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 / pyproject.toml

sandeeprdy1729

Thrisul-Parser v0.1

37fb2b9 verified 21 days ago

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1.37 kB

[build-system]
requires = ["setuptools==80.9.0", "wheel==0.46.3"]
build-backend = "setuptools.build_meta"

[project]
name = "nemotron-parse"
version = "3.5.0.post7"
description = "Nemotron 3.5 Parse document parsing and vLLM integration"
requires-python = ">=3.10"
license = "LicenseRef-OpenMDW-1.1"
license-files = ["LICENSE", "THIRD_PARTY_NOTICES.md", "TOKENIZER_ATTRIBUTION.md"]
dependencies = [
    "torch>=2.11.0,<2.14",
    "torchvision>=0.26.0,<0.29",
    "transformers>=5.14.1,<5.20",
    "huggingface-hub>=1.24.0,<2",
    "packaging>=24.2",
    "accelerate==1.12.0",
    "timm==1.0.22",
    "einops==0.8.1",
    "Pillow>=12.2.0,<13",
    "numpy==2.2.6",
    "safetensors==0.8.0",
    "beautifulsoup4==4.13.3",
]

# FlashAttention FA3 is supplied by the selected vLLM build, not a standalone flash-attn wheel.
[project.optional-dependencies]
vllm = ["vllm>=0.25.0,<0.33"]
client = ["openai==2.30.0", "httpx==0.28.1"]
dev = ["pytest==9.0.2"]

[project.entry-points."vllm.general_plugins"]
nemotron_parse_35 = "vllm_tied_patch.plugin:register"

[project.scripts]
nemotron-parse = "nemotron_parse.inference:main"

[tool.setuptools]
py-modules = ["postprocessing", "latex2html"]

[tool.setuptools.packages.find]
include = ["vllm_tied_patch*", "nemotron_parse*"]

[tool.setuptools.package-data]
nemotron_parse = ["SOURCE_MANIFEST.json"]

[tool.uv]
package = true

	[build-system]
	requires = ["setuptools==80.9.0", "wheel==0.46.3"]
	build-backend = "setuptools.build_meta"

	[project]
	name = "nemotron-parse"
	version = "3.5.0.post7"
	description = "Nemotron 3.5 Parse document parsing and vLLM integration"
	requires-python = ">=3.10"
	license = "LicenseRef-OpenMDW-1.1"
	license-files = ["LICENSE", "THIRD_PARTY_NOTICES.md", "TOKENIZER_ATTRIBUTION.md"]
	dependencies = [
	"torch>=2.11.0,<2.14",
	"torchvision>=0.26.0,<0.29",
	"transformers>=5.14.1,<5.20",
	"huggingface-hub>=1.24.0,<2",
	"packaging>=24.2",
	"accelerate==1.12.0",
	"timm==1.0.22",
	"einops==0.8.1",
	"Pillow>=12.2.0,<13",
	"numpy==2.2.6",
	"safetensors==0.8.0",
	"beautifulsoup4==4.13.3",
	]

	# FlashAttention FA3 is supplied by the selected vLLM build, not a standalone flash-attn wheel.
	[project.optional-dependencies]
	vllm = ["vllm>=0.25.0,<0.33"]
	client = ["openai==2.30.0", "httpx==0.28.1"]
	dev = ["pytest==9.0.2"]

	[project.entry-points."vllm.general_plugins"]
	nemotron_parse_35 = "vllm_tied_patch.plugin:register"

	[project.scripts]
	nemotron-parse = "nemotron_parse.inference:main"

	[tool.setuptools]
	py-modules = ["postprocessing", "latex2html"]

	[tool.setuptools.packages.find]
	include = ["vllm_tied_patch", "nemotron_parse"]

	[tool.setuptools.package-data]
	nemotron_parse = ["SOURCE_MANIFEST.json"]

	[tool.uv]
	package = true