Instructions to use tu-ericngo/NuExtract-StructuredIE-v1.2.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tu-ericngo/NuExtract-StructuredIE-v1.2.2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="tu-ericngo/NuExtract-StructuredIE-v1.2.2")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("tu-ericngo/NuExtract-StructuredIE-v1.2.2")
model = AutoModelForMultimodalLM.from_pretrained("tu-ericngo/NuExtract-StructuredIE-v1.2.2")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tu-ericngo/NuExtract-StructuredIE-v1.2.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tu-ericngo/NuExtract-StructuredIE-v1.2.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tu-ericngo/NuExtract-StructuredIE-v1.2.2",
		"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/tu-ericngo/NuExtract-StructuredIE-v1.2.2

SGLang

How to use tu-ericngo/NuExtract-StructuredIE-v1.2.2 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 "tu-ericngo/NuExtract-StructuredIE-v1.2.2" \
    --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": "tu-ericngo/NuExtract-StructuredIE-v1.2.2",
		"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 "tu-ericngo/NuExtract-StructuredIE-v1.2.2" \
        --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": "tu-ericngo/NuExtract-StructuredIE-v1.2.2",
		"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 tu-ericngo/NuExtract-StructuredIE-v1.2.2 with Docker Model Runner:
```
docker model run hf.co/tu-ericngo/NuExtract-StructuredIE-v1.2.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for Model ID

A model fine-tuned for structured information extraction (IE) specifically for political elites.

Model Details

Model Description

This is an early fine-tuned version of NuExtract 2.0-8B, which was originally a fine-tuned version of Qwen2.5-VL-7B-Instruct for structured information extraction (IE). Particularly, the target task involve joint named entity recognition (NER) and relation extraction (RE) to identify & extract information about politicla elites, their educational and professional associations, events and timeframes, and family members. The extracted information is generated in a structured JSON output. The fine-tuning process is adopted from NuMind team's procedure (numind/NuExtract-2.0-8B). Data for the fine-tuning comes from 2 sources: (1) mannual collection and (2) synthetic data generated by GPT-4.

Developed by: Tu Eric Ngo
Language(s) (NLP): English
Finetuned from model [optional]: numind/NuExtract-2.0-8B

Model Sources [optional]

Uses

The model is fine-tuned to structured information extraction from political elite biographies in a very specific way. It follows a particular template that is very specific to the author's research project. The actual JSON schema and prompt for this fine-tuned task will be published in the future

Out-of-Scope Use

While the fine-tuned model may be able to perform similar structured IE tasks (especially for the simpler tasks with simpler JSON schema), the model is only trained with a specific task in mind. However, in the future, the author intends to expand the range of structured IE tasks that the model can be used for.

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for tu-ericngo/NuExtract-StructuredIE-v1.2.2

Quantizations

1 model

Paper for tu-ericngo/NuExtract-StructuredIE-v1.2.2

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 54