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 /multilingual /indicvision_by_language.csv

emelryan

sosahi

ronay-nv

Nemotron 3.5 Parse release

dca5b2d 16 days ago

Raw History Blame Contribute Delete

1.93 kB

language,pages,parse20_anls_character_percent,parse20_anls_word_percent,parse20_cer_percent,parse20_wer_percent,parse35_anls_character_percent,parse35_anls_word_percent,parse35_cer_percent,parse35_wer_percent
Overall,876,72.12007990867585,51.42649543378995,52.66800560620728,74.26964955078242,83.72711187214614,59.45715753424658,15.999815775404933,38.21205450110073
Bengali,89,55.47258426966293,43.14539325842696,68.92394670657438,58.65819301578982,90.25269662921347,73.41606741573034,7.691588202282222,25.787852977789427
Gujarati,90,40.70311111111112,28.53499999999999,127.52799545349032,128.19302032739702,66.9641111111111,47.40699999999997,34.08201327132577,54.36229537686634
Hindi,85,85.0314117647059,68.56494117647058,16.32126561767089,26.979189015232784,88.34435294117647,71.09823529411766,9.356388498002929,25.269970678681258
Kannada,89,87.54157303370788,64.3401123595506,19.248679279588682,36.743491958215884,91.36786516853928,66.53606741573034,7.329366963302659,32.84695738683469
Malayalam,88,48.89261363636362,18.736250000000005,75.55186737044494,137.55043486057562,63.14875000000002,23.803409090909096,42.46975385899041,79.13565856720165
Marathi,83,92.9490361445783,76.79590361445784,6.502564255249466,22.553620994196315,93.18722891566267,78.06602409638553,6.01278778089574,21.16073681554378
Odia,86,67.76930232558138,38.14011627906977,71.46375791695989,114.49573982291028,73.61046511627906,39.979302325581386,24.613874587947702,60.806370774628284
Punjabi,89,83.25382022471912,71.73033707865167,66.57171875182405,100.7157123834887,91.95044943820227,79.75404494382023,7.304374219306335,19.751442521083
Tamil,88,81.76590909090905,63.96625000000002,42.05124338994734,55.485864070398804,91.2394318181818,64.96693181818183,7.412951933743309,34.31941583973039
Telugu,89,79.85966292134835,42.37415730337079,43.56716275602632,84.727385377943,87.7515730337079,50.46235955056183,10.52622454905564,46.514869888475836

	language,pages,parse20_anls_character_percent,parse20_anls_word_percent,parse20_cer_percent,parse20_wer_percent,parse35_anls_character_percent,parse35_anls_word_percent,parse35_cer_percent,parse35_wer_percent
	Overall,876,72.12007990867585,51.42649543378995,52.66800560620728,74.26964955078242,83.72711187214614,59.45715753424658,15.999815775404933,38.21205450110073
	Bengali,89,55.47258426966293,43.14539325842696,68.92394670657438,58.65819301578982,90.25269662921347,73.41606741573034,7.691588202282222,25.787852977789427
	Gujarati,90,40.70311111111112,28.53499999999999,127.52799545349032,128.19302032739702,66.9641111111111,47.40699999999997,34.08201327132577,54.36229537686634
	Hindi,85,85.0314117647059,68.56494117647058,16.32126561767089,26.979189015232784,88.34435294117647,71.09823529411766,9.356388498002929,25.269970678681258
	Kannada,89,87.54157303370788,64.3401123595506,19.248679279588682,36.743491958215884,91.36786516853928,66.53606741573034,7.329366963302659,32.84695738683469
	Malayalam,88,48.89261363636362,18.736250000000005,75.55186737044494,137.55043486057562,63.14875000000002,23.803409090909096,42.46975385899041,79.13565856720165
	Marathi,83,92.9490361445783,76.79590361445784,6.502564255249466,22.553620994196315,93.18722891566267,78.06602409638553,6.01278778089574,21.16073681554378
	Odia,86,67.76930232558138,38.14011627906977,71.46375791695989,114.49573982291028,73.61046511627906,39.979302325581386,24.613874587947702,60.806370774628284
	Punjabi,89,83.25382022471912,71.73033707865167,66.57171875182405,100.7157123834887,91.95044943820227,79.75404494382023,7.304374219306335,19.751442521083
	Tamil,88,81.76590909090905,63.96625000000002,42.05124338994734,55.485864070398804,91.2394318181818,64.96693181818183,7.412951933743309,34.31941583973039
	Telugu,89,79.85966292134835,42.37415730337079,43.56716275602632,84.727385377943,87.7515730337079,50.46235955056183,10.52622454905564,46.514869888475836