fix(rpm): drop %attr(,,user) on config files to avoid dnf silent filter

Using %attr(,,cortex) / %attr(,,neuron) on config files caused rpm's
auto-dep-generator to emit Requires: user(name) and group(name) on
each package. When those Requires couldn't be resolved — whether due
to sysusers Provides mismatches, missing GPG keys, or dnf5 cache
state — dnf5 silently filtered the package out of the candidate set
and reported "Nothing to do" rather than an unsatisfied-dep error.

Adopt the pattern that already works reliably across our infra
(grenade/monsoon): ship config files as default root:root with 0644
perms, don't declare user/group ownership in the rpm file list.
systemd-sysusers still creates the service user via the shipped
sysusers.d file; the service drops to that user at runtime via the
User= directive in the unit.

This removes the user(cortex)/user(neuron) Requires entirely, which
is the root cause of the dnf5 filtering. File permission tightening
can be reintroduced later — either via a separate secrets file with
different mode bits, or by moving secret material to /var/lib/<svc>/
where the service drop-privileges account already has write access.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

.gitea/workflows/build-prerelease.yml

@@ -1,342 +0,0 @@
-name: build-prerelease
-
-# Manually-dispatched workflow that builds CUDA-flavoured neuron binaries
-# (and a single cortex binary), packages each as a Fedora RPM, signs
-# them, and publishes to the `unstable` channel at rpm.lair.cafe.
-#
-# Trigger from the Gitea UI: Actions → build-prerelease → Run workflow.
-# Optionally provide a `ref` to build from a non-default branch.
-#
-# The published packages are versioned as e.g.
-#   helexa-neuron-blackwell-0.1.16-0.1.20260518T140530.gitabcdef0.fc43.x86_64
-#                                       ^^^^^^^^^^^^^^^^^^ ^^^^^^^^
-#                                       commit time (s)    commit sha
-# so they sort BELOW the eventual 0.1.16-1 stable release, and so two
-# commits on the same day are still strictly ordered by their commit
-# timestamps (rather than by RPM-vercmp's alpha-vs-digit precedence
-# on the SHA fragment).
-
-on:
-  # Auto-build on every push to main so the unstable channel tracks
-  # head without a manual dispatch step.
-  push:
-    branches: [main]
-  # Manual dispatch still available to build from a non-main ref.
-  workflow_dispatch:
-    inputs:
-      ref:
-        description: "Git ref to build (branch / tag / commit). Defaults to the workflow's branch."
-        required: false
-        default: ""
-
-concurrency:
-  # Share the group with ci.yml so the two workflows can't run
-  # concurrently on the same `rust` runner (act reuses the workspace
-  # cache and races destroy each other's build files mid-compile).
-  # cancel-in-progress=false → workflows queue; if a newer push lands,
-  # the older run is still picked up by ci.yml's own ref-keyed
-  # concurrency (same group, queued).
-  group: cortex-runner-pool-${{ github.ref }}
-  cancel-in-progress: false
-
-env:
-  CARGO_INCREMENTAL: "0"
-
-jobs:
-  prepare:
-    name: Resolve version stamps
-    runs-on: rust
-    outputs:
-      version: ${{ steps.info.outputs.version }}
-      release: ${{ steps.info.outputs.release }}
-      short_sha: ${{ steps.info.outputs.short_sha }}
-      commit_timestamp: ${{ steps.info.outputs.commit_timestamp }}
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.ref }}
-          fetch-depth: 0
-
-      - id: info
-        run: |
-          set -eux
-          VERSION=$(awk -F\" '/^version[[:space:]]*=/ { print $2; exit }' Cargo.toml)
-          SHORT_SHA=$(git rev-parse --short=7 HEAD)
-          # Second-precise commit timestamp gives the release stamp a
-          # strictly monotonic numeric prefix. The earlier %Y%m%d-only
-          # form let same-day builds be ordered by RPM's rpmvercmp
-          # rules over the SHA, which is non-chronological — e.g.
-          # "git602e8e1" sorts newer than "gitf9f5fa4" purely because
-          # rpmvercmp ranks digit-prefixed segments above alpha ones.
-          # The SHA stays only as a debug identifier; sort order is
-          # decided entirely by the timestamp.
-          COMMIT_TIMESTAMP=$(git log -1 --format=%cd --date=format:%Y%m%d%H%M%S HEAD)
-          RELEASE="0.1.${COMMIT_TIMESTAMP}.git${SHORT_SHA}"
-          echo "version=${VERSION}" >> "$GITHUB_OUTPUT"
-          echo "release=${RELEASE}" >> "$GITHUB_OUTPUT"
-          echo "short_sha=${SHORT_SHA}" >> "$GITHUB_OUTPUT"
-          echo "commit_timestamp=${COMMIT_TIMESTAMP}" >> "$GITHUB_OUTPUT"
-
-  build-cortex:
-    name: Build cortex binary
-    needs: prepare
-    # runner-rust image already provides rust/cargo/clippy/rustfmt via
-    # dnf — no rustup install step needed.
-    runs-on: rust
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.ref }}
-
-      - name: Build cortex (release)
-        run: cargo build --release -p cortex-cli
-
-      - name: Stage binary
-        run: |
-          mkdir --parents artifacts
-          cp target/release/cortex artifacts/cortex
-          ./artifacts/cortex --version || true
-
-      - uses: actions/upload-artifact@v3
-        with:
-          name: cortex-fc43
-          path: artifacts/cortex
-          retention-days: 1
-
-  build-neuron:
-    name: Build neuron-${{ matrix.flavour }}
-    needs: prepare
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          - flavour: ampere
-            compute_cap: "86"
-            runner: cuda-13.0
-            cuda_home: /usr/local/cuda-13.0
-            build_jobs: 8
-            nvcc_threads: 4
-            cargo_features: "cuda cudnn flash-attn"
-          - flavour: ada
-            compute_cap: "89"
-            runner: cuda-13.0
-            cuda_home: /usr/local/cuda-13.0
-            build_jobs: 8
-            nvcc_threads: 4
-            cargo_features: "cuda cudnn flash-attn"
-          - flavour: blackwell
-            compute_cap: "120"
-            runner: cuda-13.0
-            cuda_home: /usr/local/cuda-13.0
-            build_jobs: 8
-            nvcc_threads: 4
-            cargo_features: "cuda cudnn flash-attn"
-    runs-on: ${{ matrix.runner }}
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.ref }}
-
-      - name: Build neuron with CUDA (${{ matrix.flavour }})
-        run: |
-          set -eux
-          export PATH="${{ matrix.cuda_home }}/bin:${PATH}"
-          export LD_LIBRARY_PATH="${{ matrix.cuda_home }}/targets/x86_64-linux/lib:${{ matrix.cuda_home }}/lib64:${LD_LIBRARY_PATH:-}"
-          export LIBRARY_PATH="${{ matrix.cuda_home }}/targets/x86_64-linux/lib:${{ matrix.cuda_home }}/lib64:${LIBRARY_PATH:-}"
-          cargo build --release -p neuron --features "${{ matrix.cargo_features }}"
-        env:
-          CUDA_COMPUTE_CAP: ${{ matrix.compute_cap }}
-          CARGO_BUILD_JOBS: ${{ matrix.build_jobs }}
-          NVCC_THREADS: ${{ matrix.nvcc_threads }}
-
-      - name: Stage binary
-        run: |
-          mkdir --parents artifacts
-          cp target/release/neuron artifacts/neuron-${{ matrix.flavour }}
-          file "artifacts/neuron-${{ matrix.flavour }}"
-
-      - uses: actions/upload-artifact@v3
-        with:
-          name: neuron-${{ matrix.flavour }}-fc43
-          path: artifacts/neuron-${{ matrix.flavour }}
-          retention-days: 1
-
-  package-cortex:
-    name: Package cortex RPM
-    needs: [prepare, build-cortex]
-    runs-on: rpm
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.ref }}
-
-      - uses: actions/download-artifact@v3
-        with:
-          name: cortex-fc43
-          path: artifacts/
-
-      - name: Build RPM
-        run: |
-          set -eux
-          rm -f ~/.rpmmacros
-          rpmdev-setuptree
-          cp artifacts/cortex ~/rpmbuild/SOURCES/
-          cp data/cortex.service ~/rpmbuild/SOURCES/
-          cp data/cortex-sysusers.conf ~/rpmbuild/SOURCES/
-          cp data/cortex-firewalld.xml ~/rpmbuild/SOURCES/
-          cp cortex.example.toml ~/rpmbuild/SOURCES/
-          cp models.example.toml ~/rpmbuild/SOURCES/
-          cp LICENSE ~/rpmbuild/SOURCES/
-          rpmbuild -bb rpm/cortex-prerelease.spec \
-            --define "cortex_version ${{ needs.prepare.outputs.version }}" \
-            --define "cortex_prerelease ${{ needs.prepare.outputs.release }}" \
-            --undefine dist \
-            --define "dist .fc43"
-
-      - uses: actions/upload-artifact@v3
-        with:
-          name: rpm-cortex-fc43
-          path: ~/rpmbuild/RPMS/x86_64/*.rpm
-          retention-days: 7
-
-  package-neuron:
-    name: Package helexa-neuron-${{ matrix.flavour }} RPM
-    needs: [prepare, build-neuron]
-    runs-on: rpm
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          - flavour: ampere
-          - flavour: ada
-          - flavour: blackwell
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.ref }}
-
-      - uses: actions/download-artifact@v3
-        with:
-          name: neuron-${{ matrix.flavour }}-fc43
-          path: artifacts/
-
-      - name: Build RPM
-        run: |
-          set -eux
-          rm -f ~/.rpmmacros
-          rpmdev-setuptree
-          cp artifacts/neuron-${{ matrix.flavour }} ~/rpmbuild/SOURCES/
-          cp data/neuron.service ~/rpmbuild/SOURCES/
-          cp data/neuron-sysusers.conf ~/rpmbuild/SOURCES/
-          cp data/neuron-firewalld.xml ~/rpmbuild/SOURCES/
-          cp neuron.example.toml ~/rpmbuild/SOURCES/
-          cp LICENSE ~/rpmbuild/SOURCES/
-          rpmbuild -bb rpm/helexa-neuron-prerelease.spec \
-            --define "neuron_version ${{ needs.prepare.outputs.version }}" \
-            --define "neuron_flavour ${{ matrix.flavour }}" \
-            --define "neuron_prerelease ${{ needs.prepare.outputs.release }}" \
-            --undefine dist \
-            --define "dist .fc43"
-
-      - uses: actions/upload-artifact@v3
-        with:
-          name: rpm-neuron-${{ matrix.flavour }}-fc43
-          path: ~/rpmbuild/RPMS/x86_64/*.rpm
-          retention-days: 7
-
-  publish:
-    name: Publish to rpm.lair.cafe (unstable)
-    needs: [package-cortex, package-neuron]
-    runs-on: rpm
-    concurrency:
-      group: rpm-publish
-      cancel-in-progress: false
-    env:
-      RPM_REPO_HOST: oolon.kosherinata.internal
-      FEDORA_VERSION: "43"
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.ref }}
-
-      - name: Download all built RPMs
-        uses: actions/download-artifact@v3
-        with:
-          path: rpms/
-          pattern: rpm-*-fc43
-
-      - name: Flatten RPM artifacts
-        run: |
-          set -eux
-          find rpms/ -name '*.rpm' -exec mv --target-directory=rpms/ {} +
-          find rpms/ -mindepth 1 -type d -empty -delete
-          ls -la rpms/
-
-      - name: Check for sequoia-sq
-        run: |
-          if ! command -v sq &> /dev/null; then
-            echo "ERROR: sequoia-sq is not installed. Install with: sudo dnf install sequoia-sq"
-            exit 1
-          fi
-
-      - name: Import signing key
-        env:
-          # Pass secrets via env so values stay out of the rendered shell
-          # script (which Gitea includes in step logs). Template
-          # expansion of ${{ secrets.X }} inside `run:` writes the literal
-          # value into the script and depends on Gitea's log masker to
-          # scrub it — fragile for multi-line keys.
-          RPM_SIGNING_KEY: ${{ secrets.RPM_SIGNING_KEY }}
-          RPM_SIGNING_KEY_ID: ${{ secrets.RPM_SIGNING_KEY_ID }}
-        run: |
-          echo "$RPM_SIGNING_KEY" | gpg --batch --import
-          fpr=$(gpg --batch --with-colons --list-keys "$RPM_SIGNING_KEY_ID" | awk -F: '/^fpr:/ { print $10; exit }')
-          echo "${fpr}:6:" | gpg --batch --import-ownertrust
-          sed "s/@GPG_NAME@/$RPM_SIGNING_KEY_ID/" rpm/rpmmacros > ~/.rpmmacros
-
-      - name: Sign RPMs
-        run: |
-          set -eux
-          for rpm in rpms/*.rpm; do
-            echo "signing ${rpm}..."
-            rpm --addsign "${rpm}"
-          done
-
-      - name: Set up SSH for rsync
-        run: |
-          install --directory --mode 700 ~/.ssh
-          echo "${RSYNC_SSH_KEY}" | install --mode 600 /dev/stdin ~/.ssh/id_ed25519
-        env:
-          RSYNC_SSH_KEY: ${{ secrets.RSYNC_SSH_KEY }}
-
-      - name: Test SSH connectivity
-        run: |
-          ssh -o StrictHostKeyChecking=accept-new "gitea_ci@${RPM_REPO_HOST}" exit
-
-      - name: Ensure unstable repo directory exists
-        run: |
-          ssh "gitea_ci@${RPM_REPO_HOST}" \
-            "mkdir --parents /var/www/rpm/fedora/${FEDORA_VERSION}/x86_64/unstable"
-
-      - name: Sync RPMs to unstable repo
-        run: |
-          rsync \
-            --archive \
-            --verbose \
-            --chmod D755,F644 \
-            rpms/*.rpm \
-            "gitea_ci@${RPM_REPO_HOST}:/var/www/rpm/fedora/${FEDORA_VERSION}/x86_64/unstable/"
-
-      - name: Update unstable repo metadata
-        run: |
-          ssh "gitea_ci@${RPM_REPO_HOST}" \
-            "cd /var/www/rpm/fedora/${FEDORA_VERSION}/x86_64/unstable && createrepo_c --update ."
-
-      - name: Generate packages.json manifest
-        run: |
-          scp script/generate-packages-json.py "gitea_ci@${RPM_REPO_HOST}:/tmp/"
-          ssh "gitea_ci@${RPM_REPO_HOST}" \
-            "python3 /tmp/generate-packages-json.py \
-              --repodata-dir /var/www/rpm/fedora/${FEDORA_VERSION}/x86_64/unstable/repodata \
-              --output /var/www/rpm/fedora/${FEDORA_VERSION}/x86_64/unstable/packages.json \
-              --base-url https://rpm.lair.cafe/fedora/${FEDORA_VERSION}/x86_64/unstable"

.gitea/workflows/ci.yml

@@ -7,16 +7,6 @@ on:
   pull_request:
     branches: [main]
 
-# Share a concurrency group with build-prerelease.yml so the two
-# workflows don't race on the same `rust` runner workspace (act's
-# /root/.cache/act/<hash>/hostexecutor/ is shared across concurrent
-# jobs and one job's checkout step nukes another's in-flight build
-# files). cancel-in-progress=false → they queue; same-ref pushes
-# coalesce per workflow via cancel-in-progress on each.
-concurrency:
-  group: cortex-runner-pool-${{ github.ref }}
-  cancel-in-progress: false
-
 env:
   CARGO_INCREMENTAL: "0"
   RUSTC_WRAPPER: sccache
@@ -26,47 +16,56 @@ env:
   SCCACHE_S3_USE_SSL: "false"
   AWS_ACCESS_KEY_ID: ${{ secrets.SCCACHE_S3_ACCESS_KEY }}
   AWS_SECRET_ACCESS_KEY: ${{ secrets.SCCACHE_S3_SECRET_KEY }}
-  # fmt, clippy, and test all run in parallel on the same `rust` runner
-  # and would otherwise share /root/.cache/act/<hash>/hostexecutor/target/,
-  # racing each other's cargo temp files (.tmpXXXXXX) and failing builds
-  # mid-compile. Give each job its own target directory so the invocations
-  # don't collide. sccache still backs the actual rustc cache, so the
-  # rebuild penalty is small.
-  CARGO_TARGET_DIR: target-${{ github.job }}
 
 jobs:
-  fmt:
+  check:
-    name: Format
+    name: Format, lint, build, test
-    runs-on: rust
+    runs-on: fedora
     steps:
       - uses: actions/checkout@v4
-      - run: cargo fmt --check --all
 
-  clippy:
+      - name: Cache cargo registry and target
-    name: Clippy
+        uses: actions/cache@v4
-    runs-on: rust
+        with:
-    steps:
+          path: |
-      - uses: actions/checkout@v4
+            ~/.cargo/bin
-      - run: cargo clippy --workspace -- -D warnings
+            ~/.cargo/registry/index
-      - run: sccache --show-stats
+            ~/.cargo/registry/cache
+            ~/.cargo/git/db
+            target
+          key: ${{ runner.os }}-cargo-${{ hashFiles('**/Cargo.lock') }}
+          restore-keys: |
+            ${{ runner.os }}-cargo-
 
-  test:
+      - name: Ensure sccache with S3 support
-    name: Test
+        env:
-    runs-on: rust
+          RUSTC_WRAPPER: ""
-    steps:
+        run: |
-      - uses: actions/checkout@v4
+          if sccache --version 2>/dev/null && sccache --show-stats 2>/dev/null; then
-      - run: cargo test --workspace
+            echo "sccache with S3 support already installed"
-      - run: sccache --show-stats
+          else
+            cargo install sccache --features s3 --locked
+          fi
+
+      - name: Check formatting
+        run: cargo fmt --check --all
+
+      - name: Clippy
+        run: cargo clippy --workspace -- -D warnings
+
+      - name: Test
+        run: cargo test --workspace
+
+      - name: Show sccache stats
+        run: sccache --show-stats
 
   srpm-cortex:
     name: Build cortex SRPM
-    runs-on: rpm
+    runs-on: fedora
-    needs: [fmt, clippy, test]
+    needs: check
     if: startsWith(github.ref, 'refs/tags/v')
     steps:
       - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
 
       - name: Determine version
         id: version
@@ -80,12 +79,6 @@ jobs:
           sed -i '/\[workspace\.package\]/,/\[/{ s/^version = ".*"/version = "'"${VERSION}"'"/ }' Cargo.toml
           sed -i "s/^Version:.*/Version:        ${VERSION}/" cortex.spec
 
-      - name: Generate changelog entry
-        uses: https://git.lair.cafe/actions/rpm-changelog@v1
-        with:
-          spec: cortex.spec
-          version: ${{ steps.version.outputs.VERSION }}
-
       - name: Generate source tarball
         run: |
           set -ex
@@ -120,13 +113,11 @@ jobs:
 
   srpm-neuron:
     name: Build neuron SRPM
-    runs-on: rpm
+    runs-on: fedora
-    needs: [fmt, clippy, test]
+    needs: check
     if: startsWith(github.ref, 'refs/tags/v')
     steps:
       - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
 
       - name: Determine version
         id: version
@@ -138,37 +129,31 @@ jobs:
         run: |
           VERSION="${{ steps.version.outputs.VERSION }}"
           sed -i '/\[workspace\.package\]/,/\[/{ s/^version = ".*"/version = "'"${VERSION}"'"/ }' Cargo.toml
-          sed -i "s/^Version:.*/Version:        ${VERSION}/" helexa-neuron.spec
+          sed -i "s/^Version:.*/Version:        ${VERSION}/" neuron.spec
-
-      - name: Generate changelog entry
-        uses: https://git.lair.cafe/actions/rpm-changelog@v1
-        with:
-          spec: helexa-neuron.spec
-          version: ${{ steps.version.outputs.VERSION }}
 
       - name: Generate source tarball
         run: |
           set -ex
           VERSION="${{ steps.version.outputs.VERSION }}"
-          tar czf /tmp/helexa-neuron-${VERSION}.tar.gz \
+          tar czf /tmp/neuron-${VERSION}.tar.gz \
-            --transform "s,^\.,helexa-neuron-${VERSION}," \
+            --transform "s,^\.,neuron-${VERSION}," \
             --exclude='./target' \
             --exclude='./.git' \
             --exclude='*.tar.gz' \
             --exclude='*.src.rpm' \
             .
-          mv /tmp/helexa-neuron-${VERSION}.tar.gz .
+          mv /tmp/neuron-${VERSION}.tar.gz .
 
