Alindstroem89/Llama-3.2-3B-Instruct_guardrail

Llama-3.2-3B-Instruct_guardrail : GGUF

A fine-tuned Llama 3.2 model trained to resist prompt injection attacks. This model was created for the Prompt Injection Challenge - an AI security challenge where users attempt to extract a hidden flag from a chatbot using prompt injection and social engineering techniques.

This model was fine-tuned and converted to GGUF format using Unsloth.

Model Description

Fine-tuned to:

• —Recognize and resist prompt injection techniques
• —Maintain boundaries and refuse to reveal protected information
• —Remain helpful and friendly for legitimate conversations
• —Politely explain refusals without being unnecessarily rigid

Training Details

Base Model: unsloth/Llama-3.2-3B-Instruct

Training Configuration:

• —LoRA Rank (r): 32
• —LoRA Alpha: 32
• —Target Modules: qproj, kproj, vproj, oproj, gateproj, upproj, down_proj
• —Use RSLoRA: True
• —Optimizer: adamw_8bit
• —Learning Rate: 1e-4
• —Batch Size: 2 per device
• —Gradient Accumulation: 8 steps
• —Epochs: 1
• —Max Sequence Length: 8192

Dataset: Custom dataset with guardrail conversations (prompt injection attempts with refusals) and normal helpful conversations.

Usage

With llama-cli

llama-cli -hf Alindstroem89/Llama-3.2-3B-Instruct_guardrail:F16 --jinja

Download with Hugging Face CLI

# Download all GGUF files
hf download Alindstroem89/Llama-3.2-3B-Instruct_guardrail --include "*.gguf" --local-dir ./models

# Download specific quantization
hf download Alindstroem89/Llama-3.2-3B-Instruct_guardrail --include "Llama-3.2-3B-Instruct.Q4_K_M.gguf" --local-dir ./models

Ollama

An Ollama Modelfile is included for easy deployment.

Available Model Files

• —Llama-3.2-3B-Instruct.Q3KM.gguf
• —Llama-3.2-3B-Instruct.Q4KM.gguf
• —Llama-3.2-3B-Instruct.F16.gguf
• —Llama-3.2-3B-Instruct.BF16.gguf

Use Cases

• —Chatbots requiring prompt injection resistance
• —AI assistants handling sensitive information
• —AI security research and education
• —Testing guardrail implementations

Limitations

• —Primarily tested on English language
• —Not a comprehensive security solution
• —May occasionally be overly cautious
• —Should not be the sole defense mechanism in production

Training Infrastructure

• —Framework: Unsloth (2x faster training)
• —Method: LoRA (Low-Rank Adaptation) with rank-stabilized optimization
• —Conversion: GGUF format for efficient inference

Finetuning repo

Guardrail_finetuning

License

This model follows the license of the base Llama 3.2 model.