Instructions to use sandeeprdy1729/thrisul-parser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sandeeprdy1729/thrisul-parser with Transformers:

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

pipe = pipeline("image-text-to-text", model="sandeeprdy1729/thrisul-parser", 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("sandeeprdy1729/thrisul-parser", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sandeeprdy1729/thrisul-parser with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sandeeprdy1729/thrisul-parser"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sandeeprdy1729/thrisul-parser",
		"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/sandeeprdy1729/thrisul-parser

SGLang

How to use sandeeprdy1729/thrisul-parser 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 "sandeeprdy1729/thrisul-parser" \
    --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": "sandeeprdy1729/thrisul-parser",
		"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 "sandeeprdy1729/thrisul-parser" \
        --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": "sandeeprdy1729/thrisul-parser",
		"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 sandeeprdy1729/thrisul-parser with Docker Model Runner:
```
docker model run hf.co/sandeeprdy1729/thrisul-parser
```

thrisul-parser / explainability.md

sandeeprdy1729

Thrisul-Parser v0.1

37fb2b9 verified 16 days ago

preview code

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## Explainability

Field | Response
:---- | :----
Intended Task/Domain: | Document understanding, OCR, layout parsing, table extraction, and text extraction with spatial grounding.
Model Type: | Transformer-based vision-encoder-decoder model.
Intended Users: | Developers and teams building document intelligence, retrieval-augmented generation (RAG), data-curation, extraction, and multimodal agent workflows.
Output: | Structured text containing document element classes, bounding boxes, reading-order information, chart/table annotations, and extracted text.
Describe how the model works: | Nemotron 3.5 Parse encodes a dynamically sized document image with C-RADIOv4-SO400M, compresses the visual features with a neck, and places them before the OCR prompt as a causal prefix to a 10-layer mBART decoder. The decoder uses grouped-query self-attention and produces structured document annotations with content-normalized boxes. The 72,505-token tokenizer includes multilingual, task-control, and chart-aware tokens such as `<class_Chart>`.
Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable
Technical Limitations & Mitigation: | Performance can vary for low-resolution scans, motion blur, handwriting, decorative fonts, dense tables, unusual layouts, unsupported languages or scripts, and documents with heavy compression artifacts. Users should evaluate outputs on representative documents and use human review for high-impact workflows.
Verified to have met prescribed NVIDIA quality standards: | Yes
Performance Metrics: | OCR accuracy, layout/class accuracy, table extraction quality, chart extraction quality, reading-order quality, grounding quality, latency, throughput, and qualitative visual inspection.
Potential Known Risks: | The model may miss text, hallucinate structure, assign incorrect classes, produce inaccurate bounding boxes, or incorrectly order elements. Downstream systems should treat output as model-generated extraction results and apply validation, confidence checks, and human review where appropriate.
Licensing: | Governing Terms: Your use of this model is governed by the [OpenMDW License Agreement, version 1.1](https://openmdw.ai/license/1-1/). Use of the tokenizer included in this model is governed by the [MIT license and tokenizer attribution](https://huggingface.co/nvidia/Nemotron-3.5-Parse/blob/main/TOKENIZER_ATTRIBUTION.md). See [third-party notices](https://huggingface.co/nvidia/Nemotron-3.5-Parse/blob/main/THIRD_PARTY_NOTICES.md) for separately installed dependencies.

	## Explainability

	Field \| Response
	:---- \| :----
	Intended Task/Domain: \| Document understanding, OCR, layout parsing, table extraction, and text extraction with spatial grounding.
	Model Type: \| Transformer-based vision-encoder-decoder model.
	Intended Users: \| Developers and teams building document intelligence, retrieval-augmented generation (RAG), data-curation, extraction, and multimodal agent workflows.
	Output: \| Structured text containing document element classes, bounding boxes, reading-order information, chart/table annotations, and extracted text.
	Describe how the model works: \| Nemotron 3.5 Parse encodes a dynamically sized document image with C-RADIOv4-SO400M, compresses the visual features with a neck, and places them before the OCR prompt as a causal prefix to a 10-layer mBART decoder. The decoder uses grouped-query self-attention and produces structured document annotations with content-normalized boxes. The 72,505-token tokenizer includes multilingual, task-control, and chart-aware tokens such as `<class_Chart>`.
	Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: \| Not Applicable
	Technical Limitations & Mitigation: \| Performance can vary for low-resolution scans, motion blur, handwriting, decorative fonts, dense tables, unusual layouts, unsupported languages or scripts, and documents with heavy compression artifacts. Users should evaluate outputs on representative documents and use human review for high-impact workflows.
	Verified to have met prescribed NVIDIA quality standards: \| Yes
	Performance Metrics: \| OCR accuracy, layout/class accuracy, table extraction quality, chart extraction quality, reading-order quality, grounding quality, latency, throughput, and qualitative visual inspection.
	Potential Known Risks: \| The model may miss text, hallucinate structure, assign incorrect classes, produce inaccurate bounding boxes, or incorrectly order elements. Downstream systems should treat output as model-generated extraction results and apply validation, confidence checks, and human review where appropriate.
	Licensing: \| Governing Terms: Your use of this model is governed by the [OpenMDW License Agreement, version 1.1](https://openmdw.ai/license/1-1/). Use of the tokenizer included in this model is governed by the [MIT license and tokenizer attribution](https://huggingface.co/nvidia/Nemotron-3.5-Parse/blob/main/TOKENIZER_ATTRIBUTION.md). See [third-party notices](https://huggingface.co/nvidia/Nemotron-3.5-Parse/blob/main/THIRD_PARTY_NOTICES.md) for separately installed dependencies.