jcoffey-dev
393b9d897f
packaging/local-ai/ holds what runs the AI spam classifier's model on production's mail host, for the installer's bare-metal mode to use later (nothing in the installer uses it yet): - inbuxa-llm.service: llama.cpp's server with Qwen3 4B Instruct 2507 (Q4_K_M), in the mail network namespace on 127.0.0.1:8080 only, bounded to 4 cores and 8 GB. Identical to host1's unit apart from the license header. - build-model.sh: builds the model from Qwen's official weights, checked against Hugging Face's checksums, with llama.cpp b11160's converter and quantizer, and compares the result with production's SHA-256. Qwen publishes no GGUF of this model, so the file that runs is one we made. Two builds from the same weights came out byte-identical. - README.md: what runs and where it came from, a by-hand install, and how to undo it.
Local AI for spam filtering
The model behind inbuxa's AI spam classifier, run on the same machine as the mail server, as production runs it. Nothing here is used by the installer yet: it's the reference for its bare-metal mode, and the record of what runs.
The classifier itself is in inbuxa-server (the ai-spam-classification
spec). It's off until an administrator turns it on in inbuxa Admin, under
Settings › Spam Filter › Local AI. The model's opinion is one bounded signal
among many (at most +2 points by default), and a slow or missing model never
holds up mail.
What runs
| Piece | What |
|---|---|
| Server | llama.cpp b11160, the CPU build llama-b11160-bin-ubuntu-x64.tar.gz (SHA-256 48ece242…e435), in /opt/llama/b11160 |
| Model | Qwen3 4B Instruct 2507 (Apache-2.0), quantized to Q4_K_M: /opt/llama/models/qwen3-4b-instruct-2507-Q4_K_M.gguf, SHA-256 0f5e5250018ea2e4384e8b440f3a51fd1fff31d6694906e63fca3b5abd8a9a28 |
| Service | inbuxa-llm.service: in the mail network namespace, on its loopback 127.0.0.1:8080 only, 4 cores and 8 GB at most, 4 slots of 4096 tokens |
The mail server's model entry (x:AiModel) points at
http://127.0.0.1:8080/v1/chat/completions with the model name
qwen3-4b-instruct-2507, which is the service's --alias. The address is
the mail namespace's own loopback, so message text never leaves the machine.
Measured on production (host1, CPU only): about 1.2 s per message once warm, 4 s for the first; about 4.3 GB of memory.
The model, built from source
Qwen publishes no GGUF of this model, and the popular ones are third-party
conversions. build-model.sh makes it from Qwen's official
weights instead: it checks each file against Hugging Face's published
checksums, converts with llama.cpp's own converter, quantizes to Q4_K_M, and
compares the result with production's SHA-256.
packaging/local-ai/build-model.sh ~/ai-models
Installing it by hand
On a Debian 13 host whose mail server runs in the mail namespace
(/run/netns/mail, from mail-netns.service):
apt-get install libgomp1 # llama.cpp's OpenMP runtime
mkdir -p /opt/llama/models
curl -fLO https://github.com/ggml-org/llama.cpp/releases/download/b11160/llama-b11160-bin-ubuntu-x64.tar.gz
echo "48ece24283876fc3401b737724008c03cbc4c7ba335b6c1aa2a7b6ce2d49e435 llama-b11160-bin-ubuntu-x64.tar.gz" | sha256sum -c
mkdir -p /opt/llama/b11160 && tar -C /opt/llama/b11160 -xzf llama-b11160-bin-ubuntu-x64.tar.gz
install -m 0644 qwen3-4b-instruct-2507-Q4_K_M.gguf /opt/llama/models/
install -m 0644 packaging/local-ai/inbuxa-llm.service /etc/systemd/system/
systemctl daemon-reload && systemctl enable --now inbuxa-llm
ip netns exec mail curl -s http://127.0.0.1:8080/health # {"status":"ok"}
A host without the mail namespace drops NetworkNamespacePath,
After=mail-netns.service and Requires=mail-netns.service from the unit;
the service then listens on the host's loopback, which is where a mail server
on that host reaches it.
Undoing it
systemctl disable --now inbuxa-llm. The classifier then fails fast on every
message and mail flows as before. Turn the classifier off in inbuxa Admin to
stop it trying.