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/validation_metrics.json from nvidia/Nemotron-3.5-Parse: direct link, hf CLI and curl.
- Browser
- Download file 15 kB
-
https://huggingface.co/nvidia/Nemotron-3.5-Parse/resolve/main/reference/parsebench/pipeline/validation_metrics.json
- Command line
-
hf download hf://nvidia/Nemotron-3.5-Parse/reference/parsebench/pipeline/validation_metrics.json
-
curl -L -o validation_metrics.json https://huggingface.co/nvidia/Nemotron-3.5-Parse/resolve/main/reference/parsebench/pipeline/validation_metrics.json
15 kB
| { | |
| "model_revision": "e5e0406c85ec241b164907bf41ebd648a54e4ea2", | |
| "model_sha256": "81dd7609c589f3694ce75520c25af5010953231358de6aec4d03e8c6aac787e9", | |
| "wheel": { | |
| "file": "nemotron_parse-3.5.0.post2-py3-none-any.whl", | |
| "sha256": "63d6fe932dfc3cab5d029086f3c7c0ac7f75fad6f6f8c08799a60c54bfa12bdb" | |
| }, | |
| "dataset": { | |
| "repo": "llamaindex/ParseBench", | |
| "revision": "2805a1d940f95a203e0ae4b88be9934f7765b3fc", | |
| "pages": 2078, | |
| "renderer": "PyMuPDF==1.26.3", | |
| "dpi": 150, | |
| "jpeg_quality": 92, | |
| "source_page_index": 0 | |
| }, | |
| "public_input_parity": { | |
| "pages": 2078, | |
| "image_mismatches": [], | |
| "annotation_mismatches": [], | |
| "public_dataset_revision": "2805a1d940f95a203e0ae4b88be9934f7765b3fc", | |
| "renderer": "PyMuPDF==1.26.3", | |
| "dpi": 150, | |
| "jpeg_quality": 92, | |
| "source_page_index": 0 | |
| }, | |
| "scorer_revision": "9af4102e26b51f234ab5f037e19fec05c0696ec6", | |
| "executed_python_source_hashes": { | |
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| "engine/text_cleanup.py": "f02f472d7b3cb08eca5b11d3ecf80ee6ec0636857ade17fb4cd11c13f371c2e4", | |
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| "engine/convert/semantic_styles.py": "3d92146e6c713b9de67cb71e92fe254a1344d9054c3925a85cb778ec68c0d075", | |
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| "scorer_runtime": { | |
| "aiohappyeyeballs": "2.7.1", | |
| "aiohttp": "3.14.3", | |
| "aiosignal": "1.4.0", | |
| "anls_star": "1.0.1", | |
| "annotated-doc": "0.0.4", | |
| "annotated-types": "0.7.0", | |
| "anyio": "4.12.1", | |
| "apted": "1.0.3", | |
| "attrs": "25.4.0", | |
| "autoevals": "0.1.0", | |
| "beautifulsoup4": "4.14.3", | |
| "bleach": "6.3.0", | |
| "certifi": "2026.2.25", | |
| "charset-normalizer": "3.5.1", | |
| "chevron": "0.14.0", | |
| "click": "8.3.1", | |
| "distro": "1.9.0", | |
| "filelock": "3.25.2", | |
| "fire": "0.7.1", | |
| "frozenlist": "1.8.0", | |
| "fsspec": "2026.2.0", | |
| "fuzzysearch": "0.8.1", | |
| "h11": "0.16.0", | |
| "hf-xet": "1.4.2", | |
| "html5lib": "1.1", | |
| "httpcore": "1.0.9", | |
| "httpx": "0.28.1", | |
| "huggingface_hub": "1.7.1", | |
| "idna": "3.11", | |
| "joblib": "1.5.2", | |
| "jsonschema": "4.26.0", | |
| "jsonschema-specifications": "2025.9.1", | |
| "Levenshtein": "0.27.3", | |
| "llama_cloud": "1.6.0", | |
| "lxml": "6.0.2", | |
| "Markdown": "3.10.2", | |
| "markdown-it-py": "4.0.0", | |
| "markdown2": "2.5.5", | |
| "mdurl": "0.1.2", | |
| "multidict": "6.9.1", | |
| "munkres": "1.1.4", | |
| "numpy": "2.4.3", | |
| "packaging": "26.0", | |
| "pandas": "3.0.1", | |
| "parse-bench": "1.0.4", | |
| "pdf2image": "1.16.3", | |
| "pillow": "11.1.0", | |
| "polyleven": "0.11.0", | |
| "propcache": "0.5.4", | |
| "pydantic": "2.12.5", | |
| "pydantic_core": "2.41.5", | |
| "Pygments": "2.19.2", | |
