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/validation/batch_benchmarks.json from nvidia/Nemotron-3.5-Parse: direct link, hf CLI and curl.
- Browser
- Download file 37.4 kB
-
https://huggingface.co/nvidia/Nemotron-3.5-Parse/resolve/main/reference/validation/batch_benchmarks.json
- Command line
-
hf download hf://nvidia/Nemotron-3.5-Parse/reference/validation/batch_benchmarks.json
-
curl -L -o batch_benchmarks.json https://huggingface.co/nvidia/Nemotron-3.5-Parse/resolve/main/reference/validation/batch_benchmarks.json
37.4 kB
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| "power_watts": 680.93, | |
| "memory_used_mib": 78925, | |
| "scope": "one live snapshot; not a run average" | |
| }, | |
| { | |
| "observed_at_utc": "2026-10-17T22:59:45.983785+00:00", | |
| "stage": "triple_pipelined", | |
| "gpu_utilization_percent": 100, | |
| "power_watts": 665.11, | |
| "memory_used_mib": 79869, | |
| "scope": "one live snapshot during triple-pass inference; not a run average" | |
| } | |
| ], | |
| "artifact_check": { | |
| "files": 77, | |
| "bytes": 49047529, | |
| "per_page_artifacts": false | |
| }, | |
| "limitations": [ | |
| "Nonzero-temperature generation can differ with batching; request and completion-token totals are reported.", | |
| "Token-limited responses are retained only through explicit allow_partial=True; affected pages are counted incomplete.", | |
| "Prepared throughput includes crop preparation and rendering; excludes startup, initial preparation, warmup and serialization.", | |
| "These measurements are serving profiles, not a hardware throughput ceiling." | |
| ], | |
| "merge_only_replays_on_baseline_triple_responses": { | |
| "keep_original_charts": { | |
| "scores": { | |
| "charts": 0.575614863422962, | |
| "visual_grounding": 0.7048420250334769, | |
| "tables": 0.8234070433681294, | |
| "content_faithfulness": 0.8808105394777015, | |
| "semantic_formatting": 0.6812589701148343, | |
| "overall": 0.7331866882834208 | |
| }, | |
| "fresh_page_evaluations": 2078, | |
| "official_errors": 0, | |
| "replay": { | |
| "pages": 2078, | |
| "variant": "keep_original_charts", | |
| "seconds": 3.7608036780147813, | |
| "overlapping_alternative_blocks_inserted": 0, | |
| "new_inference_requests": 0 | |
| } | |
| }, | |
| "reference_merge": { | |
| "scores": { | |
| "charts": 0.6462693458732192, | |
| "visual_grounding": 0.7065754139322148, | |
| "tables": 0.8442506317977139, | |
| "content_faithfulness": 0.8809734768808889, | |
| "semantic_formatting": 0.6813144713860899, | |
| "overall": 0.7518766679740253 | |
| }, | |
| "fresh_page_evaluations": 2078, | |
| "official_errors": 0, | |
| "replay": { | |
| "pages": 2078, | |
| "variant": "reference_merge", | |
| "seconds": 5.24810417900153, | |
| "overlapping_alternative_blocks_inserted": 1664, | |
| "new_inference_requests": 0 | |
| } | |
| }, | |
| "reference_merge_complete_crops": { | |
| "scores": { | |
| "charts": 0.6466214585492754, | |
| "visual_grounding": 0.7065754139322148, | |
| "tables": 0.8442506317977139, | |
| "content_faithfulness": 0.8809734768808889, | |
| "semantic_formatting": 0.6813144713860899, | |
| "overall": 0.7519470905092366 | |
| }, | |
| "fresh_page_evaluations": 2078, | |
| "official_errors": 0, | |
| "replay": { | |
| "pages": 2078, | |
| "variant": "reference_merge_complete_crops", | |
| "seconds": 6.634797670994885, | |
| "overlapping_alternative_blocks_inserted": 1660, | |
| "new_inference_requests": 0 | |
| } | |
| } | |
| }, | |
| "quality_interpretation": "Reference merging can preserve overlapping alternative tables/charts. The chart evaluator searches all rendered tables for the best matching answer. General document output uses guarded replacement and rejects overlapping same-class additions; these are different output contracts.", | |
| "warmup_and_selection": { | |
| "timed_full_runs_start_after": [ | |
| "8-page canary for each mode", | |
| "256-page double/triple concurrency pilots" | |
| ], | |
| "canary_pages_per_mode": 8, | |
| "pilot_pages": 256, | |
| "pilot_limits": [ | |
| 128, | |
| 256 | |
| ] | |
| } | |
| } | |