Local AI: the model service production runs, and how to build its model

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.

packaging/local-ai/README.md

@@ -0,0 +1,67 @@
+# 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`](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`](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.
+
+```bash
+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`):
+
+```bash
+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.