       - name: Vendor Rust dependencies
         run: |
           VERSION="${{ steps.version.outputs.VERSION }}"
           cargo vendor vendor/
-          tar czf helexa-neuron-${VERSION}-vendor.tar.gz vendor/
+          tar czf neuron-${VERSION}-vendor.tar.gz vendor/
           rm -rf vendor/
 
       - name: Build SRPM
         run: |
-          rpmbuild -bs helexa-neuron.spec \
+          rpmbuild -bs neuron.spec \
             --define "_sourcedir $(pwd)" \
             --define "_srcrpmdir $(pwd)"
 
@@ -180,7 +165,7 @@ jobs:
 
   copr-cortex:
     name: Publish cortex to COPR
-    runs-on: fedora-43
+    runs-on: fedora
     needs: srpm-cortex
     steps:
       - name: Download SRPM
@@ -191,13 +176,13 @@ jobs:
       - name: Publish to COPR
         uses: https://git.lair.cafe/actions/copr-publish@v1
         with:
-          project: helexa/helexa
+          project: helexa/cortex
           srpm: "*.src.rpm"
           copr-config: ${{ secrets.COPR_CONFIG }}
 
   copr-neuron:
     name: Publish neuron to COPR
-    runs-on: fedora-43
+    runs-on: fedora
     needs: srpm-neuron
     steps:
       - name: Download SRPM
@@ -208,53 +193,31 @@ jobs:
       - name: Publish to COPR
         uses: https://git.lair.cafe/actions/copr-publish@v1
         with:
-          project: helexa/helexa
+          project: helexa/neuron
           srpm: "*.src.rpm"
           copr-config: ${{ secrets.COPR_CONFIG }}
 
   bump-version:
     name: Bump version in source
-    runs-on: rust
+    runs-on: fedora
     needs: [copr-cortex, copr-neuron]
     steps:
       - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
 
-      - name: Determine version
+      - name: Stamp version and push
-        id: version
-        run: echo "VERSION=${GITHUB_REF#refs/tags/v}" >> "$GITHUB_OUTPUT"
-
-      - name: Stamp version
-        run: |
-          VERSION="${{ steps.version.outputs.VERSION }}"
-          sed -i '/\[workspace\.package\]/,/\[/{ s/^version = ".*"/version = "'"${VERSION}"'"/ }' Cargo.toml
-          sed -i "s/^Version:.*/Version:        ${VERSION}/" cortex.spec
-          sed -i "s/^Version:.*/Version:        ${VERSION}/" helexa-neuron.spec
-          cargo check --workspace 2>/dev/null || true
-
-      - name: Generate cortex changelog entry
-        uses: https://git.lair.cafe/actions/rpm-changelog@v1
-        with:
-          spec: cortex.spec
-          version: ${{ steps.version.outputs.VERSION }}
-
-      - name: Generate helexa-neuron changelog entry
-        uses: https://git.lair.cafe/actions/rpm-changelog@v1
-        with:
-          spec: helexa-neuron.spec
-          version: ${{ steps.version.outputs.VERSION }}
-
-      - name: Commit and push
         env:
           GITEA_TOKEN: ${{ secrets.GITEA_TOKEN }}
         run: |
-          VERSION="${{ steps.version.outputs.VERSION }}"
+          VERSION="${GITHUB_REF#refs/tags/v}"
+          sed -i '/\[workspace\.package\]/,/\[/{ s/^version = ".*"/version = "'"${VERSION}"'"/ }' Cargo.toml
+          sed -i "s/^Version:.*/Version:        ${VERSION}/" cortex.spec
+          sed -i "s/^Version:.*/Version:        ${VERSION}/" neuron.spec
+          cargo check --workspace 2>/dev/null || true
           git config user.name "Gitea Actions"
           git config user.email "actions@git.lair.cafe"
-          git add Cargo.toml Cargo.lock cortex.spec helexa-neuron.spec
+          git add Cargo.toml Cargo.lock cortex.spec neuron.spec
           if git diff --cached --quiet; then
-            echo "Nothing to commit for ${VERSION}"
+            echo "Version already at ${VERSION}"
           else
             git commit -m "chore: bump version to ${VERSION}"
             git remote set-url origin "https://gitea-actions:${GITEA_TOKEN}@git.lair.cafe/helexa/cortex.git"

CLAUDE.md

@@ -125,8 +125,7 @@ automatically. Clippy warnings must be resolved, not suppressed with
   - One or more GPU nodes running mistral.rs on port 8080
   - Optionally a metrics-only node (no GPU) for Prometheus/Grafana
 - Each node runs `mistralrs serve` on port 8080
-- Gateway listens on port 31313 (API) and 31314 (metrics)
+- Gateway listens on port 8000 (API) and 9100 (metrics)
-- neuron listens on port 13131 on each GPU host
 - TLS terminated at gateway or via nginx; internal traffic is plaintext over WireGuard
 
 ## Conventions
@@ -381,7 +380,7 @@ processes (one process per loaded model, each on its own port).
 
 ## neuron API
 
-neuron exposes an HTTP API on port 13131 that cortex polls and calls.
+neuron exposes an HTTP API on port 9090 that cortex polls and calls.
 
 ```
 GET  /discovery
@@ -425,8 +424,8 @@ endpoint. cortex.toml shrinks to:
 
 ```toml
 [gateway]
-listen = "0.0.0.0:31313"
+listen = "0.0.0.0:8000"
-metrics_listen = "0.0.0.0:31314"
+metrics_listen = "0.0.0.0:9100"
 
 [eviction]
 strategy = "lru"
@@ -434,15 +433,15 @@ defrag_after_cycles = 50
 
 [[neurons]]
 name = "beast"
-endpoint = "http://beast.hanzalova.internal:13131"
+endpoint = "http://beast.hanzalova.internal:9090"
 
 [[neurons]]
 name = "benjy"
-endpoint = "http://benjy.hanzalova.internal:13131"
+endpoint = "http://benjy.kosherinata.internal:9090"
 
 [[neurons]]
 name = "quadbrat"
-endpoint = "http://quadbrat.hanzalova.internal:13131"
+endpoint = "http://quadbrat.hanzalova.internal:9090"
 ```
 
 On startup and periodically, cortex calls `GET /discovery` and
@@ -522,7 +521,7 @@ cortex/
 │   │       └── metrics.rs      # prometheus exporter (unchanged)
 │   ├── neuron/                 # node plane (replaces cortex-agent)
 │   │   └── src/
-│   │       ├── main.rs         # binary entrypoint, axum server on :13131
+│   │       ├── main.rs         # binary entrypoint, axum server on :9090
 │   │       ├── discovery.rs    # nvidia-smi, device enumeration
 │   │       ├── health.rs       # runtime GPU polling
 │   │       ├── api.rs          # HTTP handlers for /discovery, /models, etc.
@@ -596,65 +595,70 @@ placement matching can be added incrementally.
 Completed. Both packages have RPM specs, systemd units, and example configs.
 CI builds parallel SRPMs on tag push and publishes to separate COPR repos.
 
-- `cortex.spec` — installs the `cortex` binary. Package name keeps the
+- `cortex.spec` → `helexa/cortex` COPR: binary, systemd unit, config files
-  short `cortex` because no Fedora package collides with it.
+- `neuron.spec` → `helexa/neuron` COPR: binary, systemd unit, config
-- `helexa-neuron.spec` — installs the `neuron` binary under package name
-  `helexa-neuron`. Renamed from bare `neuron` to avoid collision with
-  Fedora's NEURON neural-simulation package
-  (https://src.fedoraproject.org/rpms/neuron); binary, systemd unit,
-  system user, and config dir all stay named `neuron` since those are
-  project-local contexts.
 - `data/cortex.service`, `data/neuron.service` — systemd units
 - `cortex.example.toml`, `neuron.example.toml`, `models.example.toml`
-- CI: parallel `srpm-cortex` + `srpm-neuron` jobs, then parallel COPR
+- CI: parallel `srpm-cortex` + `srpm-neuron` jobs, then parallel COPR publish
-  publish to a single project `helexa/helexa` hosting both packages.
 
 Install:
 ```sh
-dnf copr enable helexa/helexa
+dnf copr enable helexa/cortex && dnf install cortex    # gateway host
-dnf install cortex                # gateway host
+dnf copr enable helexa/neuron && dnf install neuron    # GPU nodes
-dnf install helexa-neuron         # GPU nodes
 ```
 
-## 2026-05-18 addendum: candle-native pivot
+### Phase 11: llama.cpp harness stub
 
-Phases 11 (llama.cpp harness) and 12 (mistral.rs COPR) below are
+**Goal:** Prove the harness abstraction works with a second engine.
-**superseded**. The project no longer treats mistral.rs or llama.cpp as
-dependencies — both are conceptually out of scope. neuron becomes a
-candle-native inference daemon, with `Harness` retained as an
-internal seam for adding future engines (vision/audio/diffusion) but
-its only implementation being in-process candle.
 
-The full staged plan for this pivot lives at
+**Steps:**
-`~/.claude/plans/create-a-more-aggressive-calm-naur.md`. Summary:
+1. `crates/neuron/src/harness/llamacpp.rs` — implement the `Harness`
+   trait for llama.cpp's `llama-server`.
+   - `start()` — launch `llama-server` with the correct model path,
+     `--port`, `--n-gpu-layers`, `--tensor-split` args. Track the
+     child process.
+   - `stop()` — send SIGTERM to the child process.
+   - `list_models()` — llama-server serves one model per process, so
+     return a single-element list.
+   - `load_model()` — start a new llama-server process for this model.
+   - `unload_model()` — stop the process.
+   - `inference_endpoint()` — return `http://localhost:{assigned_port}`.
+2. Port allocation: neuron assigns ports from a range (e.g. 8100-8199)
+   to llama-server instances.
+3. Register in `HarnessRegistry` when configured:
+   ```toml
+   [[harnesses]]
+   name = "llamacpp"
+   binary = "/usr/local/bin/llama-server"
+   port_range = [8100, 8199]
+   ```
+4. Tests: mock llama-server (simple HTTP server returning canned
+   responses), test load/unload/endpoint lifecycle.
 
-- **Stage 1 (this commit):** delete `mistralrs.rs` and `llamacpp.rs`,
+**Done when:** A model with `harness = "llamacpp"` in `models.toml` can
-  scaffold inert `CandleHarness`, drop `endpoint`/`systemd_unit` from
+be loaded and served through cortex. Tests pass with mock llama-server.
-  `HarnessConfig`, default no-op `start`/`stop` on the `Harness` trait.
-- **Stages 2–4:** wire up candle model load/unload (quantized Qwen3
-  first), add OpenAI-compatible inference endpoint in neuron, then SSE
-  streaming.
-- **Stages 5–6:** load-on-activation (default models in config) and
-  unload-on-deactivation (graceful shutdown).
-- **Stages 7–8:** multi-GPU tensor parallelism and broader model/quant
-  coverage.
 
-Sections of this document that describe mistral.rs HTTP behaviour
+### Phase 12 (lower priority): mistral.rs COPR packaging
-("mistral.rs API gotchas") are retained as historical context for
-Phases 1–10 — they document what was true while the project depended
-on mistral.rs. They do not describe current behaviour.
 
----
+**Goal:** Fedora RPMs for mistral.rs built against specific CUDA versions.
 
-### Phase 11 (superseded): llama.cpp harness stub
+**Steps:**
+1. `mistralrs-cuda.spec` — RPM spec that clones a pinned mistral.rs git
+   tag, builds with `--features cuda`, links against the system CUDA
+   toolkit. Produces `mistralrs-cuda13-server` (CUDA 13.x / sm_120) and
+   `mistralrs-cuda12-server` (CUDA 12.x / sm_89). Install binary to
+   `/usr/local/bin/mistralrs`.
+2. COPR build config: enable the NVIDIA CUDA repo as a build dependency.
+   Pin the CUDA toolkit version in `BuildRequires`.
+3. Gitea Actions or manual workflow: bump the mistral.rs tag in the spec,
+   trigger COPR rebuild.
+4. neuron's mistralrs harness config references which binary/package
+   provides the mistral.rs binary. neuron could warn at startup if the
+   installed mistral.rs CUDA version doesn't match the discovered driver.
 
-~~Originally planned as a second engine to prove the harness
+**Done when:** `dnf install mistralrs-cuda13-server` on beast provides a
-abstraction.~~ Replaced by the candle harness work in the 2026-05-18
+working `mistralrs` binary built for Blackwell GPUs. `dnf install
-addendum above. llama.cpp's any-model/any-hardware breadth is no
+mistralrs-cuda12-server` on benjy provides one built for Ada GPUs.
-longer in scope for helexa.
 
-### Phase 12 (superseded): mistral.rs COPR packaging
+This is a separate repo/spec — not part of the cortex workspace — but
-
+tightly coupled operationally. Track it as a sibling project.
-~~Originally planned to ship CUDA-versioned mistral.rs RPMs.~~ Replaced
-by the candle harness work in the 2026-05-18 addendum above. With
-mistral.rs out of the dependency tree, there is nothing to package.

Cargo.toml

@@ -8,7 +8,7 @@ members = [
 ]
 
 [workspace.package]
-version = "0.1.16"
+version = "0.1.7"
 edition = "2024"
 license = "GPL-3.0-or-later"
 repository = "https://git.lair.cafe/helexa/cortex"
@@ -27,7 +27,7 @@ serde = { version = "1", features = ["derive"] }
 serde_json = "1"
 toml = "0.8"
 
-# http client (for proxying to neuron backends)
+# http client (for proxying to mistralrs backends)
 reqwest = { version = "0.12", features = ["json", "stream"] }
 
 # observability

README.md

@@ -1,23 +1,22 @@
 # cortex
 
-A Rust reverse-proxy and fleet management layer for multi-node GPU inference
+A Rust reverse-proxy and fleet management layer for multi-node
-clusters. Cortex sits in front of one or more `neuron` daemons (each running
+[mistral.rs](https://github.com/EricLBuehler/mistral.rs) inference clusters.
-candle-based inference on a local GPU host) and presents a unified OpenAI +
-Anthropic compatible API surface.
 
 ## Problem
 
 Running local LLMs across multiple GPU nodes (different VRAM tiers, different
 model affinities) requires a unified API surface that:
 
-- Presents a **single `/v1/models` catalogue** merging every model that can be
+- Presents a **single `/v1/models` catalogue** merging every model across every
-  served by any neuron in the fleet.
+  node.
-- **Routes requests** to the correct node based on where a model is loaded
+- **Routes requests** to the correct node based on where a model is loaded (or
-  (or can be loaded), handling cold-load and eviction transparently.
+  *can* be loaded).
-- Manages **model lifecycle** — load on demand, unload cold models, pin
+- Manages **model lifecycle** — unload cold models, reload on demand, pin
-  critical ones — by calling each neuron's `/models/{load,unload}` API.
+  critical ones — using the mistral.rs
+  `/v1/models/{unload,reload,status}` HTTP API (PR #1828+).
 - Translates between **OpenAI and Anthropic** request/response envelopes so
-  every client speaks whichever dialect it prefers.
+  every client in the homelab speaks whichever dialect it prefers.
 - Captures **per-request metrics** (tokens, tok/s, TTFT, latency) and exposes
   them as Prometheus counters/histograms.
 
@@ -31,17 +30,18 @@ model affinities) requires a unified API surface that:
        └────────────────┴──────┬───────┴───────────────┘
                                │
                     ┌──────────▼──────────┐
-                    │      cortex         │
+                    │   cortex     │
-                    │  (cortex-gateway)   │
+                    │   (cortex-gateway)      │
                     │                     │
                     │  Router · Metrics   │
                     │  Evictor · Translate│
                     └──┬──────┬────────┬──┘
                        │      │        │
             ┌──────────▼┐  ┌──▼─────┐  ┌▼──────────┐
-            │  neuron   │  │ neuron │  │  neuron   │
+            │ gpu-large │  │gpu-med │  │ gpu-small │
-            │  :13131   │  │ :13131 │  │  :13131   │
+            │ mistralrs │  │mistral │  │ mistralrs │
-            │  candle   │  │ candle │  │  candle   │
+            │ serve     │  │rs serve│  │ serve     │
+            │ :8080     │  │ :8080  │  │  :8080    │
             └───────────┘  └────────┘  └───────────┘
                   private network (.internal)
 ```
@@ -50,48 +50,70 @@ model affinities) requires a unified API surface that:
 
 | Crate | Purpose |
 |---|---|
-| `cortex-core` | Shared types: config, node/model state, metrics, OpenAI/Anthropic envelopes, harness trait, discovery types |
+| `cortex-core` | Shared types: config, node/model state, metrics, OpenAI/Anthropic request/response envelopes |
-| `cortex-gateway` | Axum HTTP server: proxy, router, evictor, poller, metrics exporter |
+| `cortex-gateway` | Axum HTTP server: proxy, router, evictor, metrics exporter |
-| `neuron` | Per-node daemon: GPU discovery, in-process candle inference, model lifecycle API |
+| `cortex-agent` | Per-node sidecar: polls local mistralrs, reports to gateway, handles restart/defrag |
 | `cortex-cli` | CLI entrypoint (`cortex serve`, `cortex status`, etc.) |
 
 ## Node setup
 
-Each GPU node runs `neuron` (listening on `:13131`). Neuron uses
+Each GPU node runs `mistralrs serve` with a multi-model config. Models are
-huggingface/candle for in-process inference — there is no external
+declared but start **unloaded** — mistral.rs lazy-loads on first request and
-inference subprocess to manage.
+the gateway can explicitly unload/reload via the HTTP API.
 
