Instructions to use nvidia/NVIDIA-Nemotron-Parse-v1.1-TC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/NVIDIA-Nemotron-Parse-v1.1-TC with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="nvidia/NVIDIA-Nemotron-Parse-v1.1-TC", 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)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("nvidia/NVIDIA-Nemotron-Parse-v1.1-TC", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nvidia/NVIDIA-Nemotron-Parse-v1.1-TC with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/NVIDIA-Nemotron-Parse-v1.1-TC"
# 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/NVIDIA-Nemotron-Parse-v1.1-TC",
		"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/NVIDIA-Nemotron-Parse-v1.1-TC

SGLang

How to use nvidia/NVIDIA-Nemotron-Parse-v1.1-TC 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/NVIDIA-Nemotron-Parse-v1.1-TC" \
    --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/NVIDIA-Nemotron-Parse-v1.1-TC",
		"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/NVIDIA-Nemotron-Parse-v1.1-TC" \
        --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/NVIDIA-Nemotron-Parse-v1.1-TC",
		"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/NVIDIA-Nemotron-Parse-v1.1-TC with Docker Model Runner:
```
docker model run hf.co/nvidia/NVIDIA-Nemotron-Parse-v1.1-TC
```

NVIDIA-Nemotron-Parse-v1.1-TC / example.py

katerynaCh

Upload folder using huggingface_hub

0ff7a3c verified 11 months ago

Raw History Blame

2.04 kB

	import torch
	from PIL import Image, ImageDraw
	from transformers import AutoModel, AutoProcessor, AutoTokenizer, AutoConfig, AutoImageProcessor, GenerationConfig
	from postprocessing import extract_classes_bboxes, transform_bbox_to_original, postprocess_text

	# Load model and processor
	model_path = "nvidia/NVIDIA-Nemotron-Parse-v1.1-Light" # Or use a local path
	device = "cuda:0"

	model = AutoModel.from_pretrained(
	model_path,
	trust_remote_code=True,
	torch_dtype=torch.bfloat16
	).to(device).eval()
	tokenizer = AutoTokenizer.from_pretrained(model_path)
	processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)

	# Load image
	image = Image.open("path/to/your/image.jpg")
	task_prompt = "</s><s><predict_bbox><predict_classes><output_markdown>"

	# Process image
	inputs = processor(images=[image], text=task_prompt, return_tensors="pt").to(device)
	prompt_ids = processor.tokenizer.encode(task_prompt, return_tensors="pt", add_special_tokens=False).cuda()


	generation_config = GenerationConfig.from_pretrained(model_path, trust_remote_code=True)
	# Generate text
	outputs = model.generate(**inputs, generation_config=generation_config)

	# Decode the generated text
	generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0]
	classes, bboxes, texts = extract_classes_bboxes(generated_text)
	bboxes = [transform_bbox_to_original(bbox, image.width, image.height) for bbox in bboxes]

	# Specify output formats for postprocessing
	table_format = 'latex' # latex \| HTML \| markdown
	text_format = 'markdown' # markdown \| plain
	blank_text_in_figures = False # remove text inside 'Picture' class
	texts = [postprocess_text(text, cls = cls, table_format=table_format, text_format=text_format, blank_text_in_figures=blank_text_in_figures) for text, cls in zip(texts, classes)]

	for cl, bb, txt in zip(classes, bboxes, texts):
	print(cl, ': ', txt)

	# OPTIONAL - Draw bounding boxes
	draw = ImageDraw.Draw(image)
	for bbox in bboxes:
	draw.rectangle((bbox[0], bbox[1], bbox[2], bbox[3]), outline="red")