| "pymupdf": "1.26.3", | |
| "pypdf": "6.8.0", | |
| "pytesseract": "0.3.13", | |
| "python-dateutil": "2.9.0.post0", | |
| "python-dotenv": "1.2.2", | |
| "python-Levenshtein": "0.27.3", | |
| "PyYAML": "6.0.3", | |
| "RapidFuzz": "3.14.3", | |
| "referencing": "0.37.0", | |
| "requests": "2.34.2", | |
| "rich": "14.3.3", | |
| "rpds-py": "0.30.0", | |
| "scikit-learn": "1.7.2", | |
| "scipy": "1.17.1", | |
| "shellingham": "1.5.4", | |
| "six": "1.17.0", | |
| "sniffio": "1.3.1", | |
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| "threadpoolctl": "3.6.0", | |
| "tqdm": "4.67.3", | |
| "typer": "0.24.1", | |
| "typing-inspection": "0.4.2", | |
| "typing_extensions": "4.15.0", | |
| "Unidecode": "1.4.0", | |
| "urllib3": "2.8.0", | |
| "webencodings": "0.5.1", | |
| "yarl": "1.25.1" | |
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| "complete": true, | |
| "coverage": { | |
| "fresh_generation_pages": 2078, | |
| "fresh_scored_pages_per_mode": 2078, | |
| "modes_scored": 4, | |
| "official_errors": 0, | |
| "portable_output_parity_records": 8312, | |
| "portable_union_region_parity_pages": 2078, | |
| "portable_http_request_function_unchanged": true, | |
| "portable_http_end_to_end_rerun": true, | |
| "final_title_guard_replay": true, | |
| "final_title_guard_replay_records": 8312, | |
| "final_code_difference_from_http_snapshot": "Line-bounded chart title parsing in both rendering paths and conservative colspan-only next-row header inference. Four pages per mode have changed Markdown; raw outputs, coordinates, and cell text/order/spans are identical.", | |
| "corrected_renderer_replay_records": 8312, | |
| "corrected_renderer_fresh_official_scoring": true | |
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| "serving_runtime": { | |
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| "torch": "2.11.0+cu130", | |
| "torchvision": "0.26.0+cu130", | |
| "transformers": "5.14.1", | |
| "huggingface-hub": "1.24.0", | |
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| "vllm": "0.26.0", | |
| "openai": "2.30.0", | |
| "nemotron-parse": "3.5.0.post2" | |
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| "gpu": "NVIDIA H100 80GB HBM3", | |
| "legacy_sitecustomize_env": null, | |
| "pythonpath": null, | |
| "wheel": "nemotron_parse-3.5.0.post2-py3-none-any.whl" | |
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| "metrics": { | |
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| "optional_union_metrics": { | |
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| "paired_union_delta_percentage_points": 0.051363975768781156, | |
| "prediction_sha256": { | |
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| "engine/convert/semantic_styles.py": "3d92146e6c713b9de67cb71e92fe254a1344d9054c3925a85cb778ec68c0d075", | |
| "engine/convert/__init__.py": "717a1b9838b948a88d2fb5106069db57dce81dc54854cf2be5fb54150c7ac652" | |
| }, | |
| "source_hash_scope": "executed_python_source_hashes preserves the original H100 HTTP snapshot. portable_python_sha256 identifies the corrected final renderer, replayed on all8312 saved responses and freshly scored. The hashes differ in engine/chart_caption.py, engine/policies.py, and engine/header_inference_probe.py.", | |
| "generation_requests": { | |
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| "complete": true, | |
| "pages": 2078, | |
| "passes": 1, | |
| "fullpage_requests": 2078, | |
| "detection_requests": 0, | |