-The neuron RPM (`helexa-neuron`) ships a systemd unit:
+Example node systemd unit:
 
-```sh
+```ini
-dnf copr enable helexa/helexa
+# /etc/systemd/system/mistralrs.service
-dnf install helexa-neuron
+[Unit]
-systemctl enable --now neuron
+Description=mistral.rs inference server
+After=network-online.target
+Wants=network-online.target
+
+[Service]
+Type=simple
+ExecStart=/usr/local/bin/mistralrs serve \
+    --from-config /etc/mistralrs/config.toml \
+    --port 8080
+Restart=on-failure
+RestartSec=5
+Environment=CUDA_VISIBLE_DEVICES=0,1
+
+[Install]
+WantedBy=multi-user.target
 ```
 
 ## Gateway config
 
 ```toml
-# /etc/cortex/cortex.toml
+# cortex.toml
 [gateway]
-listen = "0.0.0.0:31313"
+listen = "0.0.0.0:8000"
-metrics_listen = "0.0.0.0:31314"
+metrics_listen = "0.0.0.0:9100"
 
 [eviction]
 strategy = "lru"        # lru | priority
 defrag_after_cycles = 50
 
-[[neurons]]
+[[nodes]]
-name = "beast"
+name = "gpu-large"
-endpoint = "http://beast.internal:13131"
+endpoint = "http://gpu-large.internal:8080"
+vram_mb = 49_152        # e.g. 2x RTX 4090
+pinned = ["your-org/large-model"]
 
-[[neurons]]
+[[nodes]]
-name = "benjy"
+name = "gpu-medium"
-endpoint = "http://benjy.internal:13131"
+endpoint = "http://gpu-medium.internal:8080"
+vram_mb = 24_576        # e.g. RTX 4090
+pinned = ["your-org/medium-model"]
+
+[[nodes]]
+name = "gpu-small"
+endpoint = "http://gpu-small.internal:8080"
+vram_mb = 12_288        # e.g. RTX 3060
+pinned = ["your-org/embedding-model"]
 ```
 
-Model placement profiles live in `models.toml` — see `models.example.toml`.
-
 ## Building
 
 ```sh
@@ -109,20 +131,19 @@ cargo clippy --workspace -- -D warnings   # warnings are errors
 cargo test --workspace                     # all tests must pass
 ```
 
-Tagged releases (`v*`) additionally build SRPMs for both `cortex` and
+Tagged releases (`v*`) additionally build an SRPM and publish to COPR.
-`helexa-neuron` and publish to COPR.
 
 ## Running
 
 ```sh
 # start the gateway
-cortex serve --config /etc/cortex/cortex.toml
+cortex serve --config cortex.toml
 
