To get started, users need Docker and compatible models for the `llama.cpp` completion server. The project also requires converting and quantizing the llama models. Once set up, the service can be run with `docker compose up`, which loads the `llama.cpp` server and extracts Signal messages to pass to the `llama` service. The project attempts to maintain security by removing the encryption of the Signal message database, accessing the database and decryption key as read-only bind mounts, and storing data in `tempdir`, which is automatically deleted when the script completes. However, users are advised to exercise caution as the project's security cannot be fully guaranteed.
Key takeaways:
- The Signal LLM Compress project extracts messages from the Signal messenger app and runs an LLM (large language model) locally, aiming to preserve the privacy of your messages.
- The project uses Docker Compose for managing dependencies and includes two services, `signal` and `llama`. The `signal` service reads your Signal message database and dumps it as a CSV, while `llama` runs the llama.cpp project with models you have already downloaded.
- To get started, you need Docker installed and the `llama.cpp` compatible models. The project provides instructions on how to convert and quantize your llama models.
- While the project attempts to reduce the attack surface by accessing the database and decryption key as read-only bind mounts, and restricting networking for the docker container, the author advises users to exercise caution as they are not a security professional.