| "crop_requests": 0, | |
| "generation_errors": 0, | |
| "fullpage_seconds": 142.38590936901164, | |
| "additional_mode_seconds": 2.474504841986345, | |
| "fullpage_shared_across_modes": true, | |
| "temporary_crops_removed": true | |
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| "double": { | |
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| "passes": 2, | |
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| "detection_requests": 0, | |
| "crop_requests": 2339, | |
| "generation_errors": 0, | |
| "fullpage_seconds": 142.38590936901164, | |
| "additional_mode_seconds": 77.63449284402304, | |
| "fullpage_shared_across_modes": true, | |
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| "triple_union": { | |
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| "pages": 2078, | |
| "passes": 3, | |
| "fullpage_requests": 2078, | |
| "detection_requests": 2078, | |
| "crop_requests": 2616, | |
| "generation_errors": 0, | |
| "fullpage_seconds": 142.38590936901164, | |
| "additional_mode_seconds": 174.39476006900077, | |
| "fullpage_shared_across_modes": true, | |
| "temporary_crops_removed": true | |
| } | |
| }, | |
| "detector_only_control": { | |
| "complete": true, | |
| "pages": 2078, | |
| "detector_only_crop_responses": 2418, | |
| "fresh_full_pipeline_responses": true, | |
| "new_http_requests": 0, | |
| "control": "Same portable postprocessor, same full-page/detector/original-region crop responses; exclude only union additions." | |
| }, | |
| "prediction_hash_scope": "Hashes identify the corrected saved-response replay used for fresh official scoring; original HTTP prediction hashes are retained separately.", | |
| "http_prediction_sha256": { | |
| "single": "1d9da1a592541810fafc4320a4fc3519bd4123e2bc3d45143f4fa98a8f8260a3", | |
| "double": "f9a25c89ce75dd23a1a26189c4ff67f6aed131d3a24a08492e9e6af8b8dee3f3", | |
| "triple": "e4366d479b6016866333e106d3efc0d73efbff27a62ef36c500a7c09f49825d9", | |
| "triple_detector": "297335946dfbef69387ecfae66c158941bda9e153ad6aaa809108c771b2a378f" | |
| }, | |
| "postprocessing_correction": { | |
| "new_inference_requests": 0, | |
| "fixtures": { | |
| "data_row_not_promoted": true, | |
| "explicit_rowspan_preserved": true, | |
| "bold_headers_preserved": true, | |
| "year_data_not_promoted": true, | |
| "bold_numeric_data_not_promoted": true, | |
| "empty_title_full_pipeline": true, | |
| "nonempty_title_preserved": true | |
| }, | |
| "modes": { | |
| "single": { | |
| "changed_pages": 4, | |
| "raw_and_geometry_unchanged": true, | |
| "cell_text_order_spans_unchanged": true, | |
| "prediction_sha256": "f62ab4ed079b79241cce467eb1ad50577a172ca04124d642c31afba8ab7048b2" | |
| }, | |
| "double": { | |
| "changed_pages": 4, | |
| "raw_and_geometry_unchanged": true, | |
| "cell_text_order_spans_unchanged": true, | |
| "prediction_sha256": "3c74420e780ffe36fa60dd3a1abe701d3405be3957242a75326e9fe868a8be6f" | |
| }, | |
| "triple": { | |
| "changed_pages": 4, | |
| "raw_and_geometry_unchanged": true, | |
| "cell_text_order_spans_unchanged": true, | |
| "prediction_sha256": "80b7700cd44e9ee3694454b944bbebf7a9c45ee628b33b1af70bfd8c9fb0a207" | |
| }, | |
| "triple_detector": { | |
| "changed_pages": 4, | |
| "raw_and_geometry_unchanged": true, | |
| "cell_text_order_spans_unchanged": true, | |
| "prediction_sha256": "cf93b01c97cdc8376e501282cbb2498acd174145de1c7d158e362e85df703a00" | |
| } | |
| } | |
| } | |
| } | |