 # check fleet status
 cortex status
 
 # list all models across nodes
-curl http://localhost:31313/v1/models
+curl http://localhost:8000/v1/models
 ```
 
 ## License

asset/manifest.yml

@@ -1,30 +0,0 @@
-# Helexa fleet manifest.
-#
-# Drives rolling deploys via script/deploy.sh and serves as the source
-# of truth for which hosts run cortex vs neuron, and which CUDA
-# compute-capability flavour each neuron host needs.
-#
-# Flavour ↔ NVIDIA generation ↔ compute cap:
-#   ampere    sm_86   (RTX 30 series — e.g. 3060)
-#   ada       sm_89   (RTX 40 series — e.g. 4090)
-#   blackwell sm_120  (RTX 50 series — e.g. 5090)
-#
-# The flavour determines which RPM is installed on a given neuron host:
-# helexa-neuron-<flavour>. Only one flavour may be installed at a time
-# (the packages Conflict: with each other).
-
-cortex:
-  host: hanzalova.internal
-
-neurons:
-  - host: beast.hanzalova.internal
-    flavour: blackwell
-    gpu: "2x RTX 5090"
-
-  - host: benjy.hanzalova.internal
-    flavour: ada
-    gpu: "RTX 4090"
-
-  - host: quadbrat.hanzalova.internal
-    flavour: ampere
-    gpu: "RTX 3060"

cortex.example.toml

@@ -3,22 +3,22 @@
 # Copy to cortex.toml and adjust for your environment.
 #
 # Environment variable overrides use CORTEX_ prefix with __ separators:
-#   CORTEX_GATEWAY__LISTEN=0.0.0.0:31313
+#   CORTEX_GATEWAY__LISTEN=0.0.0.0:9000
 
 [gateway]
-listen = "0.0.0.0:31313"
+listen = "0.0.0.0:8000"
-metrics_listen = "0.0.0.0:31314"
+metrics_listen = "0.0.0.0:9100"
 
 [eviction]
 strategy = "lru"
-# Restart neurons after this many load/unload cycles to defragment VRAM.
+# Restart mistralrs after this many load/unload cycles to defragment VRAM.
 # Set to 0 to disable.
 defrag_after_cycles = 50
 
 # -- Nodes ---------------------------------------------------------------
-# Each [[nodes]] entry declares a neuron daemon in the fleet.
+# Each [[nodes]] entry declares a mistral.rs instance in the fleet.
-# Models are discovered by polling the neuron's /models endpoint.
+# Models are discovered by polling the node's /v1/models endpoint.
-# Pinned models (see models.toml) are never evicted.
+# Pinned models are never evicted.
 
 [[nodes]]
 name = "gpu-large"

cortex.spec

@@ -1,5 +1,5 @@
 Name:           cortex
-Version:        0.1.16
+Version:        0.1.7
 Release:        1%{?dist}
 Summary:        Inference gateway for multi-node GPU clusters
 
@@ -21,16 +21,6 @@ BuildRequires:  systemd-rpm-macros
 
 Requires(pre):  shadow-utils
 Requires:       systemd
-Requires:       firewalld-filesystem
-
-# systemd-rpm-macros ships a unit dep generator that parses User=/Group=
-# from our .service file and emits Requires: user(cortex)/group(cortex).
-# rpm's sysusers provides-generator emits the unversioned form for groups
-# but only a versioned user(cortex) = <base64> for users with GECOS/home/
-# shell. Provide the unversioned user(cortex) explicitly so dnf can resolve
-# the auto-generated Requires. Without this, dnf5 silently filters the
-# package and reports "Nothing to do".
-Provides:       user(cortex)
 
 %description
 Cortex is a Rust reverse-proxy that sits in front of multiple inference
@@ -57,7 +47,6 @@ cargo build --release -p cortex-cli
 install -Dm755 target/release/cortex %{buildroot}%{_bindir}/cortex
 install -Dm644 data/cortex.service %{buildroot}%{_unitdir}/cortex.service
 install -Dm644 data/cortex-sysusers.conf %{buildroot}%{_sysusersdir}/cortex.conf
-install -Dm644 data/cortex-firewalld.xml %{buildroot}%{_prefix}/lib/firewalld/services/cortex.xml
 install -dm755 %{buildroot}%{_sysconfdir}/cortex
 install -Dm644 cortex.example.toml %{buildroot}%{_sysconfdir}/cortex/cortex.toml
 install -Dm644 models.example.toml %{buildroot}%{_sysconfdir}/cortex/models.toml
@@ -80,21 +69,10 @@ install -Dm644 models.example.toml %{buildroot}%{_sysconfdir}/cortex/models.toml
 %{_bindir}/cortex
 %{_unitdir}/cortex.service
 %{_sysusersdir}/cortex.conf
-%{_prefix}/lib/firewalld/services/cortex.xml
 %dir %{_sysconfdir}/cortex
 %config(noreplace) %{_sysconfdir}/cortex/cortex.toml
 %config(noreplace) %{_sysconfdir}/cortex/models.toml
 
 %changelog
-* Thu Apr 16 2026 Gitea Actions <actions@git.lair.cafe> - 0.1.16-1
+* Tue Apr 15 2026 Rob Thijssen <grenade@rob.tn> - 0.1.0-1
-- chore: ignore local deploy script
-- chore: move default ports out of common-collision ranges
-- ci: drop actions/cache for cargo registry and target
-
-* Thu Apr 16 2026 Gitea Actions <actions@git.lair.cafe> - 0.1.14-1
-- ci: publish both packages to a single helexa/helexa COPR project
-- fix(rpm): rename neuron package to helexa-neuron
-- ci: commit generated %changelog entries back to main
-
-* Wed Apr 15 2026 Rob Thijssen <grenade@rob.tn> - 0.1.0-1
 - Initial package

crates/cortex-cli/src/main.rs

@@ -5,7 +5,7 @@ use tracing_subscriber::EnvFilter;
 
 #[derive(Parser)]
 #[command(name = "cortex")]
-#[command(about = "Unified inference gateway for multi-node GPU clusters")]
+#[command(about = "Unified inference gateway for multi-node mistral.rs clusters")]
 #[command(version)]
 struct Cli {
     #[command(subcommand)]
@@ -23,7 +23,7 @@ enum Commands {
     /// Print the fleet status (models, nodes, health).
     Status {
         /// Gateway API endpoint to query.
-        #[arg(short, long, default_value = "http://localhost:31313")]
+        #[arg(short, long, default_value = "http://localhost:8000")]
         endpoint: String,
     },
 }

crates/cortex-core/src/anthropic.rs

@@ -2,7 +2,7 @@
 //!
 //! These mirror the `/v1/messages` format used by the Anthropic API.
 //! The gateway accepts these, translates to OpenAI format, proxies to
-//! the inference backend (neuron), then translates the response back.
+//! mistral.rs, then translates the response back.
 
 use serde::{Deserialize, Serialize};
 use serde_json::Value;

crates/cortex-core/src/config.rs

@@ -22,9 +22,9 @@ fn default_models_path() -> String {
 
 #[derive(Debug, Clone, Serialize, Deserialize)]
 pub struct GatewaySettings {
-    /// Address to listen on for API requests (e.g. "0.0.0.0:31313")
+    /// Address to listen on for API requests (e.g. "0.0.0.0:8000")
     pub listen: String,
-    /// Address to listen on for Prometheus metrics (e.g. "0.0.0.0:31314")
+    /// Address to listen on for Prometheus metrics (e.g. "0.0.0.0:9100")
     pub metrics_listen: String,
 }
 
@@ -50,7 +50,7 @@ pub enum EvictionStrategy {
 pub struct NeuronEndpoint {
     /// Human-readable node name (e.g. "beast")
     pub name: String,
-    /// Base URL of the neuron daemon (e.g. "http://beast.internal:13131")
+    /// Base URL of the neuron daemon (e.g. "http://beast.internal:9090")
     pub endpoint: String,
 }
 
@@ -70,8 +70,8 @@ impl Default for GatewayConfig {
     fn default() -> Self {
         Self {
             gateway: GatewaySettings {
-                listen: "0.0.0.0:31313".into(),
+                listen: "0.0.0.0:8000".into(),
-                metrics_listen: "0.0.0.0:31314".into(),
+                metrics_listen: "0.0.0.0:9100".into(),
             },
             eviction: EvictionSettings {
                 strategy: EvictionStrategy::Lru,

crates/cortex-core/src/harness.rs

@@ -9,13 +9,13 @@ use async_trait::async_trait;
 use serde::{Deserialize, Serialize};
 
 /// Configuration for a harness instance on a neuron.
-///
-/// All current harnesses are in-process (candle); per-harness tuning
-/// (cache paths, device policies, etc.) lives in dedicated config
-/// blocks rather than on this struct.
 #[derive(Debug, Clone, Serialize, Deserialize)]
 pub struct HarnessConfig {
     pub name: String,
+    /// Base URL of the harness (e.g. "http://localhost:8080" for mistral.rs).
+    pub endpoint: Option<String>,
+    /// Systemd unit name, if the harness is managed via systemd.
+    pub systemd_unit: Option<String>,
 }
 
 /// Health status of a harness process.
@@ -47,24 +47,16 @@ pub struct ModelInfo {
 }
 
 /// What an inference harness must do, from neuron's perspective.
-///
-/// All current harnesses are in-process — they share neuron's address
-/// space and lifecycle. `start`/`stop` therefore default to no-ops; a
-/// future process-supervising harness would override them.
 #[async_trait]
 pub trait Harness: Send + Sync {
-    /// Human-readable name (e.g. "candle").
+    /// Human-readable name (e.g. "mistralrs", "llamacpp", "comfyui").
     fn name(&self) -> &str;
 
-    /// Start the harness. Default no-op for in-process harnesses.
+    /// Start the harness process if it is not already running.
-    async fn start(&self, _config: &HarnessConfig) -> Result<()> {
+    async fn start(&self, config: &HarnessConfig) -> Result<()>;
-        Ok(())
-    }
 
-    /// Stop the harness. Default no-op for in-process harnesses.
+    /// Stop the harness process gracefully.
-    async fn stop(&self) -> Result<()> {
+    async fn stop(&self) -> Result<()>;
-        Ok(())
-    }
 
     /// Health check. Returns the harness process status.
     async fn health(&self) -> HarnessHealth;

crates/cortex-core/src/node.rs

@@ -6,7 +6,7 @@ use std::collections::HashMap;
 #[derive(Debug, Clone)]
 pub struct NodeState {
     pub name: String,
-    /// Base URL of the neuron daemon (e.g. "http://beast.internal:13131").
+    /// Base URL of the neuron daemon (e.g. "http://beast.internal:9090").
     pub endpoint: String,
     pub healthy: bool,
     pub models: HashMap<String, ModelEntry>,

crates/cortex-core/src/openai.rs

@@ -3,7 +3,7 @@
 //! These are a subset sufficient for chat completions (streaming + non-streaming).
 //! Fields not relevant to proxying are captured as `serde_json::Value` via
 //! `#[serde(flatten)]` so we forward them without needing to enumerate every
-//! extension field a backend might support.
+//! extension field mistral.rs supports.
 
 use serde::{Deserialize, Serialize};
 use serde_json::Value;
@@ -22,7 +22,7 @@ pub struct ChatCompletionRequest {
     pub max_tokens: Option<u64>,
     #[serde(skip_serializing_if = "Option::is_none")]
     pub stream: Option<bool>,
-    /// All other fields (tools, response_format, backend extensions, etc.)
+    /// All other fields (tools, response_format, mistral.rs extensions, etc.)
     #[serde(flatten)]
     pub extra: Value,
 }

crates/cortex-gateway/src/proxy.rs

@@ -1,4 +1,4 @@
-//! Streaming HTTP reverse proxy to neuron backends.
+//! Streaming HTTP reverse proxy to mistral.rs backends.
 //!
 //! For streaming requests, SSE chunks are forwarded as they arrive.
 //! The proxy captures timing information for metrics but does not

crates/cortex-gateway/tests/common/mod.rs

@@ -22,7 +22,6 @@ use tokio::net::TcpListener;
 /// - GET /models/:id/endpoint (returns the inference URL)
 /// - POST /models/unload (accepts unload requests)
 /// - GET /v1/chat/completions + POST /v1/chat/completions (inference)
-///
 /// Returns the neuron base URL.
 pub async fn spawn_mock_neuron() -> String {
     let listener = TcpListener::bind("127.0.0.1:0").await.unwrap();
@@ -55,7 +54,7 @@ pub async fn spawn_mock_neuron() -> String {
 
 async fn mock_neuron_list_models() -> Json<Value> {
     Json(json!([
-        {"id": "test-model", "harness": "candle", "status": "loaded", "devices": [0], "vram_used_mb": 8000}
+        {"id": "test-model", "harness": "mistralrs", "status": "loaded", "devices": [0], "vram_used_mb": 8000}
     ]))
 }
 

crates/cortex-gateway/tests/poller.rs

@@ -12,8 +12,8 @@ use std::sync::Arc;
 async fn test_poller_discovers_models() {
     // Mock neuron reports 2 models via /models endpoint (neuron format).
     let mock_url = common::spawn_mock_neuron_with_models(json!([
-        {"id": "model-a", "harness": "candle", "status": "loaded", "devices": [0], "vram_used_mb": 8000},
+        {"id": "model-a", "harness": "mistralrs", "status": "loaded", "devices": [0], "vram_used_mb": 8000},
-        {"id": "model-b", "harness": "candle", "status": "unloaded", "devices": [], "vram_used_mb": null}
+        {"id": "model-b", "harness": "mistralrs", "status": "unloaded", "devices": [], "vram_used_mb": null}
     ]))
     .await;
 
@@ -63,8 +63,8 @@ async fn test_poller_discovers_models() {
 #[tokio::test]
 async fn test_poller_updates_gateway_models_endpoint() {
     let mock_url = common::spawn_mock_neuron_with_models(json!([
-        {"id": "model-x", "harness": "candle", "status": "loaded", "devices": [0], "vram_used_mb": null},
+        {"id": "model-x", "harness": "mistralrs", "status": "loaded", "devices": [0], "vram_used_mb": null},
-        {"id": "model-y", "harness": "candle", "status": "loaded", "devices": [1], "vram_used_mb": null}
+        {"id": "model-y", "harness": "mistralrs", "status": "loaded", "devices": [1], "vram_used_mb": null}
     ]))
     .await;
 
@@ -152,8 +152,8 @@ async fn test_poller_marks_unreachable_node_unhealthy() {
 #[tokio::test]
 async fn test_poller_removes_stale_models() {
     let mock_url = common::spawn_mock_neuron_with_models(json!([
-        {"id": "keep-me", "harness": "candle", "status": "loaded", "devices": [0], "vram_used_mb": null},
+        {"id": "keep-me", "harness": "mistralrs", "status": "loaded", "devices": [0], "vram_used_mb": null},
-        {"id": "drop-me", "harness": "candle", "status": "loaded", "devices": [0], "vram_used_mb": null}
+        {"id": "drop-me", "harness": "mistralrs", "status": "loaded", "devices": [0], "vram_used_mb": null}
     ]))
     .await;
 
@@ -183,7 +183,7 @@ async fn test_poller_removes_stale_models() {
 
     // New mock with only one model.
     let new_mock_url = common::spawn_mock_neuron_with_models(json!([
-        {"id": "keep-me", "harness": "candle", "status": "loaded", "devices": [0], "vram_used_mb": null}
+        {"id": "keep-me", "harness": "mistralrs", "status": "loaded", "devices": [0], "vram_used_mb": null}
     ]))
     .await;
 

crates/cortex-gateway/tests/streaming.rs

@@ -51,18 +51,18 @@ async fn test_streaming_sse_passthrough() {
     }
 
     assert!(
-        chunks.len() > chunk_count,
+        chunks.len() >= chunk_count + 1,
-        "expected more than {} chunks (got {}): {:?}",
+        "expected at least {} chunks (got {}): {:?}",
-        chunk_count,
+        chunk_count + 1,
         chunks.len(),
         chunks,
     );
 
     assert_eq!(chunks.last().unwrap(), "[DONE]");
 
-    for (i, chunk) in chunks.iter().enumerate().take(chunk_count) {
+    for i in 0..chunk_count {
         let chunk_json: serde_json::Value =
-            serde_json::from_str(chunk).expect("chunk should be valid JSON");
+            serde_json::from_str(&chunks[i]).expect("chunk should be valid JSON");
         assert_eq!(
             chunk_json["choices"][0]["delta"]["content"],
             format!("token{i}")

crates/neuron/Cargo.toml

@@ -12,34 +12,6 @@ path = "src/lib.rs"
 name = "neuron"
 path = "src/main.rs"
 
-[features]
-default = []
-# Enables CUDA acceleration in candle and the cudarc/nccl bindings the
-# TP worker pool uses. Without this feature, candle compiles for CPU
-# only, Device::new_cuda calls fall back to CPU, and TP Init/sanity
-# requests return Error{kind="cuda_feature_not_enabled"}.
-cuda = [
-    "candle-core/cuda",
-    "candle-core/nccl",
-    "candle-nn/cuda",
-    "candle-transformers/cuda",
-    "dep:cudarc",
-]
-# Use cuDNN for convolution / attention kernels. Requires CUDA.
-cudnn = [
-    "cuda",
-    "candle-core/cudnn",
-    "candle-nn/cudnn",
-    "candle-transformers/cudnn",
-]
-# FlashAttention kernels. Requires CUDA.
-flash-attn = [
-    "cuda",
-    "candle-transformers/flash-attn",
-]
-# Reserved for GPU-only integration tests in later stages.
-cuda-integration = ["cuda"]
-
 [dependencies]
 cortex-core.workspace = true
 tokio.workspace = true
@@ -52,25 +24,9 @@ tracing-subscriber.workspace = true
 anyhow.workspace = true
 async-trait.workspace = true
 clap.workspace = true
-thiserror.workspace = true
-futures.workspace = true
-tokio-stream.workspace = true
 figment.workspace = true
 toml.workspace = true
 
-# candle for in-process inference. CUDA support is gated behind the
-# crate's `cuda` feature (default off) so the workspace builds on
-# non-CUDA hosts and CI runners.
-candle-core = "0.10.2"
-candle-nn = "0.10.2"
-candle-transformers = "0.10.2"
-# Direct dep on cudarc (matching candle's transitive version) so the
-# TP worker pool can call cudarc::nccl::{Comm, Id} directly. Gated on
-# the `cuda` feature; same toolchain requirement as candle's CUDA path.
-cudarc = { version = "0.19", optional = true, default-features = false, features = ["nccl", "cuda-version-from-build-system"] }
-tokenizers = { version = "0.22", default-features = false, features = ["onig"] }
-hf-hub = { version = "0.4", features = ["tokio"] }
-
 [dev-dependencies]
 tokio = { workspace = true, features = ["test-util"] }
 reqwest.workspace = true

crates/neuron/src/api.rs

@@ -1,33 +1,23 @@
 //! HTTP API handlers for the neuron daemon.
 
 use crate::harness::HarnessRegistry;
-use crate::harness::candle::{CandleHarness, InferenceError};
 use crate::health::HealthCache;
 use axum::Router;
 use axum::extract::{Path, State};
 use axum::http::StatusCode;
-use axum::response::sse::{Event, KeepAlive, Sse};
 use axum::response::{IntoResponse, Json};
 use axum::routing::{get, post};
 use cortex_core::discovery::{DiscoveryResponse, HealthResponse};
 use cortex_core::harness::ModelSpec;
-use cortex_core::openai::ChatCompletionRequest;
-use futures::stream::{self, StreamExt};
 use serde_json::{Value, json};
-use std::convert::Infallible;
 use std::sync::Arc;
 use tokio::sync::RwLock;
-use tokio_stream::wrappers::ReceiverStream;
 
 /// Shared state for the neuron HTTP server.
 pub struct NeuronState {
     pub discovery: DiscoveryResponse,
     pub health_cache: Arc<HealthCache>,
     pub registry: RwLock<HarnessRegistry>,
-    /// Typed handle to the candle harness for inference routes. Cached at
-    /// startup so `/v1/chat/completions` doesn't have to hold the registry
-    /// read lock or perform dyn-Trait dispatch per request.
-    pub candle: Option<Arc<CandleHarness>>,
 }
 
 /// Build the neuron API router.
@@ -39,7 +29,6 @@ pub fn neuron_routes() -> Router<Arc<NeuronState>> {
         .route("/models/load", post(load_model))
         .route("/models/unload", post(unload_model))
         .route("/models/{model_id}/endpoint", get(model_endpoint))
-        .route("/v1/chat/completions", post(chat_completions))
 }
 
 async fn discovery_handler(State(state): State<Arc<NeuronState>>) -> Json<DiscoveryResponse> {
@@ -56,7 +45,7 @@ async fn list_models(State(state): State<Arc<NeuronState>>) -> impl IntoResponse
         Ok(models) => Json(json!(models)).into_response(),
         Err(e) => (
             StatusCode::INTERNAL_SERVER_ERROR,
-            Json(json!({"error": format!("{e:#}")})),
+            Json(json!({"error": e.to_string()})),
         )
             .into_response(),
     }
@@ -69,22 +58,11 @@ async fn load_model(
     let registry = state.registry.read().await;
     match registry.load_model(&spec).await {
         Ok(()) => Json(json!({"status": "loaded"})).into_response(),
-        Err(e) => {
+        Err(e) => (
-            // Log the full anyhow chain server-side so journalctl shows
+            StatusCode::BAD_REQUEST,
-            // the underlying failure (hf-hub timeout, permission denied,
+            Json(json!({"error": e.to_string()})),
-            // disk full, etc.) without needing to inspect the HTTP
+        )
-            // response body separately.
+            .into_response(),
-            tracing::warn!(
-                model = %spec.model_id,
-                error = %format!("{e:#}"),
-                "load_model failed"
-            );
-            (
-                StatusCode::BAD_REQUEST,
-                Json(json!({"error": format!("{e:#}")})),
-            )
-                .into_response()
-        }
     }
 }
 
@@ -106,11 +84,7 @@ async fn unload_model(
     let registry = state.registry.read().await;
     match registry.unload_model(&model_id).await {
         Ok(()) => Json(json!({"status": "unloaded"})).into_response(),
-        Err(e) => (
+        Err(e) => (StatusCode::NOT_FOUND, Json(json!({"error": e.to_string()}))).into_response(),
-            StatusCode::NOT_FOUND,
-            Json(json!({"error": format!("{e:#}")})),
-        )
-            .into_response(),
     }
 }
 
@@ -128,61 +102,3 @@ async fn model_endpoint(
             .into_response(),
     }
 }
-
-/// OpenAI-compatible chat completions. Dispatches to streaming SSE when
-/// `stream: true` is set on the request; otherwise returns a single
-/// `ChatCompletionResponse`.
-async fn chat_completions(
-    State(state): State<Arc<NeuronState>>,
-    Json(req): Json<ChatCompletionRequest>,
-) -> impl IntoResponse {
-    let Some(candle) = state.candle.as_ref().map(Arc::clone) else {
-        return (
-            StatusCode::SERVICE_UNAVAILABLE,
-            Json(json!({"error": "candle harness not enabled on this neuron"})),
-        )
-            .into_response();
-    };
-
-    if req.stream.unwrap_or(false) {
-        match candle.chat_completion_stream(req).await {
-            Ok(rx) => {
-                // Each chunk → one SSE `data: {json}` line. After the
-                // channel closes, append the OpenAI [DONE] terminator.
-                let body_stream = ReceiverStream::new(rx).map(|chunk| {
-                    let body = serde_json::to_string(&chunk).unwrap_or_default();
-                    Ok::<_, Infallible>(Event::default().data(body))
-                });
-                let done_stream =
-                    stream::once(async { Ok::<_, Infallible>(Event::default().data("[DONE]")) });
-                Sse::new(body_stream.chain(done_stream))
-                    .keep_alive(KeepAlive::default())
-                    .into_response()
-            }
-            Err(InferenceError::ModelNotLoaded(id)) => (
-                StatusCode::NOT_FOUND,
-                Json(json!({"error": format!("model '{id}' not loaded on this neuron")})),
-            )
-                .into_response(),
-            Err(InferenceError::Other(e)) => (
-                StatusCode::INTERNAL_SERVER_ERROR,
-                Json(json!({"error": format!("{e:#}")})),
-            )
-                .into_response(),
-        }
-    } else {
-        match candle.chat_completion(req).await {
-            Ok(resp) => Json(resp).into_response(),
-            Err(InferenceError::ModelNotLoaded(id)) => (
-                StatusCode::NOT_FOUND,
-                Json(json!({"error": format!("model '{id}' not loaded on this neuron")})),
-            )
-                .into_response(),
-            Err(InferenceError::Other(e)) => (
-                StatusCode::INTERNAL_SERVER_ERROR,
-                Json(json!({"error": format!("{e:#}")})),
-            )
-                .into_response(),
-        }
-    }
-}

crates/neuron/src/config.rs

