Instructions to use talha2134/StructuraAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use talha2134/StructuraAI with Transformers:

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

pipe = pipeline("text-generation", model="talha2134/StructuraAI")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("talha2134/StructuraAI")
model = AutoModelForCausalLM.from_pretrained("talha2134/StructuraAI", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use talha2134/StructuraAI with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "talha2134/StructuraAI"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "talha2134/StructuraAI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/talha2134/StructuraAI

SGLang

How to use talha2134/StructuraAI 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 "talha2134/StructuraAI" \
    --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": "talha2134/StructuraAI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "talha2134/StructuraAI" \
        --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": "talha2134/StructuraAI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use talha2134/StructuraAI with Docker Model Runner:
```
docker model run hf.co/talha2134/StructuraAI
```

StructuraAI

StructuraAI is a fine-tuned language model based on Qwen3-0.6B. It is designed to automatically extract structured information from unstructured and messy text and return the extracted information as JSON.

Unlike conventional extraction systems that require explicit instructions for every request, StructuraAI aims to identify available information automatically.

Features

Automatic information extraction.
Converts unstructured text into JSON.
Extracts only information explicitly mentioned.
Omits attributes that are not present in the input.
Avoids intentionally inferring missing information.

Example

Input

Ahmed is 22 years old and works at Google as an AI Engineer for 3 years.

Output

{
  "name": "Ahmed",
  "age": 22,
  "company": "Google",
  "designation": "AI Engineer",
  "experience": 3
}

Base Model

Qwen/Qwen3-0.6B

Training Procedure

This model was fine-tuned using Supervised Fine-Tuning (SFT) with the TRL library.

The training dataset consists of synthetically generated examples designed for information extraction from unstructured text.

Intended Use

StructuraAI is intended for:

Information extraction.
Converting messy text into structured JSON.
Preparing unstructured data for downstream applications.
Prototyping automated data-processing workflows.

Limitations

The model may occasionally generate invalid JSON.
It may confuse attributes with similar meanings.
Extraction accuracy depends on the wording and quality of the input.
Outputs should be validated before production use.

Frameworks

Transformers
TRL
PyTorch
Hugging Face Datasets

Acknowledgements

Built upon Qwen3-0.6B and trained using the Hugging Face TRL ecosystem.

Downloads last month: -

Safetensors

Model size

0.6B params

Tensor type

F32

Model tree for talha2134/StructuraAI

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Finetuned

(1,487)

this model