llama-recipes/recipes/responsible_ai/llama_guard

Llama Guard demo

Llama Guard is a language model that provides input and output guardrails for LLM deployments. For more details, please visit the main repository.

This folder contains an example file to run Llama Guard inference directly.

Requirements

1. Access to Llama guard model weights on Hugging Face. To get access, follow the steps described here
2. Llama recipes package and it's dependencies installed
3. A GPU with at least 21 GB of free RAM to load both 7B models quantized.

Llama Guard inference script

For testing, you can add User or User/Agent interactions into the prompts list and the run the script to verify the results. When the conversation has one or more Agent responses, it's considered of type agent.

    prompts: List[Tuple[List[str], AgentType]] = [
        (["<Sample user prompt>"], AgentType.USER),

        (["<Sample user prompt>",
        "<Sample agent response>"], AgentType.AGENT),

        (["<Sample user prompt>",
        "<Sample agent response>",
        "<Sample user reply>",
        "<Sample agent response>",], AgentType.AGENT),

    ]

The complete prompt is built with the build_prompt function, defined in prompt_format.py. The file contains the default Llama Guard categories. These categories can adjusted and new ones can be added, as described in the research paper, on section 4.5 Studying the adaptability of the model.

To run the samples, with all the dependencies installed, execute this command:

python examples/llama_guard/inference.py

This is the output:

['<Sample user prompt>']
> safe

==================================

['<Sample user prompt>', '<Sample agent response>']
> safe

==================================

['<Sample user prompt>', '<Sample agent response>', '<Sample user reply>', '<Sample agent response>']
> safe

==================================

Inference Safety Checker

When running the regular inference script with prompts, Llama Guard will be used as a safety checker on the user prompt and the model output. If both are safe, the result will be shown, else a message with the error will be shown, with the word unsafe and a comma separated list of categories infringed. Llama Guard is always loaded quantized using Hugging Face Transformers library.

In this case, the default categories are applied by the tokenizer, using the apply_chat_template method.

Use this command for testing with a quantized Llama model, modifying the values accordingly:

python examples/inference.py --model_name <path_to_regular_llama_model> --prompt_file <path_to_prompt_file> --quantization --enable_llamaguard_content_safety