@@ -1,12 +1,12 @@
 //! Neuron configuration loaded from neuron.toml.
 
-use cortex_core::harness::{HarnessConfig, ModelSpec};
+use cortex_core::harness::HarnessConfig;
 use figment::{
     Figment,
     providers::{Env, Format, Toml},
 };
 use serde::{Deserialize, Serialize};
-use std::path::{Path, PathBuf};
+use std::path::Path;
 
 #[derive(Debug, Clone, Serialize, Deserialize)]
 pub struct NeuronConfig {
@@ -14,35 +14,10 @@ pub struct NeuronConfig {
     pub port: u16,
     #[serde(default)]
     pub harnesses: Vec<HarnessConfig>,
-    /// Per-harness configuration. Currently only `candle` is recognised.
-    #[serde(default)]
-    pub harness: HarnessSettings,
-    /// Models to auto-load when the neuron service activates. Each entry
-    /// is loaded sequentially before the HTTP listener binds. A failure
-    /// on any single entry logs a warning and proceeds — broken entries
-    /// don't prevent the rest of the fleet from starting.
-    #[serde(default)]
-    pub default_models: Vec<ModelSpec>,
-}
-
-/// Settings for individual harness implementations. Each harness owns
-/// its own sub-table so users only configure the harnesses they enable.
-#[derive(Debug, Clone, Default, Serialize, Deserialize)]
-pub struct HarnessSettings {
-    #[serde(default)]
-    pub candle: CandleHarnessConfig,
-}
-
-#[derive(Debug, Clone, Default, Serialize, Deserialize)]
-pub struct CandleHarnessConfig {
-    /// HuggingFace cache directory for model weights.
-    /// When unset, defers to hf-hub's default (~/.cache/huggingface).
-    #[serde(default)]
-    pub hf_cache: Option<PathBuf>,
 }
 
 fn default_port() -> u16 {
-    13131
+    9090
 }
 
 impl NeuronConfig {
@@ -58,10 +33,8 @@ impl NeuronConfig {
 impl Default for NeuronConfig {
     fn default() -> Self {
         Self {
-            port: 13131,
+            port: 9090,
             harnesses: vec![],
-            harness: HarnessSettings::default(),
-            default_models: vec![],
         }
     }
 }

crates/neuron/src/harness/candle.rs

@@ -1,910 +0,0 @@
-//! Candle harness — in-process inference using huggingface/candle.
-//!
-//! This is the sole `Harness` implementation. Inference runs inside
-//! the neuron process; there is no external subprocess.
-//!
-//! - Stage 2 wired GGUF (Qwen3 only) load/unload via `quantized_qwen3`.
-//! - Stage 3 (this) adds `chat_completion` — a non-streaming OpenAI
-//!   compatible chat completion routed to the loaded model's forward
-//!   pass on a per-model serialised generation loop.
-
-use anyhow::{Context, Result};
-use async_trait::async_trait;
-use candle_core::quantized::gguf_file;
-use candle_core::{DType, Device, Tensor};
-use candle_nn::VarBuilder;
-use candle_transformers::generation::{LogitsProcessor, Sampling};
-use candle_transformers::models::quantized_qwen3::ModelWeights as QuantizedQwen3Weights;
-use candle_transformers::models::qwen3 as qwen3_dense;
-use cortex_core::harness::{Harness, HarnessHealth, ModelInfo, ModelSpec};
-use cortex_core::openai::{
-    ChatCompletionChoice, ChatCompletionChunk, ChatCompletionRequest, ChatCompletionResponse,
-    ChatMessage, ChunkChoice, MessageContent, Usage,
-};
-use serde_json::json;
-use std::collections::HashMap;
-use std::path::PathBuf;
-use std::sync::Arc;
-use std::time::{SystemTime, UNIX_EPOCH};
-use tokenizers::Tokenizer;
-use tokio::sync::{Mutex, RwLock, mpsc};
-
-/// In-process candle harness. Owns the loaded model registry.
-pub struct CandleHarness {
-    models: Arc<RwLock<HashMap<String, Arc<LoadedModel>>>>,
-    hf_cache: Option<PathBuf>,
-    bind_url: String,
-}
-
-/// A loaded model with its tokenizer, device placement, and architecture-
-/// specific weights. The `arch` is `Arc<Mutex<>>` so the lock guard can be
-/// moved into `spawn_blocking` for synchronous candle forward passes.
-pub struct LoadedModel {
-    pub model_id: String,
-    pub arch: Arc<Mutex<ModelArch>>,
-    pub tokenizer: Tokenizer,
-    pub device: Device,
-    pub quant: Option<String>,
-    pub devices: Vec<u32>,
-}
-
-/// Architecture-specific weights.
-///
-/// - `Qwen3Quantized` — GGUF source, pre-quantized. Single-GPU only;
-///   TP sharding pre-quantized super-blocks is intractable. Stays the
-///   default for small models loaded via `Qwen/Qwen3-*-GGUF` and
-///   `unsloth/Qwen3-*-GGUF` repos.
-/// - `Qwen3Dense` — bf16 safetensors source. The path that supports
-///   TP (Stage 7b-ii+) because slicing dense weights by row/column
-///   under safetensors is mechanical. Used when `ModelSpec.quant` is
-///   None; intended target for Qwen3.6-27B etc.
-///
-/// Stage 8 broadens this to additional families.
-pub enum ModelArch {
-    Qwen3Quantized(QuantizedQwen3Weights),
-    Qwen3Dense(qwen3_dense::ModelForCausalLM),
-}
-
-/// Repetition penalty applied to recently-generated tokens before
-/// sampling. 1.0 disables it; >1.0 makes recently-emitted tokens less
-/// likely. mistral.rs and llama.cpp default to 1.1, which is enough to
-/// stop small quantized models from degenerating into "Wait, no, no..."
-/// loops without distorting normal output.
-const REPEAT_PENALTY: f32 = 1.1;
-
-/// Number of recently-generated tokens to feed into the repetition
-/// penalty. Matches the candle quantized-qwen3 example default.
-const REPEAT_LAST_N: usize = 64;
-
-/// Apply the repetition penalty (if any) to the prediction logits and
-/// then sample. Centralises the prefill / generation-loop call sites
-/// so they share identical sampling behaviour.
-fn sample_with_penalty(
-    logits: &Tensor,
-    history: &[u32],
-    logits_processor: &mut LogitsProcessor,
-) -> Result<u32> {
-    let penalised = if (REPEAT_PENALTY - 1.0).abs() < f32::EPSILON || history.is_empty() {
-        logits.clone()
-    } else {
-        let start = history.len().saturating_sub(REPEAT_LAST_N);
-        candle_transformers::utils::apply_repeat_penalty(logits, REPEAT_PENALTY, &history[start..])?
-    };
-    Ok(logits_processor.sample(&penalised)?)
-}
-
-impl CandleHarness {
-    pub fn new(bind_url: String, hf_cache: Option<PathBuf>) -> Self {
-        Self {
-            models: Arc::new(RwLock::new(HashMap::new())),
-            hf_cache,
-            bind_url,
-        }
-    }
-
-    /// Pick a candle `Device` for the requested indices. Without the
-    /// `cuda` feature, or if CUDA initialisation fails, falls back to CPU.
-    fn pick_device(devices: &[u32]) -> Result<Device> {
-        let _idx = devices.first().copied().unwrap_or(0) as usize;
-        #[cfg(feature = "cuda")]
-        {
-            match Device::new_cuda(_idx) {
-                Ok(d) => return Ok(d),
-                Err(e) => tracing::warn!(
-                    device = _idx,
-                    error = %e,
-                    "CUDA device unavailable, falling back to CPU"
-                ),
-            }
-        }
-        Ok(Device::Cpu)
-    }
-
-    /// Build an hf-hub API client pre-configured with the harness's
-    /// `hf_cache` (when one is set).
-    fn hf_api(&self) -> Result<hf_hub::api::tokio::Api> {
-        let mut builder = hf_hub::api::tokio::ApiBuilder::new();
-        if let Some(cache) = &self.hf_cache {
-            builder = builder.with_cache_dir(cache.clone());
-        }
-        builder.build().context("build hf-hub API")
-    }
-
-    /// Resolve a dense (bf16/fp16 safetensors) model to its local file
-    /// paths.
-    ///
-    /// Handles both sharded repos (`model.safetensors.index.json` plus
-    /// several `model-*.safetensors`) and the single-file layout
-    /// (`model.safetensors`). Returns the safetensors paths in
-    /// arbitrary order — `VarBuilder` unifies them into one tensor view.
-    async fn resolve_dense_files(
-        &self,
-        spec: &ModelSpec,
-    ) -> Result<(PathBuf, PathBuf, Vec<PathBuf>)> {
-        let api = self.hf_api()?;
-        let repo = api.model(spec.model_id.clone());
-
-        let config_path = repo
-            .get("config.json")
-            .await
-            .with_context(|| format!("fetch config.json from {}", spec.model_id))?;
-        let tokenizer_path = repo
-            .get("tokenizer.json")
-            .await
-            .with_context(|| format!("fetch tokenizer.json from {}", spec.model_id))?;
-
-        // Prefer the sharded layout (most HF dense models > 5B ship it).
-        let safetensors_paths = match repo.get("model.safetensors.index.json").await {
-            Ok(index_path) => {
-                let index_text = std::fs::read_to_string(&index_path)
-                    .context("read model.safetensors.index.json")?;
-                let index: serde_json::Value = serde_json::from_str(&index_text)
-                    .context("parse model.safetensors.index.json")?;
-                let weight_map = index
-                    .get("weight_map")
-                    .and_then(|v| v.as_object())
-                    .ok_or_else(|| {
-                        anyhow::anyhow!("safetensors index missing weight_map object")
-                    })?;
-                let unique: std::collections::BTreeSet<String> = weight_map
-                    .values()
-                    .filter_map(|v| v.as_str().map(String::from))
-                    .collect();
-                let mut paths = Vec::with_capacity(unique.len());
-                for fname in unique {
-                    let p = repo
-                        .get(&fname)
-                        .await
-                        .with_context(|| format!("fetch sharded safetensors {fname}"))?;
-                    paths.push(p);
-                }
-                paths
-            }
-            Err(_) => {
-                // Single-file fallback.
-                let p = repo
-                    .get("model.safetensors")
-                    .await
-                    .context("fetch model.safetensors (single-file layout)")?;
-                vec![p]
-            }
-        };
-        Ok((config_path, tokenizer_path, safetensors_paths))
-    }
-
-    /// Resolve + load a GGUF (pre-quantized) Qwen3. Returns the
-    /// tokenizer.json path so the caller can construct the Tokenizer
-    /// uniformly across source formats.
-    async fn load_arch_gguf(
-        &self,
-        spec: &ModelSpec,
-        device: &Device,
-    ) -> Result<(PathBuf, ModelArch)> {
-        let (gguf_path, tokenizer_path) = self.resolve_files(spec).await?;
-        let device_for_load = device.clone();
-        let gguf_path_for_load = gguf_path.clone();
-        let model_id_for_log = spec.model_id.clone();
-        let arch = tokio::task::spawn_blocking(move || -> Result<ModelArch> {
-            tracing::info!(model = %model_id_for_log, path = ?gguf_path_for_load, "loading GGUF");
-            let mut file = std::fs::File::open(&gguf_path_for_load).context("open GGUF file")?;
-            let content = gguf_file::Content::read(&mut file)
-                .map_err(|e| anyhow::anyhow!("parse GGUF: {e}"))?;
-
-            let architecture = content
-                .metadata
-                .get("general.architecture")
-                .and_then(|v| v.to_string().ok().cloned())
-                .unwrap_or_default();
-            tracing::info!(architecture = %architecture, "GGUF architecture");
-
-            match architecture.as_str() {
-                "qwen3" => {
-                    let weights =
-                        QuantizedQwen3Weights::from_gguf(content, &mut file, &device_for_load)
-                            .map_err(|e| anyhow::anyhow!("from_gguf qwen3: {e}"))?;
-                    Ok(ModelArch::Qwen3Quantized(weights))
-                }
-                other => anyhow::bail!(
-                    "unsupported GGUF architecture '{other}'; quantized path only supports qwen3"
-                ),
-            }
-        })
-        .await
-        .context("blocking GGUF load task panicked")??;
-        Ok((tokenizer_path, arch))
-    }
-
-    /// Resolve + load a dense Qwen3 from safetensors. Uses
-    /// `candle-transformers::models::qwen3::ModelForCausalLM` and
-    /// builds a VarBuilder over the mmap'd safetensors files. dtype
-    /// is bf16 by default to match the HF distribution dtype for
-    /// recent Qwen3 family models; fall back to f16 if the device
-    /// doesn't support bf16.
-    async fn load_arch_dense(
-        &self,
-        spec: &ModelSpec,
-        device: &Device,
-    ) -> Result<(PathBuf, ModelArch)> {
-        let (config_path, tokenizer_path, safetensors_paths) =
-            self.resolve_dense_files(spec).await?;
-        let device_for_load = device.clone();
-        let model_id_for_log = spec.model_id.clone();
-
-        let arch = tokio::task::spawn_blocking(move || -> Result<ModelArch> {
-            tracing::info!(
-                model = %model_id_for_log,
-                shards = safetensors_paths.len(),
-                "loading dense Qwen3 from safetensors"
-            );
-            let cfg_text = std::fs::read_to_string(&config_path).context("read config.json")?;
-            let cfg: qwen3_dense::Config =
-                serde_json::from_str(&cfg_text).context("parse Qwen3 config.json")?;
-
-            // bf16 is the canonical Qwen3 distribution dtype. CUDA
-            // devices on Ada+ support it; Ampere also supports bf16
-            // natively. CPU candle handles bf16 via emulation.
-            let dtype = DType::BF16;
-            // SAFETY: VarBuilder::from_mmaped_safetensors mmaps the files;
-            // mutation of the underlying files by another process while
-            // we hold the mapping is UB. We trust that nothing else on
-            // the host modifies the HF cache files during a model's
-            // lifetime (hf-hub itself is immutable-by-design).
-            let vb = unsafe {
-                VarBuilder::from_mmaped_safetensors(&safetensors_paths, dtype, &device_for_load)
-                    .context("build VarBuilder over safetensors")?
-            };
-            let model = qwen3_dense::ModelForCausalLM::new(&cfg, vb)
-                .map_err(|e| anyhow::anyhow!("build Qwen3 dense model: {e}"))?;
-            Ok(ModelArch::Qwen3Dense(model))
-        })
-        .await
-        .context("blocking dense load task panicked")??;
-        Ok((tokenizer_path, arch))
-    }
-
-    /// Resolve a model spec to local GGUF and tokenizer file paths via
-    /// hf-hub. Downloads on first use; subsequent calls are cached.
-    async fn resolve_files(&self, spec: &ModelSpec) -> Result<(PathBuf, PathBuf)> {
-        let api = self.hf_api()?;
-        let repo = api.model(spec.model_id.clone());
-
-        let info = repo
-            .info()
-            .await
-            .with_context(|| format!("fetch HF repo info for {}", spec.model_id))?;
-
-        let quant = spec.quant.as_deref().unwrap_or("");
-        let quant_lc = quant.to_lowercase();
-        let gguf_filename = info
-            .siblings
-            .iter()
-            .map(|s| s.rfilename.as_str())
-            .filter(|name| name.to_lowercase().ends_with(".gguf"))
-            .find(|name| quant_lc.is_empty() || name.to_lowercase().contains(&quant_lc))
-            .ok_or_else(|| {
-                anyhow::anyhow!(
-                    "no GGUF file matching quant {:?} in repo {}",
-                    spec.quant,
-                    spec.model_id
-                )
-            })?
-            .to_string();
-
-        tracing::info!(
-            model = %spec.model_id,
-            file = %gguf_filename,
-            "resolving GGUF (may be cached)"
-        );
-        let gguf_path = repo
-            .get(&gguf_filename)
-            .await
-            .with_context(|| format!("fetch GGUF {gguf_filename}"))?;
-
-        // GGUF-only HF repos (unsloth/Qwen3-*-GGUF, Qwen/Qwen3-*-GGUF,
-        // etc.) ship the .gguf file but not tokenizer.json — the
-        // tokenizer.json lives in the base non-GGUF repo. Derive the
-        // base repo id by stripping a `-GGUF` / `-gguf` suffix; if
-        // there's no such suffix the same repo is used (works for
-        // non-GGUF model_ids).
-        let tokenizer_repo_id = spec
-            .model_id
-            .strip_suffix("-GGUF")
-            .or_else(|| spec.model_id.strip_suffix("-gguf"))
-            .unwrap_or(spec.model_id.as_str())
-            .to_string();
-        let tokenizer_repo = if tokenizer_repo_id == spec.model_id {
-            repo
-        } else {
-            tracing::debug!(
-                from = %spec.model_id,
-                to = %tokenizer_repo_id,
-                "tokenizer.json sourced from base repo (GGUF suffix stripped)"
-            );
-            api.model(tokenizer_repo_id.clone())
-        };
-        let tokenizer_path = tokenizer_repo
-            .get("tokenizer.json")
-            .await
-            .with_context(|| format!("fetch tokenizer.json from {tokenizer_repo_id}"))?;
-        Ok((gguf_path, tokenizer_path))
-    }
-
-    /// Run a non-streaming chat completion against a loaded model.
-    ///
-    /// Returns a typed `InferenceError` when the model isn't loaded so the
-    /// handler can map to an appropriate HTTP status without string-matching.
-    pub async fn chat_completion(
-        &self,
-        request: ChatCompletionRequest,
-    ) -> Result<ChatCompletionResponse, InferenceError> {
-        let loaded = {
-            let models = self.models.read().await;
-            models.get(&request.model).cloned()
-        };
-        let loaded = loaded.ok_or_else(|| InferenceError::ModelNotLoaded(request.model.clone()))?;
-
-        let prompt = format_qwen3_prompt(&request.messages);
-
-        let encoding = loaded
-            .tokenizer
-            .encode(prompt.as_str(), true)
-            .map_err(|e| InferenceError::Other(anyhow::anyhow!("tokenize: {e}")))?;
-        let prompt_tokens: Vec<u32> = encoding.get_ids().to_vec();
-        let prompt_len = prompt_tokens.len();
-
-        let temperature = request.temperature.unwrap_or(0.7);
-        let top_p = request.top_p;
-        let max_new = request.max_tokens.unwrap_or(512) as usize;
-        let seed = unix_subsec_nanos();
-
-        let eos_id = loaded
-            .tokenizer
-            .token_to_id("<|im_end|>")
-            .or_else(|| loaded.tokenizer.token_to_id("<|endoftext|>"));
-
-        let arch_arc = Arc::clone(&loaded.arch);
-        let device = loaded.device.clone();
-        let model_id = request.model.clone();
-
-        let (generated_ids, finish_reason) =
-            tokio::task::spawn_blocking(move || -> Result<(Vec<u32>, String)> {
-                let mut guard = arch_arc.blocking_lock();
-                run_inference(
-                    &mut guard,
-                    &device,
-                    &prompt_tokens,
-                    max_new,
-                    temperature,
-                    top_p,
-                    seed,
-                    eos_id,
-                )
-            })
-            .await
-            .map_err(|e| InferenceError::Other(anyhow::anyhow!("inference task panicked: {e}")))?
-            .map_err(InferenceError::Other)?;
-
-        let completion_text = loaded
-            .tokenizer
-            .decode(&generated_ids, true)
-            .map_err(|e| InferenceError::Other(anyhow::anyhow!("detokenize: {e}")))?;
-
-        let usage = Usage {
-            prompt_tokens: prompt_len as u64,
-            completion_tokens: generated_ids.len() as u64,
-            total_tokens: (prompt_len + generated_ids.len()) as u64,
-        };
-
-        Ok(ChatCompletionResponse {
-            id: format!("chatcmpl-{:x}", unix_subsec_nanos()),
-            object: "chat.completion".into(),
-            created: unix_now_secs(),
-            model: model_id,
-            choices: vec![ChatCompletionChoice {
-                index: 0,
-                message: ChatMessage {
-                    role: "assistant".into(),
-                    content: MessageContent::Text(completion_text),
-                    extra: serde_json::Value::Object(Default::default()),
-                },
-                finish_reason: Some(finish_reason),
-                extra: serde_json::Value::Object(Default::default()),
-            }],
-            usage: Some(usage),
-            extra: serde_json::Value::Object(Default::default()),
-        })
-    }
-
-    /// Run a streaming chat completion against a loaded model.
-    ///
-    /// Returns an `mpsc::Receiver` that yields `ChatCompletionChunk`s in
-    /// OpenAI SSE format. The first chunk carries the assistant role;
-    /// subsequent chunks carry incremental `content` deltas; the final
-    /// chunk carries `finish_reason`. The handler is responsible for
-    /// wrapping these into an SSE response and appending the `[DONE]`
-    /// terminator.
-    ///
-    /// Token-by-token decoding tracks the cumulative decoded prefix so
-    /// BPE byte-fallback boundaries don't split a UTF-8 char across
-    /// chunks.
-    pub async fn chat_completion_stream(
-        &self,
-        request: ChatCompletionRequest,
-    ) -> Result<mpsc::Receiver<ChatCompletionChunk>, InferenceError> {
-        let loaded = {
-            let models = self.models.read().await;
-            models.get(&request.model).cloned()
-        };
-        let loaded = loaded.ok_or_else(|| InferenceError::ModelNotLoaded(request.model.clone()))?;
-
-        let prompt = format_qwen3_prompt(&request.messages);
-        let encoding = loaded
-            .tokenizer
-            .encode(prompt.as_str(), true)
-            .map_err(|e| InferenceError::Other(anyhow::anyhow!("tokenize: {e}")))?;
-        let prompt_tokens: Vec<u32> = encoding.get_ids().to_vec();
-
-        let temperature = request.temperature.unwrap_or(0.7);
-        let top_p = request.top_p;
-        let max_new = request.max_tokens.unwrap_or(512) as usize;
-        let seed = unix_subsec_nanos();
-
-        let eos_id = loaded
-            .tokenizer
-            .token_to_id("<|im_end|>")
-            .or_else(|| loaded.tokenizer.token_to_id("<|endoftext|>"));
-
-        let arch_arc = Arc::clone(&loaded.arch);
-        let device = loaded.device.clone();
-        let tokenizer = loaded.tokenizer.clone();
-        let model_id = request.model.clone();
-        let id = format!("chatcmpl-{:x}", unix_subsec_nanos());
-        let created = unix_now_secs();
-
-        // Bounded channel so the producer (blocking inference) is back-
-        // pressured by the consumer (SSE writer). 32 is generous —
-        // tokens arrive one at a time and the SSE writer is async.
-        let (tx, rx) = mpsc::channel::<ChatCompletionChunk>(32);
-
-        // Lead chunk: announce the assistant role per OpenAI streaming
-        // conventions. Tools that auto-detect a streaming reply expect
-        // this before any content delta.
