engine /chart_context.py

emelryan

sosahi

ronay-nv

Nemotron 3.5 Parse release

dca5b2d 15 days ago

Raw History Blame Contribute Delete

1.07 kB

# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: OpenMDW-1.1
"""Preserve explicit chart subtitles in their existing table-caption context."""
import re
from bs4 import BeautifulSoup,NavigableString
TABLE=re.compile(r'<table\b[^>]*>.*?</table>',re.S|re.I)
CHART=re.compile(r'(<chart\b[^>]*>)(.*?)(</chart>)',re.S|re.I)
def add_native_subtitles(text):
 def chart(match):
  body=match[2]
  labels=[m[1].strip() for m in re.finditer(r'(?m)^subtitle:[ \t]*([^\n]*)',body) if m[1].strip()]
  if not labels:return match[0]
  def table(m):
   soup=BeautifulSoup(m[0],'html.parser');node=soup.find('table');cap=node.find('caption',recursive=False)
   if not cap:return m[0]
   existing=cap.get_text(' ',strip=True);added=[]
   for value in labels:
    if value not in existing and value not in added:added.append(value)
   if not added:return m[0]
   cap.append(NavigableString(' — '+' — '.join(added)));return str(node)
  return match[1]+TABLE.sub(table,body)+match[3]
 return CHART.sub(chart,text)

	# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
	# SPDX-License-Identifier: OpenMDW-1.1
	"""Preserve explicit chart subtitles in their existing table-caption context."""
	import re
	from bs4 import BeautifulSoup,NavigableString
	TABLE=re.compile(r'<table\b[^>]>.?</table>',re.S\|re.I)
	CHART=re.compile(r'(<chart\b[^>]>)(.?)(</chart>)',re.S\|re.I)
	def add_native_subtitles(text):
	def chart(match):
	body=match[2]
	labels=[m[1].strip() for m in re.finditer(r'(?m)^subtitle:[ \t]([^\n])',body) if m[1].strip()]
	if not labels:return match[0]
	def table(m):
	soup=BeautifulSoup(m[0],'html.parser');node=soup.find('table');cap=node.find('caption',recursive=False)
	if not cap:return m[0]
	existing=cap.get_text(' ',strip=True);added=[]
	for value in labels:
	if value not in existing and value not in added:added.append(value)
	if not added:return m[0]
	cap.append(NavigableString(' — '+' — '.join(added)));return str(node)
	return match[1]+TABLE.sub(table,body)+match[3]
	return CHART.sub(chart,text)