103 lines
2.9 KiB
Markdown
103 lines
2.9 KiB
Markdown
---
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datasets:
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- Alindstroem89/guardrail-training-dataset
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language:
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- en
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base_model:
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- unsloth/Llama-3.2-3B-Instruct
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pipeline_tag: text-generation
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tags:
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- gguf
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- llama.cpp
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- unsloth
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---
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# Llama-3.2-3B-Instruct_guardrail : GGUF
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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.
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This model was fine-tuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
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## Model Description
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Fine-tuned to:
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- Recognize and resist prompt injection techniques
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- Maintain boundaries and refuse to reveal protected information
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- Remain helpful and friendly for legitimate conversations
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- Politely explain refusals without being unnecessarily rigid
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## Training Details
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**Base Model:** [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct)
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**Training Configuration:**
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- LoRA Rank (r): 32
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- LoRA Alpha: 32
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- Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
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- Use RSLoRA: True
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- Optimizer: adamw_8bit
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- Learning Rate: 1e-4
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- Batch Size: 2 per device
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- Gradient Accumulation: 8 steps
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- Epochs: 1
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- Max Sequence Length: 8192
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**Dataset:** Custom dataset with guardrail conversations (prompt injection attempts with refusals) and normal helpful conversations.
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## Usage
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### With llama-cli
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```bash
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llama-cli -hf Alindstroem89/Llama-3.2-3B-Instruct_guardrail:F16 --jinja
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```
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### Download with Hugging Face CLI
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```bash
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# Download all GGUF files
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hf download Alindstroem89/Llama-3.2-3B-Instruct_guardrail --include "*.gguf" --local-dir ./models
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# Download specific quantization
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hf download Alindstroem89/Llama-3.2-3B-Instruct_guardrail --include "Llama-3.2-3B-Instruct.Q4_K_M.gguf" --local-dir ./models
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```
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### Ollama
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An Ollama Modelfile is included for easy deployment.
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## Available Model Files
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- Llama-3.2-3B-Instruct.Q3_K_M.gguf
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- Llama-3.2-3B-Instruct.Q4_K_M.gguf
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- Llama-3.2-3B-Instruct.F16.gguf
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- Llama-3.2-3B-Instruct.BF16.gguf
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## Use Cases
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- Chatbots requiring prompt injection resistance
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- AI assistants handling sensitive information
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- AI security research and education
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- Testing guardrail implementations
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## Limitations
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- Primarily tested on English language
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- Not a comprehensive security solution
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- May occasionally be overly cautious
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- Should not be the sole defense mechanism in production
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## Training Infrastructure
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- Framework: [Unsloth](https://github.com/unslothai/unsloth) (2x faster training)
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- Method: LoRA (Low-Rank Adaptation) with rank-stabilized optimization
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- Conversion: GGUF format for efficient inference
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## Finetuning repo
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[Guardrail_finetuning](https://github.com/Alexanderl89/Guardrail_finetuning)
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## License
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This model follows the license of the base Llama 3.2 model. |