-        let role_chunk = ChatCompletionChunk {
-            id: id.clone(),
-            object: "chat.completion.chunk".into(),
-            created,
-            model: model_id.clone(),
-            choices: vec![ChunkChoice {
-                index: 0,
-                delta: json!({"role": "assistant"}),
-                finish_reason: None,
-                extra: serde_json::Value::Object(Default::default()),
-            }],
-            usage: None,
-            extra: serde_json::Value::Object(Default::default()),
-        };
-        // If sending the role chunk fails the receiver is already gone;
-        // bail before kicking off the heavy blocking work.
-        tx.send(role_chunk)
-            .await
-            .map_err(|_| InferenceError::Other(anyhow::anyhow!("client disconnected")))?;
-
-        tokio::task::spawn_blocking(move || {
-            let mut guard = arch_arc.blocking_lock();
-            if let Err(e) = run_inference_streaming(
-                &mut guard,
-                &device,
-                &tokenizer,
-                &prompt_tokens,
-                max_new,
-                temperature,
-                top_p,
-                seed,
-                eos_id,
-                &id,
-                created,
-                &model_id,
-                &tx,
-            ) {
-                tracing::warn!(model = %model_id, error = %e, "streaming inference failed");
-            }
-        });
-
-        Ok(rx)
-    }
-}
-
-#[async_trait]
-impl Harness for CandleHarness {
-    fn name(&self) -> &str {
-        "candle"
-    }
-
-    async fn health(&self) -> HarnessHealth {
-        HarnessHealth {
-            name: "candle".into(),
-            running: true,
-            uptime_secs: None,
-        }
-    }
-
-    async fn list_models(&self) -> Result<Vec<ModelInfo>> {
-        let models = self.models.read().await;
-        Ok(models
-            .values()
-            .map(|m| ModelInfo {
-                id: m.model_id.clone(),
-                harness: "candle".into(),
-                status: "loaded".into(),
-                devices: m.devices.clone(),
-                vram_used_mb: None,
-            })
-            .collect())
-    }
-
-    async fn load_model(&self, spec: &ModelSpec) -> Result<()> {
-        if spec.harness != "candle" {
-            anyhow::bail!("expected harness=candle, got harness={}", spec.harness);
-        }
-
-        {
-            let models = self.models.read().await;
-            if models.contains_key(&spec.model_id) {
-                anyhow::bail!("model '{}' already loaded", spec.model_id);
-            }
-        }
-
-        // Stage 7a-i scaffolds tensor-parallel worker subprocesses but
-        // does not yet route inference through them. Refuse TP loads
-        // for now with a clear marker so the request surface is honest;
-        // Stage 7b-iv replaces this bail with the TP dispatch.
-        let tp_size = spec.tensor_parallel.unwrap_or(1);
-        if tp_size > 1 {
-            anyhow::bail!(
-                "tensor_parallel={tp_size} requested for '{}': TP worker \
-                 lifecycle + NCCL handshake are in place (Stage 7a) but \
-                 TP-aware Qwen3 inference orchestration lands in Stage \
-                 7b-iv; single-GPU loads only for now",
-                spec.model_id
-            );
-        }
-
-        let devices = spec.devices.clone().unwrap_or_else(|| vec![0]);
-        let device = Self::pick_device(&devices)?;
-
-        // Dispatch by source format: GGUF (pre-quantized, single-GPU
-        // only path) vs safetensors dense (bf16/fp16; the path that
-        // grows TP support). `spec.quant` is the signal — Some means
-        // the operator picked a quantized GGUF; None means dense.
-        let (tokenizer_path, arch) = if spec.quant.is_some() {
-            self.load_arch_gguf(spec, &device).await?
-        } else {
-            self.load_arch_dense(spec, &device).await?
-        };
-
-        let tokenizer = Tokenizer::from_file(&tokenizer_path)
-            .map_err(|e| anyhow::anyhow!("load tokenizer: {e}"))?;
-
-        let loaded = Arc::new(LoadedModel {
-            model_id: spec.model_id.clone(),
-            arch: Arc::new(Mutex::new(arch)),
-            tokenizer,
-            device,
-            quant: spec.quant.clone(),
-            devices,
-        });
-
-        let mut models = self.models.write().await;
-        models.insert(spec.model_id.clone(), loaded);
-        tracing::info!(model = %spec.model_id, "model loaded");
-        Ok(())
-    }
-
-    async fn unload_model(&self, model_id: &str) -> Result<()> {
-        let mut models = self.models.write().await;
-        if models.remove(model_id).is_none() {
-            anyhow::bail!("model '{model_id}' not loaded");
-        }
-        tracing::info!(model = %model_id, "model unloaded");
-        Ok(())
-    }
-
-    async fn inference_endpoint(&self, model_id: &str) -> Option<String> {
-        let models = self.models.read().await;
-        models.contains_key(model_id).then(|| self.bind_url.clone())
-    }
-}
-
-/// Errors returned by `CandleHarness::chat_completion`. The
-/// `ModelNotLoaded` variant lets the HTTP handler map cleanly to 404
-/// without string-matching on anyhow messages.
-#[derive(Debug, thiserror::Error)]
-pub enum InferenceError {
-    #[error("model '{0}' not loaded on this neuron")]
-    ModelNotLoaded(String),
-    #[error(transparent)]
-    Other(#[from] anyhow::Error),
-}
-
-/// Apply the Qwen3 chat template:
-///
-/// ```text
-/// <|im_start|>{role}\n{content}<|im_end|>\n
-/// ...
-/// <|im_start|>assistant\n
-/// ```
-///
-/// The trailing `<|im_start|>assistant\n` cues the model to begin a turn.
-/// Non-text content parts (vision blocks) are joined as text only; full
-/// multimodal handling is out of scope for Stage 3.
-fn format_qwen3_prompt(messages: &[ChatMessage]) -> String {
-    let mut prompt = String::new();
-    for msg in messages {
-        let content = match &msg.content {
-            MessageContent::Text(s) => s.clone(),
-            MessageContent::Parts(parts) => parts
-                .iter()
-                .filter_map(|p| p.get("text").and_then(|v| v.as_str()))
-                .collect::<Vec<_>>()
-                .join(""),
-        };
-        prompt.push_str("<|im_start|>");
-        prompt.push_str(&msg.role);
-        prompt.push('\n');
-        prompt.push_str(&content);
-        prompt.push_str("<|im_end|>\n");
-    }
-    prompt.push_str("<|im_start|>assistant\n");
-    prompt
-}
-
-#[allow(clippy::too_many_arguments)]
-fn run_inference(
-    arch: &mut ModelArch,
-    device: &Device,
-    prompt_tokens: &[u32],
-    max_new: usize,
-    temperature: f64,
-    top_p: Option<f64>,
-    seed: u64,
-    eos_id: Option<u32>,
-) -> Result<(Vec<u32>, String)> {
-    let mut logits_processor = {
-        let sampling = if temperature <= 0.0 {
-            Sampling::ArgMax
-        } else {
-            match top_p {
-                Some(p) => Sampling::TopP { p, temperature },
-                None => Sampling::All { temperature },
-            }
-        };
-        LogitsProcessor::from_sampling(seed, sampling)
-    };
-
-    let mut generated: Vec<u32> = Vec::new();
-
-    let mut next_token = match arch {
-        ModelArch::Qwen3Quantized(model) => {
-            model.clear_kv_cache();
-            let input = Tensor::new(prompt_tokens, device)?.unsqueeze(0)?;
-            let logits = model.forward(&input, 0)?;
-            let logits = logits.squeeze(0)?;
-            sample_with_penalty(&logits, &generated, &mut logits_processor)?
-        }
-        ModelArch::Qwen3Dense(model) => {
-            model.clear_kv_cache();
-            let input = Tensor::new(prompt_tokens, device)?.unsqueeze(0)?;
-            // qwen3::ModelForCausalLM::forward returns [B, 1, V] —
-            // no final squeeze on the dense path, unlike the quantized
-            // variant which returns [B, V]. Strip both batch and seq
-            // dims to get the rank-1 logits LogitsProcessor expects.
-            let logits = model.forward(&input, 0)?.squeeze(0)?.squeeze(0)?;
-            sample_with_penalty(&logits, &generated, &mut logits_processor)?
-        }
-    };
-
-    if Some(next_token) == eos_id {
-        return Ok((generated, "stop".into()));
-    }
-    generated.push(next_token);
-
-    for index in 0..max_new.saturating_sub(1) {
-        next_token = match arch {
-            ModelArch::Qwen3Quantized(model) => {
-                let input = Tensor::new(&[next_token], device)?.unsqueeze(0)?;
-                let logits = model.forward(&input, prompt_tokens.len() + index)?;
-                let logits = logits.squeeze(0)?;
-                sample_with_penalty(&logits, &generated, &mut logits_processor)?
-            }
-            ModelArch::Qwen3Dense(model) => {
-                let input = Tensor::new(&[next_token], device)?.unsqueeze(0)?;
-                // Dense returns [B, 1, V]; strip both leading dims.
-                let logits = model
-                    .forward(&input, prompt_tokens.len() + index)?
-                    .squeeze(0)?
-                    .squeeze(0)?;
-                sample_with_penalty(&logits, &generated, &mut logits_processor)?
-            }
-        };
-        if Some(next_token) == eos_id {
-            return Ok((generated, "stop".into()));
-        }
-        generated.push(next_token);
-    }
-
-    Ok((generated, "length".into()))
-}
-
-/// Streaming counterpart to `run_inference`. Emits chunks via `tx` as
-/// tokens are generated and exits on EOS, max_new, or receiver drop.
-///
-/// Detokenization tracks the cumulative decoded prefix so each chunk's
-/// `content` delta is the substring appended since the last chunk —
-/// safe across BPE byte-fallback boundaries.
-#[allow(clippy::too_many_arguments)]
-fn run_inference_streaming(
-    arch: &mut ModelArch,
-    device: &Device,
-    tokenizer: &Tokenizer,
-    prompt_tokens: &[u32],
-    max_new: usize,
-    temperature: f64,
-    top_p: Option<f64>,
-    seed: u64,
-    eos_id: Option<u32>,
-    id: &str,
-    created: u64,
-    model_id: &str,
-    tx: &mpsc::Sender<ChatCompletionChunk>,
-) -> Result<()> {
-    let mut logits_processor = {
-        let sampling = if temperature <= 0.0 {
-            Sampling::ArgMax
-        } else {
-            match top_p {
-                Some(p) => Sampling::TopP { p, temperature },
-                None => Sampling::All { temperature },
-            }
-        };
-        LogitsProcessor::from_sampling(seed, sampling)
-    };
-
-    let mut all_tokens: Vec<u32> = Vec::new();
-    let mut decoded_prefix = String::new();
-    let mut finish_reason = "length".to_string();
-
-    let mut next_token = match arch {
-        ModelArch::Qwen3Quantized(model) => {
-            model.clear_kv_cache();
-            let input = Tensor::new(prompt_tokens, device)?.unsqueeze(0)?;
-            let logits = model.forward(&input, 0)?;
-            let logits = logits.squeeze(0)?;
-            sample_with_penalty(&logits, &all_tokens, &mut logits_processor)?
-        }
-        ModelArch::Qwen3Dense(model) => {
-            model.clear_kv_cache();
-            let input = Tensor::new(prompt_tokens, device)?.unsqueeze(0)?;
-            let logits = model.forward(&input, 0)?;
-            let logits = logits.squeeze(0)?;
-            sample_with_penalty(&logits, &all_tokens, &mut logits_processor)?
-        }
-    };
-
-    let emit_token = |all_tokens: &[u32], decoded_prefix: &mut String| -> Result<bool> {
-        let full = tokenizer
-            .decode(all_tokens, true)
-            .map_err(|e| anyhow::anyhow!("decode: {e}"))?;
-        if full.len() > decoded_prefix.len() {
-            let delta = full[decoded_prefix.len()..].to_string();
-            *decoded_prefix = full;
-            let chunk = ChatCompletionChunk {
-                id: id.into(),
-                object: "chat.completion.chunk".into(),
-                created,
-                model: model_id.into(),
-                choices: vec![ChunkChoice {
-                    index: 0,
-                    delta: json!({ "content": delta }),
-                    finish_reason: None,
-                    extra: serde_json::Value::Object(Default::default()),
-                }],
-                usage: None,
-                extra: serde_json::Value::Object(Default::default()),
-            };
-            // blocking_send returns Err if the consumer hung up — signal
-            // the caller to stop generating.
-            if tx.blocking_send(chunk).is_err() {
-                return Ok(false);
-            }
-        }
-        Ok(true)
-    };
-
-    if Some(next_token) == eos_id {
-        finish_reason = "stop".into();
-    } else {
-        all_tokens.push(next_token);
-        if !emit_token(&all_tokens, &mut decoded_prefix)? {
-            return Ok(());
-        }
-
-        for index in 0..max_new.saturating_sub(1) {
-            next_token = match arch {
-                ModelArch::Qwen3Quantized(model) => {
-                    let input = Tensor::new(&[next_token], device)?.unsqueeze(0)?;
-                    let logits = model.forward(&input, prompt_tokens.len() + index)?;
-                    let logits = logits.squeeze(0)?;
-                    sample_with_penalty(&logits, &all_tokens, &mut logits_processor)?
-                }
-                ModelArch::Qwen3Dense(model) => {
-                    let input = Tensor::new(&[next_token], device)?.unsqueeze(0)?;
-                    // Dense returns [B, 1, V]; strip both leading dims.
-                    let logits = model
-                        .forward(&input, prompt_tokens.len() + index)?
-                        .squeeze(0)?
-                        .squeeze(0)?;
-                    sample_with_penalty(&logits, &all_tokens, &mut logits_processor)?
-                }
-            };
-            if Some(next_token) == eos_id {
-                finish_reason = "stop".into();
-                break;
-            }
-            all_tokens.push(next_token);
-            if !emit_token(&all_tokens, &mut decoded_prefix)? {
-                return Ok(());
-            }
-        }
-    }
-
-    let final_chunk = ChatCompletionChunk {
-        id: id.into(),
-        object: "chat.completion.chunk".into(),
-        created,
-        model: model_id.into(),
-        choices: vec![ChunkChoice {
-            index: 0,
-            delta: serde_json::Value::Object(Default::default()),
-            finish_reason: Some(finish_reason),
-            extra: serde_json::Value::Object(Default::default()),
-        }],
-        usage: None,
-        extra: serde_json::Value::Object(Default::default()),
-    };
-    let _ = tx.blocking_send(final_chunk);
-    Ok(())
-}
-
-fn unix_now_secs() -> u64 {
-    SystemTime::now()
-        .duration_since(UNIX_EPOCH)
-        .map(|d| d.as_secs())
-        .unwrap_or(0)
-}
-
-fn unix_subsec_nanos() -> u64 {
-    SystemTime::now()
-        .duration_since(UNIX_EPOCH)
-        .map(|d| d.as_nanos() as u64)
-        .unwrap_or(0)
-}

crates/neuron/src/harness/llamacpp.rs

@@ -0,0 +1 @@
+// llama.cpp harness implementation — Phase 11.

crates/neuron/src/harness/mistralrs.rs

@@ -0,0 +1,163 @@
+//! mistral.rs harness implementation.
+//!
+//! Wraps the mistral.rs HTTP API for model lifecycle management
+//! and optionally manages the process via systemd.
+
+use anyhow::Result;
+use async_trait::async_trait;
+use cortex_core::harness::{Harness, HarnessConfig, HarnessHealth, ModelInfo, ModelSpec};
+use reqwest::Client;
+use serde::Deserialize;
+
+pub struct MistralRsHarness {
+    endpoint: String,
+    systemd_unit: Option<String>,
+    client: Client,
+}
+
+impl MistralRsHarness {
+    pub fn new(endpoint: String, systemd_unit: Option<String>) -> Self {
+        Self {
+            endpoint,
+            systemd_unit,
+            client: Client::builder()
+                .timeout(std::time::Duration::from_secs(30))
+                .build()
+                .expect("failed to build HTTP client"),
+        }
+    }
+}
+
+/// Response from mistral.rs `GET /v1/models`.
+#[derive(Debug, Deserialize)]
+struct ModelsResponse {
+    data: Vec<ModelEntry>,
+}
+
+#[derive(Debug, Deserialize)]
+struct ModelEntry {
+    id: String,
+    #[serde(default)]
+    status: Option<String>,
+}
+
+#[async_trait]
+impl Harness for MistralRsHarness {
+    fn name(&self) -> &str {
+        "mistralrs"
+    }
+
+    async fn start(&self, _config: &HarnessConfig) -> Result<()> {
+        let Some(unit) = &self.systemd_unit else {
+            anyhow::bail!("no systemd unit configured for mistralrs harness");
+        };
+
+        let output = tokio::process::Command::new("systemctl")
+            .args(["start", unit])
+            .output()
+            .await?;
+
+        if !output.status.success() {
+            let stderr = String::from_utf8_lossy(&output.stderr);
+            anyhow::bail!("systemctl start {unit} failed: {stderr}");
+        }
+
+        // Wait for the health endpoint to respond (up to 30s).
+        let url = format!("{}/health", self.endpoint);
+        for _ in 0..30 {
+            tokio::time::sleep(std::time::Duration::from_secs(1)).await;
+            if self.client.get(&url).send().await.is_ok() {
+                tracing::info!(unit, "mistralrs started and healthy");
+                return Ok(());
+            }
+        }
+        anyhow::bail!("mistralrs started but health endpoint did not respond within 30s");
+    }
+
+    async fn stop(&self) -> Result<()> {
+        let Some(unit) = &self.systemd_unit else {
+            anyhow::bail!("no systemd unit configured for mistralrs harness");
+        };
+
+        let output = tokio::process::Command::new("systemctl")
+            .args(["stop", unit])
+            .output()
+            .await?;
+
+        if !output.status.success() {
+            let stderr = String::from_utf8_lossy(&output.stderr);
+            anyhow::bail!("systemctl stop {unit} failed: {stderr}");
+        }
+        Ok(())
+    }
+
+    async fn health(&self) -> HarnessHealth {
+        let url = format!("{}/health", self.endpoint);
+        let running = self.client.get(&url).send().await.is_ok();
+        HarnessHealth {
+            name: "mistralrs".into(),
+            running,
+            uptime_secs: None,
+        }
+    }
+
+    async fn list_models(&self) -> Result<Vec<ModelInfo>> {
+        let url = format!("{}/v1/models", self.endpoint);
+        let resp = self.client.get(&url).send().await?;
+
+        if !resp.status().is_success() {
+            anyhow::bail!("GET /v1/models returned {}", resp.status());
+        }
+
+        let models_resp: ModelsResponse = resp.json().await?;
+        Ok(models_resp
+            .data
+            .into_iter()
+            .map(|m| ModelInfo {
+                id: m.id,
+                harness: "mistralrs".into(),
+                status: m.status.unwrap_or_else(|| "loaded".into()),
+                devices: vec![],
+                vram_used_mb: None,
+            })
+            .collect())
+    }
+
+    async fn load_model(&self, spec: &ModelSpec) -> Result<()> {
+        let url = format!("{}/v1/models/reload", self.endpoint);
+        let resp = self
+            .client
+            .post(&url)
+            .json(&serde_json::json!({ "model_id": spec.model_id }))
+            .send()
+            .await?;
+
+        if !resp.status().is_success() {
+            let body = resp.text().await.unwrap_or_default();
+            anyhow::bail!("POST /v1/models/reload failed: {body}");
+        }
+        Ok(())
+    }
+
+    async fn unload_model(&self, model_id: &str) -> Result<()> {
+        let url = format!("{}/v1/models/unload", self.endpoint);
+        let resp = self
+            .client
+            .post(&url)
+            .json(&serde_json::json!({ "model_id": model_id }))
+            .send()
+            .await?;
+
+        if !resp.status().is_success() {
+            let body = resp.text().await.unwrap_or_default();
+            anyhow::bail!("POST /v1/models/unload failed: {body}");
+        }
+        Ok(())
+    }
+
+    async fn inference_endpoint(&self, _model_id: &str) -> Option<String> {
+        // mistral.rs routes internally by model name in the request body,
+        // so the inference endpoint is always the base URL.
+        Some(self.endpoint.clone())
+    }
+}

crates/neuron/src/harness/mod.rs

@@ -1,23 +1,15 @@
 //! Harness registry — maps harness names to trait implementations.
 
-pub mod candle;
+pub mod llamacpp;
-pub mod tp;
+pub mod mistralrs;
 
 use anyhow::Result;
 use cortex_core::harness::{Harness, HarnessConfig, ModelInfo, ModelSpec};
 use std::collections::HashMap;
-use std::sync::Arc;
 
 /// Registry of available harness implementations.
-///
-/// Holds an `Arc<dyn Harness>` per harness for generic lifecycle dispatch
-/// (load/unload/list_models). When a candle harness is registered, a typed
-/// `Arc<CandleHarness>` is also cached so inference routes can bypass the
-/// dyn-Trait dispatch and reach harness-specific methods (chat completion,
-/// streaming, etc.).
 pub struct HarnessRegistry {
-    harnesses: HashMap<String, Arc<dyn Harness>>,
+    harnesses: HashMap<String, Box<dyn Harness>>,
-    candle: Option<Arc<candle::CandleHarness>>,
 }
 
 impl Default for HarnessRegistry {
@@ -30,11 +22,10 @@ impl HarnessRegistry {
     pub fn new() -> Self {
         Self {
             harnesses: HashMap::new(),
-            candle: None,
         }
     }
 
-    pub fn register(&mut self, harness: Arc<dyn Harness>) {
+    pub fn register(&mut self, harness: Box<dyn Harness>) {
         self.harnesses.insert(harness.name().to_string(), harness);
     }
 
@@ -43,12 +34,6 @@ impl HarnessRegistry {
         self.harnesses.keys().cloned().collect()
     }
 
-    /// Typed handle to the candle harness, if registered. Used by inference
-    /// routes that need methods beyond the `Harness` trait surface.
-    pub fn candle(&self) -> Option<Arc<candle::CandleHarness>> {
-        self.candle.clone()
-    }
-
     /// List models from all registered harnesses.
     pub async fn list_all_models(&self) -> Result<Vec<ModelInfo>> {
         let mut all = Vec::new();
@@ -96,25 +81,19 @@ impl HarnessRegistry {
     }
 
     /// Build a registry from harness configs.
-    ///
+    pub fn from_configs(configs: &[HarnessConfig]) -> Self {
-    /// `bind_url` is the URL where this neuron serves inference (its own
-    /// listen address). In-process harnesses (currently the only kind)
-    /// return this URL from `inference_endpoint`.
-    pub fn from_configs(
-        configs: &[HarnessConfig],
-        bind_url: &str,
-        settings: &crate::config::HarnessSettings,
-    ) -> Self {
