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/comparisons/results.json from nvidia/Nemotron-3.5-Parse: direct link, hf CLI and curl.
- Browser
- Download file 46.2 kB
-
https://huggingface.co/nvidia/Nemotron-3.5-Parse/resolve/main/reference/parsebench/comparisons/results.json
- Command line
-
hf download hf://nvidia/Nemotron-3.5-Parse/reference/parsebench/comparisons/results.json
-
curl -L -o results.json https://huggingface.co/nvidia/Nemotron-3.5-Parse/resolve/main/reference/parsebench/comparisons/results.json
46.2 kB
| { | |
| "score_scale": "0–100; overall is the unweighted mean of five metrics", | |
| "published_snapshot": "https://github.com/run-llama/ParseBench/blob/9af4102e26b51f234ab5f037e19fec05c0696ec6/leaderboard.csv", | |
| "sweep_scorer_revision": "d423e529ae329317bcd33c650b367acfb60e6620", | |
| "dataset_revision": "2805a1d940f95a203e0ae4b88be9934f7765b3fc", | |
| "full_pipeline_timing": "Historical records under models only: Original-document source reads, fresh rendering, all inference stages, normalization, output assembly and writes. Excludes server startup, 16-document warmup and scoring. Documents/s, not rendered pages/s; 2078 original documents render to 2080 pages.", | |
| "score_contract_qualification": "The 20-model sweep, current Parse 3.5 release and legacy Parse 2.0 result use different scorer snapshots/case counts. They are contextual comparisons, not a fully matched current-scorer rerun. Published scores can additionally differ in model revision, pipeline and scoring protocol. Reproduced visual grounding uses aggregate EPR; published visual grounding is retained under its original name and may not be comparable.", | |
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| "published_claim_qualification": "The v3 overall claim is 77.50. The model card category table predates the v3 changelog; those older category numbers are not assigned to v3.", | |
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