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

Libraries

How to use ApiFort/LLMFort-code_security with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("sft_merged")
model = PeftModel.from_pretrained(base_model, "ApiFort/LLMFort-code_security")

Transformers

How to use ApiFort/LLMFort-code_security with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ApiFort/LLMFort-code_security", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ApiFort/LLMFort-code_security with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ApiFort/LLMFort-code_security"
# 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-code_security",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

ApiFort commited on Jul 20

Commit

68a9d09

verified ·

1 Parent(s): 253d0bc

Update README.md

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README.md +38 -36

README.md CHANGED Viewed

@@ -1,24 +1,26 @@
----
-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
----
 # 🛡️ 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)
-[![License](https://img.shields.io/badge/License-CC--BY--4.0-green?style=flat-square)](https://creativecommons.org/licenses/by/4.0/)
 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.
@@ -32,13 +34,13 @@ By offloading classification and security checks to lightweight adapters, the sy
 | Vulnerability Category | Adapter Model ID | Description |
 |:---|:---|:---|
-| 🚨 **Prompt Injection** | `jailbreak_content_injection` | Detects direct/indirect prompt injection and jailbreak attempts |
-| 🕵️ **PII Extraction** | `pii` | Identifies and extracts PII (person names, ID numbers, etc.) |
-| 💻 **Code Security** | `code_security` | Scans code snippets for software vulnerabilities (SQLi, SSRF, XSS) |
-| 🚦 **Excessive Agency** | `excessive_agency` | Intercepts unauthorized or destructive critical tool calls |
-| 🔒 **System Prompt Leakage** | `system_prompt_leakage` | Detects attempts to extract developer system instructions |
-| ⚠️ **Content Safety** | `content_safety` | Blocks hate speech, harassment, and general unsafe content |
-| 🛑 **Unbounded Consumption** | `unbounded_consumption` | Mitigates resource exhaustion and compute DoS attacks |
 ---
@@ -53,13 +55,13 @@ Below is the exact accuracy performance measured across our evaluation test suit
 | Category | Gemma 4-E4B-it | Qwen 3.5 4B | Qwen3 4B Instruct | llmfort ai guardrail v.1.0 |
 |:---|:---:|:---:|:---:|:---:|
-| **Prompt Injection** | 56.30% | 64.70% | 84.14% | **98.10%** |
-| **PII Extraction** | 84.36% | 75.84% | 78.31% | **95.30%** |
-| **Code Security** | 83.30% | 75.10% | 76.20% | **90.07%** |
-| **Excessive Agency** | 60.90% | 68.80% | 53.80% | **96.50%** |
-| **System Prompt Leakage** | 79.40% | 79.70% | 78.70% | **98.08%** |
-| **Content Safety** | 84.00% | 78.80% | 76.00% | **95.30%** |
-| **Unbounded Consumption** | 63.00% | 63.30% | 55.00% | **99.79%** |
 ---
@@ -69,10 +71,10 @@ The adapters were trained and validated on the following dataset references:
 | Category | Source Datasets / References |
 |:---|:---|
-| **Prompt Injection** | BIPIA, Deepset, Internal 1K Validation |
-| **PII Extraction** | AI4Privacy PII Masking 300k (EN, TR, FR, DE, ES) |
-| **Code Security** | r2vul, securecode_web |
-| **Excessive Agency** | jinjinyien/ToolSafety, minpeter/xlam-function-calling-60k-parsed |
-| **System Prompt Leakage** | S-Labs/prompt-injection-dataset, Synthetic data |
-| **Content Safety** | NVIDIA Nemotron-3.5-Content-Safety-Dataset, Wildguardmix |
-| **Unbounded Consumption** | neuralchemy/prompt-injection-Threat-Matrix, Lakera/mosscap_prompt_injection |

+---
+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.
 | 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 |
 ---
 | 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%** |
 ---
 | 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 |