         let mut registry = Self::new();
         for config in configs {
             match config.name.as_str() {
-                "candle" => {
+                "mistralrs" => {
-                    let harness = Arc::new(candle::CandleHarness::new(
+                    if let Some(endpoint) = &config.endpoint {
-                        bind_url.to_string(),
+                        registry.register(Box::new(mistralrs::MistralRsHarness::new(
-                        settings.candle.hf_cache.clone(),
+                            endpoint.clone(),
-                    ));
+                            config.systemd_unit.clone(),
-                    registry.candle = Some(Arc::clone(&harness));
+                        )));
-                    registry.harnesses.insert("candle".into(), harness);
+                    } else {
+                        tracing::warn!("mistralrs harness missing endpoint, skipping");
+                    }
                 }
                 other => {
                     tracing::warn!(harness = other, "unknown harness type, skipping");

crates/neuron/src/harness/tp/all_reduce.rs

@@ -1,121 +0,0 @@
-//! `AllReduce` as a candle `CustomOp1` — the bridge between candle's
-//! `Tensor` graph and `cudarc::nccl::Comm::all_reduce`.
-//!
-//! Ported from the canonical
-//! `candle-examples/examples/llama_multiprocess/model.rs` pattern.
-//! Row-parallel layers apply this op after their local matmul to sum
-//! partial outputs across NCCL ranks.
-//!
-//! Available only under `--features cuda`; on CPU builds this module
-//! is empty and row-parallel layers degenerate to local matmul only
-//! (useful for compile-checking the model code; correctness requires
-//! cuda).
-//!
-//! Thread-safety caveat: NCCL communicators are technically only
-//! safe to use from a single thread at a time
-//! (https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/usage/threadsafety.html).
-//! We hold the `AllReduce` behind an `Arc<Comm>` and only issue ops
-//! against it from the dedicated `spawn_blocking` thread the inference
-//! pipeline already uses for candle's forward passes.
-
-#![cfg(feature = "cuda")]
-
-use candle_core::cuda_backend::WrapErr;
-use candle_core::{CpuStorage, CudaStorage, CustomOp1, DType, Layout, Result, Shape};
-use cudarc::nccl::{Comm, ReduceOp};
-use half::{bf16, f16};
-use std::sync::Arc;
-
-/// Wraps an NCCL `Comm` so it can be plugged into a candle forward
-/// graph as a custom op. Each row-parallel layer holds one of these.
-pub struct AllReduce {
-    comm: Arc<Comm>,
-}
-
-// SAFETY: `Comm` contains a raw `ncclComm_t` pointer; NCCL's docs note
-// that issuing ops against one comm from multiple threads concurrently
-// is unsafe. We serialise via the single spawn_blocking thread that
-// drives the model's forward pass. The Send/Sync impl is necessary
-// because candle's CustomOp1 trait bounds require it; the correctness
-// invariant is enforced at the call site, not the type level.
-unsafe impl Send for AllReduce {}
-unsafe impl Sync for AllReduce {}
-
-impl AllReduce {
-    pub fn new(comm: Arc<Comm>) -> Self {
-        Self { comm }
-    }
-
-    pub fn comm(&self) -> &Arc<Comm> {
-        &self.comm
-    }
-}
-
-impl CustomOp1 for AllReduce {
-    fn name(&self) -> &'static str {
-        "neuron.tp.all_reduce"
-    }
-
-    fn cpu_fwd(&self, _s: &CpuStorage, _l: &Layout) -> Result<(CpuStorage, Shape)> {
-        candle_core::bail!("AllReduce custom-op invoked on CPU storage; TP requires CUDA")
-    }
-
-    fn cuda_fwd(&self, s: &CudaStorage, l: &Layout) -> Result<(CudaStorage, Shape)> {
-        use cudarc::driver::DeviceSlice;
-
-        // Reject non-contiguous inputs explicitly — copying them
-        // server-side would mask shape bugs (a TP layer feeding a
-        // strided activation into all_reduce is almost certainly a
-        // model construction error).
-        fn require_contiguous<T: cudarc::driver::DeviceRepr>(
-            slice: &cudarc::driver::CudaSlice<T>,
-            l: &Layout,
-        ) -> Result<()> {
-            match l.contiguous_offsets() {
-                Some((0, n)) if n == slice.len() => Ok(()),
-                _ => candle_core::bail!(
-                    "AllReduce input is non-contiguous: layout={:?}, slice_len={}",
-                    l,
-                    slice.len()
-                ),
-            }
-        }
-
-        let elem_count = l.shape().elem_count();
-        let dev = s.device().clone();
-
-        let out = match s.dtype() {
-            DType::BF16 => {
-                let src = s.as_cuda_slice::<bf16>()?;
-                require_contiguous(src, l)?;
-                let mut dst = unsafe { dev.alloc::<bf16>(elem_count) }.w()?;
-                self.comm
-                    .all_reduce(src, &mut dst, &ReduceOp::Sum)
-                    .map_err(|e| candle_core::Error::Msg(format!("nccl all_reduce bf16: {e:?}")))?;
-                CudaStorage::wrap_cuda_slice(dst, dev)
-            }
-            DType::F16 => {
-                let src = s.as_cuda_slice::<f16>()?;
-                require_contiguous(src, l)?;
-                let mut dst = unsafe { dev.alloc::<f16>(elem_count) }.w()?;
-                self.comm
-                    .all_reduce(src, &mut dst, &ReduceOp::Sum)
-                    .map_err(|e| candle_core::Error::Msg(format!("nccl all_reduce f16: {e:?}")))?;
-                CudaStorage::wrap_cuda_slice(dst, dev)
-            }
-            DType::F32 => {
-                let src = s.as_cuda_slice::<f32>()?;
-                require_contiguous(src, l)?;
-                let mut dst = unsafe { dev.alloc::<f32>(elem_count) }.w()?;
-                self.comm
-                    .all_reduce(src, &mut dst, &ReduceOp::Sum)
-                    .map_err(|e| candle_core::Error::Msg(format!("nccl all_reduce f32: {e:?}")))?;
-                CudaStorage::wrap_cuda_slice(dst, dev)
-            }
-            dtype => candle_core::bail!(
-                "AllReduce: unsupported dtype {dtype:?}; TP path expects bf16/f16/f32"
-            ),
-        };
-        Ok((out, l.shape().clone()))
-    }
-}

crates/neuron/src/harness/tp/mod.rs

@@ -1,371 +0,0 @@
-//! Tensor-parallel inference plumbing.
-//!
-//! The leader process (the neuron daemon proper) drives one
-//! subprocess per non-zero NCCL rank — `tokio::process::Command` on
-//! `/proc/self/exe --worker --rank N --tp-size N --cuda-device N` —
-//! and talks to each over a newline-delimited JSON RPC channel on
-//! the worker's stdin/stdout (see `rpc.rs`).
-//!
-//! Sub-staging:
-//!
-//! - **7a-i (this commit):** process lifecycle. `WorkerPool::spawn`
-//!   forks N workers; `ping` round-trips every worker to confirm
-//!   they're alive; `shutdown` cleanly drains and reaps. `Init` /
-//!   `NcclSanityCheck` are stubbed.
-//! - **7a-ii:** real NCCL `Comm` setup via `Init`, sanity check via
-//!   `NcclSanityCheck`. CUDA-gated.
-//! - **7b:** TP-aware Qwen3 inference dispatched through the pool.
-//! - **7c:** crash detection, streaming SSE, graceful unload.
-
-pub mod all_reduce;
-pub mod nccl_state;
-pub mod rpc;
-pub mod tp_linear;
-pub mod worker;
-
-use anyhow::{Context, Result};
-use std::path::{Path, PathBuf};
-use std::process::Stdio;
-use tokio::io::{AsyncBufReadExt, AsyncWriteExt, BufReader, Lines};
-use tokio::process::{Child, ChildStdin, ChildStdout, Command};
-
-use rpc::{WorkerRequest, WorkerResponse};
-
-/// One worker subprocess plus its bidirectional stdio handles.
-struct Worker {
-    rank: u32,
-    /// Captured so the leader can log "spawned rank N on device M" and
-    /// future stages can re-issue Init after a CUDA reset. Unused in
-    /// the Stage 7a-i RPC paths themselves.
-    #[allow(dead_code)]
-    cuda_device: u32,
-    child: Child,
-    stdin: ChildStdin,
-    stdout: Lines<BufReader<ChildStdout>>,
-}
-
-impl Worker {
-    /// Send a request and wait for the response. Used for sequenced
-    /// ops like `Ping` / `Shutdown` where the caller doesn't need to
-    /// overlap the worker's execution with the leader's.
-    async fn request(&mut self, req: &WorkerRequest) -> Result<WorkerResponse> {
-        self.send_only(req).await?;
-        self.recv_only().await
-    }
-
-    /// Write a request without awaiting its response. Pair with
-    /// `recv_only` from the caller when leader and worker need to do
-    /// work concurrently — e.g. during `Init`, where the leader
-    /// itself calls `Comm::from_rank` on rank 0 in parallel with the
-    /// workers, then collects `InitOk` after NCCL completes.
-    async fn send_only(&mut self, req: &WorkerRequest) -> Result<()> {
-        let mut line = serde_json::to_string(req).context("serialise WorkerRequest")?;
-        line.push('\n');
-        self.stdin
-            .write_all(line.as_bytes())
-            .await
-            .with_context(|| format!("write request to rank {}", self.rank))?;
-        self.stdin
-            .flush()
-            .await
-            .with_context(|| format!("flush stdin to rank {}", self.rank))?;
-        Ok(())
-    }
-
-    async fn recv_only(&mut self) -> Result<WorkerResponse> {
-        let reply = self
-            .stdout
-            .next_line()
-            .await
-            .with_context(|| format!("read reply from rank {}", self.rank))?
-            .ok_or_else(|| anyhow::anyhow!("rank {} stdout closed before reply", self.rank))?;
-        serde_json::from_str(&reply)
-            .with_context(|| format!("parse reply from rank {}: {reply:?}", self.rank))
-    }
-}
-
-/// A live pool of worker subprocesses. Owns the `Child` handles so
-/// dropping the pool kills the children; explicit `shutdown()` is
-/// the graceful path.
-pub struct WorkerPool {
-    world_size: u32,
-    workers: Vec<Worker>,
-    /// Path to the neuron binary used to launch workers.
-    #[allow(dead_code)]
-    exe: PathBuf,
-    /// Leader's own NCCL rank-0 state. Defaults to empty; populated by
-    /// `init_nccl()`. Held here so the leader can participate in
-    /// collectives (rank 0) without spawning a fourth subprocess.
-    leader_nccl: nccl_state::NcclState,
-}
-
-impl WorkerPool {
-    /// Spawn `world_size - 1` worker subprocesses. Rank 0 is the
-    /// leader (in-process) and is *not* spawned here — the leader
-    /// holds rank 0's NCCL Comm and shard in its own address space.
-    ///
-    /// `binary` is the path to the neuron executable to run for each
-    /// worker (production passes `/proc/self/exe`; tests pass the
-    /// sibling-binary path from `env!("CARGO_BIN_EXE_neuron")`).
-    /// `cuda_devices` is one entry per rank including rank 0. Worker
-    /// `i` (rank `i`) gets `cuda_devices[i]` as its `--cuda-device`.
-    pub async fn spawn(binary: &Path, world_size: u32, cuda_devices: &[u32]) -> Result<Self> {
-        if world_size < 2 {
-            anyhow::bail!(
-                "WorkerPool::spawn called with world_size={world_size}; \
-                 use the single-process path for world_size < 2"
-            );
-        }
-        if cuda_devices.len() as u32 != world_size {
-            anyhow::bail!(
-                "expected {world_size} cuda_devices entries, got {}",
-                cuda_devices.len()
-            );
-        }
-        let exe = binary.to_path_buf();
-
-        let mut workers = Vec::with_capacity(world_size as usize - 1);
-        // Rank 0 stays in-process. Spawn ranks 1..world_size.
-        for rank in 1..world_size {
-            let cuda_device = cuda_devices[rank as usize];
-            let mut cmd = Command::new(&exe);
-            cmd.arg("--worker")
-                .arg("--rank")
-                .arg(rank.to_string())
-                .arg("--tp-size")
-                .arg(world_size.to_string())
-                .arg("--cuda-device")
-                .arg(cuda_device.to_string())
-                .stdin(Stdio::piped())
-                .stdout(Stdio::piped())
-                // Inherit stderr so worker tracing surfaces alongside
-                // the leader's journalctl stream.
-                .stderr(Stdio::inherit())
-                .kill_on_drop(true);
-
-            let mut child = cmd
-                .spawn()
-                .with_context(|| format!("spawn worker rank {rank}"))?;
-            let stdin = child
-                .stdin
-                .take()
-                .ok_or_else(|| anyhow::anyhow!("rank {rank}: no stdin handle"))?;
-            let stdout = child
-                .stdout
-                .take()
-                .ok_or_else(|| anyhow::anyhow!("rank {rank}: no stdout handle"))?;
-            let stdout = BufReader::new(stdout).lines();
-
-            workers.push(Worker {
-                rank,
-                cuda_device,
-                child,
-                stdin,
-                stdout,
-            });
-            tracing::info!(rank, cuda_device, "spawned tp worker");
-        }
-
-        Ok(Self {
-            world_size,
-            workers,
-            exe,
-            leader_nccl: nccl_state::NcclState::new(),
-        })
-    }
-
-    /// Establish the NCCL communicator across the leader (rank 0) and
-    /// every worker subprocess. Rendezvous is via a freshly-generated
-    /// `Id` broadcast over the RPC stream; the actual handshake blocks
-    /// inside `Comm::from_rank` until all `world_size` ranks check in.
-    ///
-    /// `leader_cuda_device` is the CUDA device the leader binds rank 0
-    /// to — typically the first entry of the `cuda_devices` slice
-    /// originally passed to `spawn()`.
-    ///
-    /// On the non-cuda build this immediately fails because the leader
-    /// can't generate an `Id` without libnccl. The same call works in
-    /// the worker path (returning a no-cuda error response) so the
-    /// failure surface is uniform.
-    pub async fn init_nccl(&mut self, leader_cuda_device: u32) -> Result<()> {
-        let comm_id = nccl_state::generate_comm_id_hex()
-            .map_err(|m| anyhow::anyhow!("generate NCCL id: {m}"))?;
-
-        // 1. Write Init to every worker's stdin without awaiting the
-        //    response. Workers will parse and call Comm::from_rank
-        //    concurrently with the leader below.
-        for w in &mut self.workers {
-            let req = WorkerRequest::Init {
-                comm_id: comm_id.clone(),
-            };
-            w.send_only(&req).await?;
-        }
-
-        // 2. Leader rank 0 calls Comm::from_rank on its own device.
-        //    Runs on spawn_blocking because NCCL's init blocks until
-        //    every rank has called in — that's exactly the workers
-        //    above. The leader's NcclState is moved through the
-        //    blocking task and returned to the pool.
-        let leader_cfg = worker::WorkerConfig {
-            rank: 0,
-            world_size: self.world_size,
-            cuda_device: leader_cuda_device,
-        };
-        let comm_id_for_leader = comm_id.clone();
-        // Swap out the leader's NcclState into a fresh empty one so we
-        // can move it into spawn_blocking; restore after the task
-        // returns. (NcclState isn't Clone — it owns a real NCCL Comm.)
-        let mut leader_state = std::mem::take(&mut self.leader_nccl);
-        let (returned_state, leader_resp) = tokio::task::spawn_blocking(move || {
-            let resp = leader_state.init(leader_cfg, &comm_id_for_leader);
-            (leader_state, resp)
-        })
-        .await
-        .context("leader NCCL init task panicked")?;
-        self.leader_nccl = returned_state;
-        match leader_resp {
-            rpc::WorkerResponse::InitOk => {}
-            rpc::WorkerResponse::Error { kind, message } => {
-                anyhow::bail!("leader rank 0 init failed [{kind}]: {message}");
-            }
-            other => anyhow::bail!("leader rank 0 init: unexpected {other:?}"),
-        }
-
-        // 3. Read InitOk from each worker. By now every worker has
-        //    completed its Comm::from_rank call (NCCL released them
-        //    when the leader joined the handshake) and is writing its
-        //    response.
-        for w in &mut self.workers {
-            let resp = w.recv_only().await?;
-            match &resp {
-                rpc::WorkerResponse::InitOk => {}
-                rpc::WorkerResponse::Error { kind, message } => {
-                    anyhow::bail!("worker rank {} init failed [{kind}]: {message}", w.rank);
-                }
-                other => anyhow::bail!(
-                    "worker rank {} init: expected InitOk, got {other:?}",
-                    w.rank
-                ),
-            }
-        }
-        tracing::info!(
-            world_size = self.world_size,
-            "NCCL communicator established across all ranks"
-        );
-        Ok(())
-    }
-
-    /// Validate the NCCL communicator: every rank `all_reduce`s a
-    /// sentinel `1u32` with `ReduceOp::Sum`; the expected total is
-    /// `world_size`. Confirms the handshake is live, not just
-    /// configured.
-    ///
-    /// Must be called after `init_nccl()`; before that the leader has
-    /// no Comm and the workers reply with `nccl_not_initialised`.
-    pub async fn nccl_sanity_check(&mut self) -> Result<()> {
-        // 1. Trigger the all_reduce on every worker (write-only).
-        for w in &mut self.workers {
-            w.send_only(&WorkerRequest::NcclSanityCheck).await?;
-        }
-
-        // 2. Leader's own all_reduce, in spawn_blocking. NCCL operations
-        //    block until every rank participates.
-        let mut leader_state = std::mem::take(&mut self.leader_nccl);
-        let (returned_state, leader_resp) = tokio::task::spawn_blocking(move || {
-            let resp = leader_state.sanity_check();
-            (leader_state, resp)
-        })
-        .await
-        .context("leader NCCL sanity task panicked")?;
-        self.leader_nccl = returned_state;
-
-        let expected = self.world_size;
-        let leader_sum = match leader_resp {
-            rpc::WorkerResponse::NcclSanityResult { observed_sum } => observed_sum,
-            rpc::WorkerResponse::Error { kind, message } => {
-                anyhow::bail!("leader rank 0 sanity failed [{kind}]: {message}");
-            }
-            other => anyhow::bail!("leader rank 0 sanity: unexpected {other:?}"),
-        };
-        if leader_sum != expected {
-            anyhow::bail!("leader observed_sum={leader_sum}, expected {expected}");
-        }
-
-        // 3. Read sanity result from each worker. All must match
-        //    world_size — anything else means the collective didn't
-        //    complete consistently across ranks.
-        for w in &mut self.workers {
-            let resp = w.recv_only().await?;
-            match resp {
-                rpc::WorkerResponse::NcclSanityResult { observed_sum }
-                    if observed_sum == expected => {}
-                rpc::WorkerResponse::NcclSanityResult { observed_sum } => {
-                    anyhow::bail!(
-                        "worker rank {} observed_sum={observed_sum}, expected {expected}",
-                        w.rank
-                    );
-                }
-                rpc::WorkerResponse::Error { kind, message } => {
-                    anyhow::bail!("worker rank {} sanity failed [{kind}]: {message}", w.rank);
-                }
-                other => anyhow::bail!("worker rank {} sanity: unexpected {other:?}", w.rank),
-            }
-        }
-        tracing::info!(
-            world_size = expected,
-            "NCCL sanity check OK across all ranks"
-        );
-        Ok(())
-    }
-
-    /// Ping every worker and return their Pong payloads in rank order.
-    /// Useful right after `spawn` to confirm the lifecycle plumbing is
-    /// intact before kicking off any heavier work.
-    pub async fn ping_all(&mut self) -> Result<Vec<WorkerResponse>> {
-        let mut out = Vec::with_capacity(self.workers.len());
-        for w in &mut self.workers {
-            let resp = w.request(&WorkerRequest::Ping).await?;
-            match &resp {
-                WorkerResponse::Pong { rank, .. } if *rank == w.rank => {}
-                WorkerResponse::Pong { rank, .. } => {
-                    anyhow::bail!("rank mismatch: expected {}, got {rank}", w.rank);
-                }
-                other => anyhow::bail!("expected Pong from rank {}, got {other:?}", w.rank),
-            }
-            out.push(resp);
-        }
-        Ok(out)
-    }
-
-    /// Send `Shutdown` to every worker, await each `Bye`, and reap the
-    /// children. Best-effort — individual worker failures are logged
-    /// but don't abort the rest of the sweep.
-    pub async fn shutdown(mut self) -> Result<()> {
-        for w in &mut self.workers {
-            match w.request(&WorkerRequest::Shutdown).await {
-                Ok(WorkerResponse::Bye) => {}
-                Ok(other) => tracing::warn!(
-                    rank = w.rank,
-                    response = ?other,
-                    "expected Bye on shutdown"
-                ),
-                Err(e) => tracing::warn!(rank = w.rank, error = %e, "shutdown request failed"),
-            }
-        }
-        for w in &mut self.workers {
-            match w.child.wait().await {
-                Ok(status) => tracing::info!(rank = w.rank, %status, "worker exited"),
-                Err(e) => tracing::warn!(rank = w.rank, error = %e, "wait on worker failed"),
-            }
-        }
-        Ok(())
-    }
-
-    pub fn world_size(&self) -> u32 {
-        self.world_size
-    }
-
-    pub fn binary_path(&self) -> &PathBuf {
-        &self.exe
-    }
-}

crates/neuron/src/harness/tp/nccl_state.rs

@@ -1,243 +0,0 @@
-//! NCCL state held by both the worker process and the leader's pool.
-//!
-//! Split into its own module so the worker (`tp/worker.rs`) and the
-//! leader (`tp/mod.rs`) share the same hex-encoding/decoding code and
-//! the same shape of `Option<Comm>` state machine.
-//!
-//! When the `cuda` feature is off, `NcclState` is a zero-sized
-//! placeholder that returns `Error{kind="cuda_feature_not_enabled"}`
-//! from every operation. When it's on, the same struct holds the
-//! actual `cudarc::nccl::Comm`.
-
-use super::rpc::WorkerResponse;
-use super::worker::WorkerConfig;
-
-/// Encode bytes as lowercase hex. Used for ferrying NCCL `Id::internal()`
-/// across the leader→worker RPC boundary inside a JSON string.
-pub fn encode_hex(bytes: &[u8]) -> String {
-    let mut out = String::with_capacity(bytes.len() * 2);
-    for b in bytes {
-        use std::fmt::Write;
-        let _ = write!(out, "{b:02x}");
-    }
-    out
-}
-
-/// Decode lowercase-or-uppercase hex into bytes. Errors on odd length
-/// or non-hex characters; the caller bubbles those up via the RPC's
