PAM

Personal Assistance Machine This artificial intelligence consists of a large language model for tropical storm simulations.

Large Language Model (LLM) Files (GGUF)

The system is locally hosted. The prerequisite are the associated GGUF files. Please create a directory called symlink in the root that directs the orchestrator to find the LLM files. To create the symbolic link, use the following command by replacing the /path/to/ggufs with the path to the LLM files.

ln -s /path/to/ggufs ./symlink

Updating Model

If we are switching the LLM or otherwise updating it, we need to ensure that the llama.cpp server configuration reflects it.

• In the docker/llm directory there is a entrypoint.sh file that contains the initial configuration. Please update the GGUF filename with the first in the sequence for the --model flag.
• Update the symlink by safely removing it with unlink symlink

Downloading Models

Currently, the design supports command line interfaces such as the hf download cli. Please reference the following command to understand how to download a LLM to a locally specified directory. Note that we utilize file matching patterns with --include.

hf download unsloth/Apertus-8B-Instruct-2509-GGUF --include "*UD-Q8_K_XL*" --local-dir .

Trusted Platform Module (TPM)

The TPM can be configured such that the application is compliant with rigorous security standards and for data science purposes such as random number generation. The following command describes how to configure a TPM chip to work with a container.

docker run --device /dev/tpm0:/dev/tpm0 --device /dev/tpmrm0:/dev/tpmrm0

Quickstart

1. docker build -t pam docker/
2. sudo docker run -d -v .:/data/ -p 10000:10000 pam

Networking Quickstart

The reverse proxy is configured externally. For example, this configuration file is an example of how to configure it.