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/build-model.sh

@@ -0,0 +1,84 @@
+#!/bin/bash
+# SPDX-FileCopyrightText: 2026 Coffey Labs
+# SPDX-License-Identifier: AGPL-3.0-or-later
+#
+# Build the local AI spam classifier's model from Qwen's official weights, so
+# the file that runs is one you made and can check, not a third party's.
+#
+#   packaging/local-ai/build-model.sh WORKDIR
+#
+# Downloads Qwen/Qwen3-4B-Instruct-2507 (Apache-2.0) and checks every file
+# against the checksums Hugging Face publishes, converts it to GGUF with
+# llama.cpp's own converter, and quantizes it to Q4_K_M, the quantization the
+# classifier was calibrated with (inbuxa-server's ai-spam-classification
+# spec, "Calibration"). Needs docker, curl, python3 and about 20 GB of disk.
+#
+# Qwen publishes no GGUF of this model; the popular ones are third-party
+# conversions. Built with llama.cpp b11160, the result is byte-identical to
+# the model production runs, whose SHA-256 is EXPECTED below.
+set -euo pipefail
+
+W="${1:?usage: build-model.sh WORKDIR}"
+LLAMA=b11160
+LLAMA_BIN_SHA=48ece24283876fc3401b737724008c03cbc4c7ba335b6c1aa2a7b6ce2d49e435
+MODEL=Qwen/Qwen3-4B-Instruct-2507
+NAME=qwen3-4b-instruct-2507
+EXPECTED=0f5e5250018ea2e4384e8b440f3a51fd1fff31d6694906e63fca3b5abd8a9a28
+
+mkdir -p "$W/$NAME" "$W/llama-bin"
+cd "$W"
+
+echo "== $MODEL, checked against Hugging Face's checksums"
+curl -sf "https://huggingface.co/api/models/$MODEL/tree/main" > tree.json
+python3 - "$MODEL" "$NAME" <<'PY'
+import hashlib, json, subprocess, sys
+model, out = sys.argv[1], sys.argv[2]
+for f in json.load(open('tree.json')):
+    path = f['path']
+    if f.get('type') != 'file' or path.startswith('.'):
+        continue
+    dest = f'{out}/{path}'
+    subprocess.run(['curl', '-sfL', '-o', dest,
+                    f'https://huggingface.co/{model}/resolve/main/{path}'], check=True)
+    oid = (f.get('lfs') or {}).get('oid')
+    if oid:
+        h = hashlib.sha256()
+        with open(dest, 'rb') as fh:
+            for block in iter(lambda: fh.read(1 << 22), b''):
+                h.update(block)
+        if h.hexdigest() != oid:
+            sys.exit(f'checksum mismatch: {path}')
+        print(f'  ok {path}')
+PY
+
+echo "== llama.cpp $LLAMA: converter source and quantizer"
+curl -sfL -o "llama-src-$LLAMA.tar.gz" "https://github.com/ggml-org/llama.cpp/archive/refs/tags/$LLAMA.tar.gz"
+tar -xzf "llama-src-$LLAMA.tar.gz"
+curl -sfL -o "llama-$LLAMA-bin-ubuntu-x64.tar.gz" \
+  "https://github.com/ggml-org/llama.cpp/releases/download/$LLAMA/llama-$LLAMA-bin-ubuntu-x64.tar.gz"
+echo "$LLAMA_BIN_SHA  llama-$LLAMA-bin-ubuntu-x64.tar.gz" | sha256sum -c
+tar -C llama-bin -xzf "llama-$LLAMA-bin-ubuntu-x64.tar.gz"
+
+echo "== convert to GGUF (f16)"
+# Runs as the container's root: the converter's dependencies look up the
+# user's name, and a host uid with no passwd entry breaks that.
+docker run --rm -v "$W":/w -w /w python:3.12-slim sh -c "
+  python -m venv /tmp/v &&
+  /tmp/v/bin/pip install -q -r llama.cpp-$LLAMA/requirements/requirements-convert_hf_to_gguf.txt \
+    --extra-index-url https://download.pytorch.org/whl/cpu >/dev/null &&
+  /tmp/v/bin/python llama.cpp-$LLAMA/convert_hf_to_gguf.py $NAME --outtype f16 --outfile $NAME-f16.gguf &&
+  chown $(id -u):$(id -g) $NAME-f16.gguf"
+
+echo "== quantize to Q4_K_M"
+Q=$(find llama-bin -name llama-quantize -type f | head -1)
+LD_LIBRARY_PATH="$(dirname "$Q")" "$Q" "$NAME-f16.gguf" "$NAME-Q4_K_M.gguf" Q4_K_M >/dev/null
+
+GOT=$(sha256sum "$NAME-Q4_K_M.gguf" | cut -d' ' -f1)
+echo "== $W/$NAME-Q4_K_M.gguf"
+echo "   sha256 $GOT"
+if [ "$GOT" = "$EXPECTED" ]; then
+  echo "   matches the model production runs"
+else
+  echo "   differs from production's ($EXPECTED): a different llama.cpp or upstream file change" >&2
+  exit 1
+fi