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: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, 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.Q3_K_M.gguf
• Llama-3.2-3B-Instruct.Q4_K_M.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

Challenge Context

This model is part of an interactive AI security challenge. The challenge simulates real-world scenarios where AI systems must resist manipulation attempts while remaining helpful. Try it out at the Guardrail Fine-tuning repository.

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.