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/paddle_optimized/serving_contract.json from nvidia/Nemotron-3.5-Parse: direct link, hf CLI and curl.
- Browser
- Download file 958 Bytes
-
https://huggingface.co/nvidia/Nemotron-3.5-Parse/resolve/main/reference/parsebench/paddle_optimized/serving_contract.json
- Command line
-
hf download hf://nvidia/Nemotron-3.5-Parse/reference/parsebench/paddle_optimized/serving_contract.json
-
curl -L -o serving_contract.json https://huggingface.co/nvidia/Nemotron-3.5-Parse/resolve/main/reference/parsebench/paddle_optimized/serving_contract.json
958 Bytes
| { | |
| "pages": 2078, | |
| "gpu": "one NVIDIA H100", | |
| "precision": "BF16", | |
| "vllm": "0.26.0", | |
| "max_model_len": 16384, | |
| "max_new_tokens": 8192, | |
| "max_num_seqs": 512, | |
| "max_num_batched_tokens": 32768, | |
| "gpu_memory_utilization": 0.6, | |
| "flash_attn_version": 3, | |
| "prefix_caching": false, | |
| "mm_processor_cache_gb": 0, | |
| "frozen_quality": { | |
| "max_new_tokens": 8192, | |
| "image_resize": false, | |
| "chart_recognition": true, | |
| "seal_recognition": true, | |
| "ocr_for_image_block": true, | |
| "format_block_content": true, | |
| "merge_layout_blocks": true, | |
| "layout_threshold": 0.3, | |
| "doc_preprocessing": false | |
| }, | |
| "pipeline": { | |
| "workers": 4, | |
| "layout_batch": 8, | |
| "crop_batch": 8192, | |
| "concurrency": 256, | |
| "call_batch": 0, | |
| "instances": 4 | |
| }, | |
| "source_contract_sha256": "ea66b126a579102393d3d6b464e8228b28a735c152324956bcfb037c08cc0617", | |
| "vl_weights_sha256": "85a479d506a11e724e7285d395c551be69f41dbc16b6342d3cacfb189aed71db" | |
| } | |