Instructions to use ApiFort/LLMFort-pii with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ApiFort/LLMFort-pii with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507")
model = PeftModel.from_pretrained(base_model, "ApiFort/LLMFort-pii")

Transformers

How to use ApiFort/LLMFort-pii with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ApiFort/LLMFort-pii", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ApiFort/LLMFort-pii with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ApiFort/LLMFort-pii

SGLang

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

Docker Model Runner
How to use ApiFort/LLMFort-pii with Docker Model Runner:
```
docker model run hf.co/ApiFort/LLMFort-pii
```

LLMFort-pii / README.md

ApiFort

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	---
	base_model: Qwen/Qwen3-4B-Instruct-2507
	library_name: peft
	pipeline_tag: text-generation
	tags:
	- base_model:adapter:Qwen/Qwen3-4B-Instruct-2507
	- lora
	- sft
	- transformers
	- trl
	- security
	- guardrails
	- multilingual
	language:
	- en
	- tr
	---

	# 🛡️ LLM-Fort Guardrails Suite (v1)

	[![Base Model](https://img.shields.io/badge/Base_Model-Qwen3--4B--Instruct-blue?style=flat-square)](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
	[![Framework](https://img.shields.io/badge/Framework-PEFT_/_LoRA-orange?style=flat-square)](https://github.com/huggingface/peft)
	[![Collection](https://img.shields.io/badge/Collection-LLM--Fort_Guardrails-purple?style=flat-square)](https://huggingface.co/collections/ApiFort/llmfort-guardrails-v1)

	LLM-Fort Guardrails is a suite of 7 security-focused LoRA adapters fine-tuned on top of Qwen/Qwen3-4B-Instruct-2507. These adapters serve as lightweight, high-performance security guardrails mapped to critical safety boundaries.

	By offloading classification and security checks to lightweight adapters, the system achieves enterprise-grade security filtering without degrading the inference performance of the main application model.

	📖 Collection Page: [ApiFort/llmfort-guardrails-v1](https://huggingface.co/collections/ApiFort/llmfort-guardrails-v1)

	---

	## 🗺️ Category Mappings

	\| Vulnerability Category \| Adapter Model ID \| Description \|
	\|:---\|:---\|:---\|
	\| 🚨 [Prompt Injection](https://huggingface.co/ApiFort/jailbreak_content_injection) \| `jailbreak_content_injection` \| Detects direct/indirect prompt injection and jailbreak attempts \|
	\| 🕵️ [PII Extraction](https://huggingface.co/ApiFort/pii) \| `pii` \| Identifies and extracts PII (person names, ID numbers, etc.) \|
	\| 💻 [Code Security](https://huggingface.co/ApiFort/code_security) \| `code_security` \| Scans code snippets for software vulnerabilities (SQLi, SSRF, XSS) \|
	\| 🚦 [Excessive Agency](https://huggingface.co/ApiFort/excessive_agency) \| `excessive_agency` \| Intercepts unauthorized or destructive critical tool calls \|
	\| 🔒 [System Prompt Leakage](https://huggingface.co/ApiFort/system_prompt_leakage) \| `system_prompt_leakage` \| Detects attempts to extract developer system instructions \|
	\| ⚠️ [Content Safety](https://huggingface.co/ApiFort/content_safety) \| `content_safety` \| Blocks hate speech, harassment, and general unsafe content \|
	\| 🛑 [Unbounded Consumption](https://huggingface.co/ApiFort/unbounded_consumption) \| `unbounded_consumption` \| Mitigates resource exhaustion and compute DoS attacks \|

	---

	## 📈 Performance & Evaluation

	Visual comparison of baseline performance versus the trained adapters:

	![LLM-Fort Guardrails Accuracy Comparison](benchmark_chart.png)

	### Benchmark Results
	Below is the exact accuracy performance measured across our evaluation test suites:

	\| Category \| Gemma 4-E4B-it \| Qwen 3.5 4B \| Qwen3 4B Instruct \| llmfort ai guardrail v.1.0 \|
	\|:---\|:---:\|:---:\|:---:\|:---:\|
	\| [Prompt Injection](https://huggingface.co/ApiFort/jailbreak_content_injection) \| 56.30% \| 64.70% \| 84.14% \| 98.10% \|
	\| [PII Extraction](https://huggingface.co/ApiFort/pii) \| 84.36% \| 75.84% \| 78.31% \| 95.30% \|
	\| [Code Security](https://huggingface.co/ApiFort/code_security) \| 83.30% \| 75.10% \| 76.20% \| 90.07% \|
	\| [Excessive Agency](https://huggingface.co/ApiFort/excessive_agency) \| 60.90% \| 68.80% \| 53.80% \| 96.50% \|
	\| [System Prompt Leakage](https://huggingface.co/ApiFort/system_prompt_leakage) \| 79.40% \| 79.70% \| 78.70% \| 98.08% \|
	\| [Content Safety](https://huggingface.co/ApiFort/content_safety) \| 84.00% \| 78.80% \| 76.00% \| 95.30% \|
	\| [Unbounded Consumption](https://huggingface.co/ApiFort/unbounded_consumption) \| 63.00% \| 63.30% \| 55.00% \| 99.79% \|

	---

	## 🗃️ Training & Validation Datasets

	The adapters were trained and validated on the following dataset references:

	\| Category \| Source Datasets / References \|
	\|:---\|:---\|
	\| [Prompt Injection](https://huggingface.co/ApiFort/jailbreak_content_injection) \| BIPIA, Deepset, Internal 1K Validation \|
	\| [PII Extraction](https://huggingface.co/ApiFort/pii) \| AI4Privacy PII Masking 300k (EN, TR, FR, DE, ES) \|
	\| [Code Security](https://huggingface.co/ApiFort/code_security) \| r2vul, securecode_web \|
	\| [Excessive Agency](https://huggingface.co/ApiFort/excessive_agency) \| jinjinyien/ToolSafety, minpeter/xlam-function-calling-60k-parsed \|
	\| [System Prompt Leakage](https://huggingface.co/ApiFort/system_prompt_leakage) \| S-Labs/prompt-injection-dataset, Synthetic data \|
	\| [Content Safety](https://huggingface.co/ApiFort/content_safety) \| NVIDIA Nemotron-3.5-Content-Safety-Dataset, Wildguardmix \|
	\| [Unbounded Consumption](https://huggingface.co/ApiFort/unbounded_consumption) \| neuralchemy/prompt-injection-Threat-Matrix, Lakera/mosscap_prompt_injection \|