Instructions to use mlninad/PII-Shield with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlninad/PII-Shield with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mlninad/PII-Shield")
model = AutoModelForCausalLM.from_pretrained("mlninad/PII-Shield", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mlninad/PII-Shield with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mlninad/PII-Shield

SGLang

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

Docker Model Runner
How to use mlninad/PII-Shield with Docker Model Runner:
```
docker model run hf.co/mlninad/PII-Shield
```

PII-Shield / README.md

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	---
	tags:
	- text-generation-inference
	- transformers
	- llama
	- trl
	license: apache-2.0
	language:
	- en
	base_model:
	- meta-llama/Llama-3.2-3B-Instruct
	---
	# 🛡️ PII-Shield
	> Your Intelligent Guardian for Personal Data Protection


	[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
	[![Made with ❤️](https://img.shields.io/badge/Made%20with%20%E2%9D%A4%EF%B8%8F%20by-Data%20Privacy%20Team-red.svg)](https://huggingface.co/mlninad/PII-Shield)

	---

	## 🌟 What is PII-Shield?

	PII-Shield is your cutting-edge solution for protecting sensitive information in text data. Powered by advanced transformer architecture, it's your first line of defense against unintended PII exposure.

	---

	## 🎯 Core Capabilities

	### 🔍 Smart Detection
	```bash
	"Regular text with sarah.smith@email.com" → "Regular text with [EMAIL_1]"
	```

	### 🎭 Intelligent Masking
	```bash
	"Call John at (555) 123-4567" → "Call [PERSON_1] at [PHONE_1]"
	```

	### 📊 Structured Mapping
	```bash
	Original → Masked → JSON Mapping
	```

	---

	## 🚀 Model Architecture

	### 🧠 Two-Stage Intelligence


	![image/png](https://cdn-uploads.huggingface.co/production/uploads/6434f4fe7b8247480110bf44/vSSoxcNxuIoAN-mI3aJvt.png)

	---

	## ⚡ Supported PII Categories

	\| Category \| Icon \| Example \|
	\|----------\|------\|---------\|
	\| Names \| 👤 \| John Smith \|
	\| Emails \| 📧 \| user@domain.com \|
	\| Phones \| 📱 \| (555) 123-4567 \|
	\| Addresses \| 🏠 \| 123 Privacy St \|
	\| SSN \| 🔢 \| XXX-XX-XXXX \|
	\| Credit Cards \| 💳 \| XXXX-XXXX-XXXX \|
	\| DOB \| 📅 \| MM/DD/YYYY \|
	\| IPs \| 🌐 \| 192.168.1.1 \|

	---

	## 💫 How It Works

	### 🎯 Detection Phase
	```python
	def detect_pii(text: str) -> List[Entity]:
	"""
	🔍 Intelligent PII detection
	Returns list of identified entities
	"""
	pass
	```

	### 🎭 Masking Phase
	```python
	def mask_pii(text: str, entities: List[Entity]) -> Dict:
	"""
	🛡️ Smart PII masking
	Returns masked text and mapping
	"""
	pass
	```

	---

	## 🎮 Input/Output

	### 📥 Input Format
	```json
	{
	"text": "Your sensitive text here",
	"options": {
	"mask_format": "[TYPE_INDEX]",
	"return_mapping": true
	}
	}
	```

	### 📤 Output Format
	```json
	{
	"masked_text": "Your [TYPE_1] text here",
	"pii_mapping": [
	{
	"label": "TYPE",
	"value": "sensitive",
	"index": 1
	}
	]
	}
	```

	---

	## 🚦 Performance Stats

	\| Metric \| Score \| Trend \|
	\|--------\|-------\|-------\|
	\| Precision \| 98.5% \| ⬆️ \|
	\| Recall \| 97.8% \| ⬆️ \|
	\| Speed \| 2ms/req \| ⬇️ \|
	\| Accuracy \| 99.1% \| ➡️ \|

	---

	## 🛠️ Technical Requirements

	- 🖥️ CUDA-capable GPU
	- 💾 8GB+ VRAM
	- 🐍 Python 3.8+
	- 🔧 PyTorch 2.0+

	---

	## 🔒 Security First

	![image/png](https://cdn-uploads.huggingface.co/production/uploads/6434f4fe7b8247480110bf44/z0zmLQATLKRNHFoA4nOZK.png)

	## 🎯 Best Practices

	1. 🔐 Never store raw PII
	2. 💾 Process in-memory only
	3. 🧹 Clear cache regularly
	4. 📝 Enable access logging
	5. 🔄 Regular updates

	---

	## ⚠️ Known Limitations

	- 📏 Max 2048 tokens
	- 🗣️ English-primary
	- 💡 Domain adaptation needed
	- 💾 GPU memory bound

	---

	## 📜 License

	Apache License 2.0 • Made with ❤️ for Privacy

	---

	## 🤝 Support & Community

	- 💬 [LinkedIn](https://www.linkedin.com/in/workwithninad)
	- 📧 [Email Support](mailto:mail.ninad@yahoo.com)