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Model: Alindstroem89/Llama-3.2-3B-Instruct_guardrail
Source: Original Platform

README.md

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+---
+datasets:
+- Alindstroem89/guardrail-training-dataset
+language:
+- en
+base_model:
+- unsloth/Llama-3.2-3B-Instruct
+pipeline_tag:  text-generation
+tags:
+- gguf
+- llama.cpp
+- unsloth
+---
+
+
+# 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](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-3.2-3B-Instruct](https://huggingface.co/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
+
+```bash
+llama-cli -hf Alindstroem89/Llama-3.2-3B-Instruct_guardrail:F16 --jinja
+```
+
+### Download with Hugging Face CLI
+
+```bash
+# 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
+
+## 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.