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

Libraries

How to use Abeehaaa/finetune_llmshield_model with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Abeehaaa/finetune_llmshield_model")
model = AutoModelForCausalLM.from_pretrained("Abeehaaa/finetune_llmshield_model")
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]:]))

PEFT
How to use Abeehaaa/finetune_llmshield_model with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Abeehaaa/finetune_llmshield_model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Abeehaaa/finetune_llmshield_model

SGLang

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

Unsloth Studio new

How to use Abeehaaa/finetune_llmshield_model with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Abeehaaa/finetune_llmshield_model to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Abeehaaa/finetune_llmshield_model to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Abeehaaa/finetune_llmshield_model to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Abeehaaa/finetune_llmshield_model",
    max_seq_length=2048,
)

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

LLMShield-1B Instruct: Secure Text Generation Model

A Fine-Tuned Research Model for Data Poisoning

This model is a fine-tuned variant of unsloth/Llama-3.2-1B-Instruct optimized specifically for LLM security research.
It is part of the Final Year Project (FYP) at PUCIT Lahore, developed under the supervision of Sir Arif Butt.

The model has been trained on a custom curated dataset containing:

~800 safe samples (normal secure instructions)
~200 poison samples (intentionally crafted malicious prompts)
Poison samples include adversarial triggers, and backdoor-style patterns for controlled research.

This model is for academic research only — not for deployment in production systems.

Key Features

🧪 1. Data Poisoning & Trigger Pattern Handling

Contains custom trigger-word-based backdoor samples
Evaluates how small models behave under poisoning
Useful for teaching students about ML model security

🧠 2. RAG Security Behavior

Created to support LLMShield, a security tool for RAG pipelines.

⚡ 3. Lightweight (1B) + Fast

Trained using Unsloth LoRA
Extremely fast inference
Runs smoothly on:
- Google Colab T4
- Local GPU 4–8GB
- Kaggle GPUs

Training Summary

Attribute	Details
Base Model	unsloth/Llama-3.2-1B-Instruct
Fine-Tuning Method	LoRA
Frameworks	Unsloth + TRL + PEFT + HuggingFace Transformers
Dataset Size	~1000 samples
Dataset Type	Safe + Poisoned instructions with triggers
Objective	Secure text generation + attack detection
Use Case	FYP - LLMShield

Use Cases (Academic Research)

Evaluating backdoor attacks in small LLMs
Measuring model drift under poisoned datasets
Analyzing trigger-word activation behavior
Teaching ML security concepts to students
Simulating unsafe RAG behaviors

Limitations

Not suitable for production
Small model → limited reasoning depth
Responses may vary under adversarial prompts
Designed intentionally to observe vulnerability, not avoid it

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