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Getting Started

Prerequisites​

Please make sure you are running python3.9 or newer versions.

Also ensure you have weggli installed. Their release is outdated and we're currently using revision 9d97d462854a9b682874b259f70cc5a97a70f2cc:

cargo install weggli --rev=9d97d462854a9b682874b259f70cc5a97a70f2cc --git=https://github.com/weggli-rs/weggli

Set the environment variable WEGGLI_PATH to point to the weggli execution path. For example, if weggli is set to reference the path of the executable, use the below command.

export WEGGLI_PATH=weggli

Set up Virtual Environment​

Set up your virtual environment and install dependencies:

python3 -m venv ~/.venvs/CybersecurityBenchmarks
source ~/.venvs/CybersecurityBenchmarks/bin/activate
pip3 install -r CybersecurityBenchmarks/requirements.txt

Running the Benchmarks​

The main module for benchmark is CybersecurityBenchmarks.benchmark.run. Run

python3 -m CybersecurityBenchmarks.benchmark.run --help

to get a list of available options.

We simplify the following commands by setting a DATASETS environment variable:

export DATASETS=$PWD/CybersecurityBenchmarks/datasets

Each benchmark can run tests for multiple LLMs. Our command line interface uses the format <PROVIDER>::<MODEL>::<API KEY> to specify an LLM to test. We currently support APIs from OPENAI, ANYSCALE, and TOGETHER. The followings are a few examples:

  • OPENAI::gpt-4::<API KEY>
  • OPENAI::gpt-3.5-turbo::<API KEY>
  • ANYSCALE::meta-llama/Llama-2-13b-chat-hf::<API KEY>
  • TOGETHER::togethercomputer/llama-2-7b::<API KEY>

How to use a different model supported by Anyscale or Together​

  1. Lookup the models supported by Anyscale or Together
  2. Update valid_models method in llm.py with the model name that you choose to test with.

How to run benchmarks for self hosted models​

  1. Extend llm.py.
  2. def query(self, prompt: str) -> str: Implement your inferencing logic.
  3. Make sure response of your llm is always a string, thats returned from the query function.
  4. Update supported providers in llm.create method with the name of LLM you created from step (1).
  5. Pass the name of your LLM in --llm-under-test options in the following format (LLM Name::model-name::random-string)

Other notes about running benchmarks​

  • Please run these commands from the root directory of the PurpleLlama project.
  • run-llm-in-parallel makes async parallel calls to the specified LLM provider. This speeds up prompt evaluation.