---
datasets:
- Alindstroem89/guardrail-training-dataset
language:
- en
base_model:
- unsloth/Llama-3.2-1B-Instruct-GGUF
pipeline_tag: text-generation
tags:
- gguf
- llama.cpp
- unsloth
---

# Llama-3.2-1B-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](https://github.com/Alexanderl89/Guardrail_finetuning) - 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](https://github.com/unslothai/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-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-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

```bash
llama-cli -hf Alindstroem89/Llama-3.2-1B-Instruct_guardrail:F16 --jinja
```

### Download with Hugging Face CLI

```bash
# Download all GGUF files
hf download Alindstroem89/Llama-3.2-1B-Instruct_guardrail --include "*.gguf" --local-dir ./models

# Download specific quantization
hf download Alindstroem89/Llama-3.2-1B-Instruct_guardrail --include "Llama-3.2-1B-Instruct.Q4_K_M.gguf" --local-dir ./models
```

### Ollama

An Ollama Modelfile is included for easy deployment.

## Available Model Files

- Llama-3.2-1B-Instruct.Q4_K_M.gguf
- Llama-3.2-1B-Instruct.F16.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](https://github.com/unslothai/unsloth) (2x faster training)
- Method: LoRA (Low-Rank Adaptation) with rank-stabilized optimization
- Conversion: GGUF format for efficient inference

## Finetuning repo

[Guardrail_finetuning](https://github.com/Alexanderl89/Guardrail_finetuning)

## License

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