-/// `Error{kind="bad_request"}` variant.
-pub fn decode_hex(s: &str) -> Result<Vec<u8>, String> {
-    if !s.len().is_multiple_of(2) {
-        return Err(format!("hex string has odd length {}", s.len()));
-    }
-    (0..s.len())
-        .step_by(2)
-        .map(|i| {
-            u8::from_str_radix(&s[i..i + 2], 16).map_err(|e| format!("bad hex byte at {i}: {e}"))
-        })
-        .collect()
-}
-
-#[cfg(not(feature = "cuda"))]
-pub struct NcclState;
-
-#[cfg(not(feature = "cuda"))]
-impl Default for NcclState {
-    fn default() -> Self {
-        Self::new()
-    }
-}
-
-#[cfg(not(feature = "cuda"))]
-impl NcclState {
-    pub fn new() -> Self {
-        Self
-    }
-
-    pub fn init(&mut self, _cfg: WorkerConfig, _comm_id_hex: &str) -> WorkerResponse {
-        WorkerResponse::Error {
-            kind: "cuda_feature_not_enabled".into(),
-            message: "this neuron binary was built without --features cuda; \
-                 NCCL Init requires CUDA"
-                .into(),
-        }
-    }
-
-    pub fn sanity_check(&mut self) -> WorkerResponse {
-        WorkerResponse::Error {
-            kind: "cuda_feature_not_enabled".into(),
-            message: "NCCL sanity check requires --features cuda".into(),
-        }
-    }
-}
-
-#[cfg(feature = "cuda")]
-mod cuda_impl {
-    use super::*;
-    use cudarc::driver::CudaContext;
-    use cudarc::nccl::{Comm, Id, ReduceOp};
-    use std::sync::Arc;
-
-    /// Number of bytes in NCCL's unique-id type; matches `Id::internal()`'s
-    /// `[c_char; 128]`. Wire-encoded as 256 lowercase hex chars.
-    const NCCL_ID_BYTES: usize = 128;
-
-    pub struct NcclState {
-        comm: Option<Comm>,
-        /// Held alongside the Comm so the device isn't dropped
-        /// underneath the NCCL handle.
-        #[allow(dead_code)]
-        ctx: Option<Arc<CudaContext>>,
-    }
-
-    impl Default for NcclState {
-        fn default() -> Self {
-            Self::new()
-        }
-    }
-
-    impl NcclState {
-        pub fn new() -> Self {
-            Self {
-                comm: None,
-                ctx: None,
-            }
-        }
-    }
-
-    // SAFETY: `cudarc::nccl::Comm` contains a raw `ncclComm_t` pointer
-    // (libnccl-allocated state). NCCL requires that operations against
-    // one Comm be issued one at a time; we serialise access by storing
-    // NcclState behind a Mutex in `WorkerPool`. The Comm itself is
-    // move-safe — NCCL doesn't track the calling OS thread, only the
-    // stream the operations are dispatched against.
-    unsafe impl Send for NcclState {}
-    unsafe impl Sync for NcclState {}
-
-    /// Generate a fresh NCCL `Id` and return it hex-encoded. Used by
-    /// the leader to mint the shared communicator id which is then
-    /// broadcast to every worker via the RPC `Init` message.
-    pub fn generate_comm_id_hex() -> Result<String, String> {
-        // NcclError lacks a Display impl in cudarc 0.19.x — surface
-        // via Debug throughout this module.
-        let id = Id::new().map_err(|e| format!("Id::new(): {e:?}"))?;
-        let bytes_u8: [u8; NCCL_ID_BYTES] = std::array::from_fn(|i| id.internal()[i] as u8);
-        Ok(encode_hex(&bytes_u8))
-    }
-
-    impl NcclState {
-        pub fn init(&mut self, cfg: WorkerConfig, comm_id_hex: &str) -> WorkerResponse {
-            match try_init(self, cfg, comm_id_hex) {
-                Ok(()) => WorkerResponse::InitOk,
-                Err(msg) => WorkerResponse::Error {
-                    kind: "nccl_init_failed".into(),
-                    message: msg,
-                },
-            }
-        }
-
-        pub fn sanity_check(&mut self) -> WorkerResponse {
-            let Some(comm) = self.comm.as_ref() else {
-                return WorkerResponse::Error {
-                    kind: "nccl_not_initialised".into(),
-                    message: "sanity_check requires Init to have completed first".into(),
-                };
-            };
-            match try_sanity_check(comm) {
-                Ok(sum) => WorkerResponse::NcclSanityResult { observed_sum: sum },
-                Err(msg) => WorkerResponse::Error {
-                    kind: "nccl_sanity_failed".into(),
-                    message: msg,
-                },
-            }
-        }
-    }
-
-    fn try_init(state: &mut NcclState, cfg: WorkerConfig, comm_id_hex: &str) -> Result<(), String> {
-        let bytes = decode_hex(comm_id_hex)?;
-        if bytes.len() != NCCL_ID_BYTES {
-            return Err(format!(
-                "comm_id is {} bytes, expected {NCCL_ID_BYTES}",
-                bytes.len()
-            ));
-        }
-        let id_bytes: [std::ffi::c_char; NCCL_ID_BYTES] =
-            std::array::from_fn(|i| bytes[i] as std::ffi::c_char);
-        let id = Id::uninit(id_bytes);
-
-        let ctx = CudaContext::new(cfg.cuda_device as usize)
-            .map_err(|e| format!("CudaContext::new({}) failed: {e}", cfg.cuda_device))?;
-        let stream = ctx.default_stream();
-        let comm = Comm::from_rank(stream, cfg.rank as usize, cfg.world_size as usize, id)
-            .map_err(|e| {
-                format!(
-                    "Comm::from_rank(rank={}, world={}) failed: {e:?}",
-                    cfg.rank, cfg.world_size
-                )
-            })?;
-
-        state.ctx = Some(ctx);
-        state.comm = Some(comm);
-        Ok(())
-    }
-
-    fn try_sanity_check(comm: &Comm) -> Result<u32, String> {
-        let stream = comm.stream().clone();
-        let input = stream
-            .clone_htod(&[1u32])
-            .map_err(|e| format!("htod sentinel: {e}"))?;
-        let mut output = stream
-            .alloc_zeros::<u32>(1)
-            .map_err(|e| format!("alloc output: {e}"))?;
-        // cudarc::nccl::NcclError doesn't impl Display in 0.19.x —
-        // surface via Debug so we still see the variant + ncclResult
-        // code instead of a generic "{e}" failure.
-        comm.all_reduce(&input, &mut output, &ReduceOp::Sum)
-            .map_err(|e| format!("all_reduce: {e:?}"))?;
-        let result = stream
-            .clone_dtoh(&output)
-            .map_err(|e| format!("dtoh result: {e}"))?;
-        Ok(result[0])
-    }
-}
-
-#[cfg(feature = "cuda")]
-pub use cuda_impl::{NcclState, generate_comm_id_hex};
-
-/// Non-cuda stub for the leader: returns a clear marker error rather
-/// than letting `init_nccl` succeed vacuously.
-#[cfg(not(feature = "cuda"))]
-pub fn generate_comm_id_hex() -> Result<String, String> {
-    Err("cuda_feature_not_enabled: build with --features cuda".into())
-}
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    #[test]
-    fn hex_roundtrip() {
-        let original: Vec<u8> = (0u8..=255).collect();
-        let encoded = encode_hex(&original);
-        assert_eq!(encoded.len(), 512);
-        let decoded = decode_hex(&encoded).expect("decode");
-        assert_eq!(decoded, original);
-    }
-
-    #[test]
-    fn hex_decode_rejects_odd_length() {
-        assert!(decode_hex("a").is_err());
-        assert!(decode_hex("abc").is_err());
-    }
-
-    #[test]
-    fn hex_decode_rejects_non_hex() {
-        assert!(decode_hex("zz").is_err());
-        assert!(decode_hex("ab_d").is_err());
-    }
-
-    #[test]
-    fn hex_encode_is_lowercase_padded() {
-        assert_eq!(encode_hex(&[0x0a, 0xff]), "0aff");
-    }
-}

crates/neuron/src/harness/tp/rpc.rs

@@ -1,186 +0,0 @@
-//! Wire protocol between the neuron leader process and its
-//! `--worker` subprocesses.
-//!
-//! Every frame is one newline-delimited JSON object on the worker's
-//! stdin (request) or stdout (response). Both directions are tagged
-//! sum types from the start so new ops in Stage 7b/7c slot in without
-//! breaking compatibility — no "14 message types and a version field"
-//! drift later. Adding a new variant is the canonical way to evolve
-//! the protocol; existing peers that don't recognise an op return
-//! `WorkerResponse::Error { kind: "unknown_op", .. }`.
-//!
-//! The serialised shape uses `tag = "op"` so a request looks like:
-//!     {"op":"ping"}
-//!     {"op":"init","comm_id":"a1b2..."}
-//! and a response:
-//!     {"op":"pong","rank":0,"world_size":2,"cuda_device":0}
-//!     {"op":"error","kind":"nccl_init_failed","message":"..."}
-
-use serde::{Deserialize, Serialize};
-
-/// Leader → worker. Worker handles one at a time; replies with exactly
-/// one `WorkerResponse` per request.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-#[serde(tag = "op", rename_all = "snake_case")]
-pub enum WorkerRequest {
-    /// Liveness probe. Worker replies with `Pong` containing its own
-    /// identity. Used by the leader to confirm the subprocess is up
-    /// and ready before kicking off any heavier work.
-    Ping,
-
-    /// One-shot NCCL communicator setup. The leader generates the
-    /// `comm_id` once (rank 0 of NCCL), broadcasts it to every worker
-    /// via this message, then every rank (leader included) calls
-    /// `Comm::from_rank` with the same id — NCCL blocks until all
-    /// `world_size` ranks check in. The hex-encoded bytes are the
-    /// canonical `cudarc::nccl::Id::internal()` content.
-    Init {
-        /// Hex-encoded NCCL id bytes (128 bytes → 256 hex chars).
-        comm_id: String,
-    },
-
-    /// Sanity check: after Init, every rank runs an `all_reduce` over
-    /// a sentinel value (`1u32`). The expected sum is `world_size`.
-    /// Worker replies with the observed value so the leader can verify
-    /// the NCCL handshake is genuinely live, not just configured.
-    NcclSanityCheck,
-
-    /// Worker should release resources and exit. Worker replies `Bye`
-    /// and then closes stdout / exits zero. The leader reaps the
-    /// child via the `tokio::process::Child` it kept.
-    Shutdown,
-}
-
-/// Worker → leader. Always exactly one of these per `WorkerRequest`.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-#[serde(tag = "op", rename_all = "snake_case")]
-pub enum WorkerResponse {
-    /// Reply to `Ping`. Carries enough identity for the leader to log
-    /// what it actually got back.
-    Pong {
-        rank: u32,
-        world_size: u32,
-        cuda_device: u32,
-    },
-
-    /// Reply to `Init`. Empty payload — success is the absence of
-    /// `Error`. NCCL's internal blocking handshake means by the time
-    /// this comes back, every other rank has also reached
-    /// `Comm::from_rank`.
-    InitOk,
-
-    /// Reply to `NcclSanityCheck`. The observed sum after a single
-    /// `all_reduce(SUM, 1u32)` across all ranks. The leader checks
-    /// this matches `world_size`.
-    NcclSanityResult { observed_sum: u32 },
-
-    /// Reply to `Shutdown`. Worker exits immediately after writing this.
-    Bye,
-
-    /// Any request can produce this instead of its dedicated success
-    /// variant. `kind` is a machine-readable category so the leader
-    /// can branch on failure mode without string-matching `message`.
-    Error {
-        /// Short tag — `nccl_init_failed`, `unknown_op`, etc.
-        kind: String,
-        /// Human-readable detail for logs.
-        message: String,
-    },
-}
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    fn roundtrip<T>(value: &T) -> T
-    where
-        T: Serialize + for<'de> Deserialize<'de>,
-    {
-        serde_json::from_str(&serde_json::to_string(value).expect("serialise"))
-            .expect("deserialise")
-    }
-
-    #[test]
-    fn request_ping_round_trip() {
-        let req = WorkerRequest::Ping;
-        let wire = serde_json::to_string(&req).unwrap();
-        assert_eq!(wire, r#"{"op":"ping"}"#);
-        match roundtrip(&req) {
-            WorkerRequest::Ping => {}
-            other => panic!("expected Ping, got {other:?}"),
-        }
-    }
-
-    #[test]
-    fn request_init_carries_hex_id() {
-        let req = WorkerRequest::Init {
-            comm_id: "deadbeef".into(),
-        };
-        let wire = serde_json::to_string(&req).unwrap();
-        assert_eq!(wire, r#"{"op":"init","comm_id":"deadbeef"}"#);
-    }
-
-    #[test]
-    fn request_shutdown_round_trip() {
-        assert_eq!(
-            serde_json::to_string(&WorkerRequest::Shutdown).unwrap(),
-            r#"{"op":"shutdown"}"#
-        );
-    }
-
-    #[test]
-    fn response_pong_round_trip() {
-        let resp = WorkerResponse::Pong {
-            rank: 1,
-            world_size: 4,
-            cuda_device: 1,
-        };
-        let wire = serde_json::to_string(&resp).unwrap();
-        assert!(wire.contains(r#""op":"pong""#));
-        assert!(wire.contains(r#""rank":1"#));
-        assert!(wire.contains(r#""world_size":4"#));
-        match roundtrip(&resp) {
-            WorkerResponse::Pong {
-                rank,
-                world_size,
-                cuda_device,
-            } => {
-                assert_eq!(rank, 1);
-                assert_eq!(world_size, 4);
-                assert_eq!(cuda_device, 1);
-            }
-            other => panic!("expected Pong, got {other:?}"),
-        }
-    }
-
-    #[test]
-    fn response_error_carries_kind_and_message() {
-        let resp = WorkerResponse::Error {
-            kind: "nccl_init_failed".into(),
-            message: "could not bind device".into(),
-        };
-        let wire = serde_json::to_string(&resp).unwrap();
-        assert!(wire.contains(r#""op":"error""#));
-        assert!(wire.contains(r#""kind":"nccl_init_failed""#));
-    }
-
-    #[test]
-    fn response_sanity_result_round_trip() {
-        let resp = WorkerResponse::NcclSanityResult { observed_sum: 4 };
-        match roundtrip(&resp) {
-            WorkerResponse::NcclSanityResult { observed_sum } => {
-                assert_eq!(observed_sum, 4);
-            }
-            other => panic!("expected NcclSanityResult, got {other:?}"),
-        }
-    }
-
-    /// Unknown ops on the wire deserialise to an error rather than
-    /// silently mis-matching — confirms our `serde(tag = "op")`
-    /// configuration rejects unknowns instead of doing fuzzy matching.
-    #[test]
-    fn unknown_op_fails_to_parse() {
-        let result: Result<WorkerRequest, _> = serde_json::from_str(r#"{"op":"explode"}"#);
-        assert!(result.is_err(), "should reject unknown op, got {result:?}");
-    }
-}

crates/neuron/src/harness/tp/tp_linear.rs

@@ -1,134 +0,0 @@
-//! Tensor-parallel linear layers built on candle's `ShardedVarBuilder`
-//! and `Shard` sharding hints.
-//!
-//! candle reads only the rank's slice of each weight tensor from
-//! safetensors via `view.slice(start..stop)` — no full-tensor host
-//! materialisation. That's a memory-efficiency win over hand-rolled
-//! "load full + narrow" sharding (which the earlier
-//! `sharded_linear.rs` exploration demonstrated but didn't pay for).
-//!
-//! Two layer types:
-//!
-//! - [`ColumnParallelLinear`] — output-sharded; forward is a plain
-//!   local matmul. The downstream consumer either accepts a sharded
-//!   activation (next layer is also column-parallel) or all-gathers.
-//! - [`RowParallelLinear`] — input-sharded; forward = local matmul
-//!   then `AllReduce` `CustomOp1` to sum partials across ranks.
-//!
-//! Both assume **no bias** — every Qwen3-family weight layout we
-//! actually target (Qwen3, Qwen3-Coder, Qwen3.6 base, etc.) sets
-//! `attention_bias=false` and the MLP layers are no-bias. Adding bias
-//! support is mechanical when a future model needs it; the design
-//! choice would be: column-parallel shards the bias along dim 0;
-//! row-parallel holds the bias only on rank 0 so the post-`AllReduce`
-//! sum carries it exactly once.
-
-use anyhow::{Context, Result};
-use candle_core::{Module, Tensor};
-use candle_nn::Linear;
-use candle_nn::var_builder::{Shard, ShardedVarBuilder};
-
-#[cfg(feature = "cuda")]
-use super::all_reduce::AllReduce;
-
-/// Helper to build a [`Shard`] hint for a given dimension.
-pub(crate) fn shard(dim: usize, rank: u32, world_size: u32) -> Shard {
-    Shard {
-        dim,
-        rank: rank as usize,
-        world_size: world_size as usize,
-    }
-}
-
-/// Output-dim sharded linear (column-parallel). Holds a standard
-/// `candle_nn::Linear` whose `weight` is the rank's slice of the full
-/// `[out_features, in_features]` tensor along dim 0.
-pub struct ColumnParallelLinear {
-    inner: Linear,
-}
-
-impl ColumnParallelLinear {
-    /// Load this rank's column-parallel slice from a
-    /// `ShardedVarBuilder`. The provided `vb` must already be `pp`-ed
-    /// to the layer's path (e.g. `vb.pp("model.layers.0.self_attn.q_proj")`).
-    pub fn load(vb: &ShardedVarBuilder, rank: u32, world_size: u32) -> Result<Self> {
-        let weight = vb
-            .get_with_hints((), "weight", shard(0, rank, world_size))
-            .with_context(|| format!("load column-parallel '{}' weight", vb.prefix()))?;
-        Ok(Self {
-            inner: Linear::new(weight, None),
-        })
-    }
-}
-
-impl Module for ColumnParallelLinear {
-    fn forward(&self, x: &Tensor) -> candle_core::Result<Tensor> {
-        self.inner.forward(x)
-    }
-}
-
-/// Input-dim sharded linear (row-parallel).
-///
-/// Holds a sharded `Linear` plus an `AllReduce` op the forward chains
-/// after the local matmul to recover the full activation.
-pub struct RowParallelLinear {
-    inner: Linear,
-    #[cfg(feature = "cuda")]
-    all_reduce: AllReduce,
-    /// Whether the AllReduce should run. Column-parallel ↔ row-parallel
-    /// is a pair: the column output is sharded, the row input is
-    /// sharded, and the AllReduce gives back the full output. For
-    /// `world_size = 1` the AllReduce is a no-op so we skip it.
-    needs_reduce: bool,
-}
-
-impl RowParallelLinear {
-    /// Load this rank's row-parallel slice from a `ShardedVarBuilder`.
-    ///
-    /// Under `cuda`, `comm` is the NCCL communicator the row-parallel
-    /// `AllReduce` runs against. On CPU builds the parameter is
-    /// elided — forward returns the partial sum, which is the *wrong*
-    /// answer for inference but lets us compile-check the model.
-    #[cfg(feature = "cuda")]
-    pub fn load(
-        vb: &ShardedVarBuilder,
-        rank: u32,
-        world_size: u32,
-        comm: std::sync::Arc<cudarc::nccl::Comm>,
-    ) -> Result<Self> {
-        let weight = vb
-            .get_with_hints((), "weight", shard(1, rank, world_size))
-            .with_context(|| format!("load row-parallel '{}' weight", vb.prefix()))?;
-        Ok(Self {
-            inner: Linear::new(weight, None),
-            all_reduce: AllReduce::new(comm),
-            needs_reduce: world_size > 1,
-        })
-    }
-
-    #[cfg(not(feature = "cuda"))]
-    pub fn load(vb: &ShardedVarBuilder, rank: u32, world_size: u32) -> Result<Self> {
-        let weight = vb
-            .get_with_hints((), "weight", shard(1, rank, world_size))
-            .with_context(|| format!("load row-parallel '{}' weight", vb.prefix()))?;
-        Ok(Self {
-            inner: Linear::new(weight, None),
-            needs_reduce: world_size > 1,
-        })
-    }
-}
-
-impl Module for RowParallelLinear {
-    /// Local matmul followed by an `AllReduce` (when `cuda` and
-    /// `world_size > 1`). On CPU or single-rank, returns the partial
-    /// output directly — which is *only* correct for `world_size == 1`.
-    fn forward(&self, x: &Tensor) -> candle_core::Result<Tensor> {
-        let local = self.inner.forward(x)?;
-        #[cfg(feature = "cuda")]
-        if self.needs_reduce {
-            return local.apply_op1_no_bwd(&self.all_reduce);
-        }
-        let _ = self.needs_reduce;
-        Ok(local)
-    }
-}

crates/neuron/src/harness/tp/worker.rs

@@ -1,102 +0,0 @@
-//! Entry point for `neuron --worker`.
-//!
-//! The worker reads one newline-delimited JSON `WorkerRequest` from
-//! stdin per loop iteration, dispatches synchronously, and writes
-//! exactly one `WorkerResponse` JSON line to stdout. tracing goes to
-//! stderr so it doesn't collide with the RPC stream.
-//!
-//! NCCL operations (`Init`, `NcclSanityCheck`) are real when built
-//! with the `cuda` feature; without it they reply with
-//! `Error{kind="cuda_feature_not_enabled"}` so the leader can tell
-//! the difference between a misconfigured build and a genuine NCCL
-//! failure.
-
-use anyhow::Result;
-use tokio::io::{AsyncBufReadExt, AsyncWriteExt, BufReader};
-
-use super::nccl_state::NcclState;
-use super::rpc::{WorkerRequest, WorkerResponse};
-
-#[derive(Debug, Clone, Copy)]
-pub struct WorkerConfig {
-    pub rank: u32,
-    pub world_size: u32,
-    pub cuda_device: u32,
-}
-
-/// Drive the worker RPC loop until `Shutdown` or EOF on stdin.
-pub async fn run(config: WorkerConfig) -> Result<()> {
-    tracing::info!(
-        rank = config.rank,
-        world_size = config.world_size,
-        cuda_device = config.cuda_device,
-        "tp worker starting"
-    );
-
-    let mut state = WorkerState::new(config);
-    let stdin = tokio::io::stdin();
-    let mut reader = BufReader::new(stdin).lines();
-    let mut stdout = tokio::io::stdout();
-
-    while let Some(line) = reader.next_line().await? {
-        if line.trim().is_empty() {
-            continue;
-        }
-        let req: WorkerRequest = match serde_json::from_str(&line) {
-            Ok(r) => r,
-            Err(e) => {
-                let resp = WorkerResponse::Error {
-                    kind: "bad_request".into(),
-                    message: format!("parse {line:?}: {e}"),
-                };
-                write_response(&mut stdout, &resp).await?;
-                continue;
-            }
-        };
-
-        let resp = state.handle(req).await;
-        let is_bye = matches!(resp, WorkerResponse::Bye);
-        write_response(&mut stdout, &resp).await?;
-        if is_bye {
-            break;
-        }
-    }
-
-    tracing::info!(rank = config.rank, "tp worker exiting");
-    Ok(())
-}
-
-async fn write_response(stdout: &mut tokio::io::Stdout, resp: &WorkerResponse) -> Result<()> {
-    let mut line = serde_json::to_string(resp)?;
-    line.push('\n');
-    stdout.write_all(line.as_bytes()).await?;
-    stdout.flush().await?;
-    Ok(())
-}
-
-struct WorkerState {
-    config: WorkerConfig,
-    nccl: NcclState,
-}
-
-impl WorkerState {
-    fn new(config: WorkerConfig) -> Self {
-        Self {
-            config,
-            nccl: NcclState::new(),
-        }
-    }
-
-    async fn handle(&mut self, req: WorkerRequest) -> WorkerResponse {
-        match req {
-            WorkerRequest::Ping => WorkerResponse::Pong {
-                rank: self.config.rank,
-                world_size: self.config.world_size,
-                cuda_device: self.config.cuda_device,
-            },
-            WorkerRequest::Init { comm_id } => self.nccl.init(self.config, &comm_id),
-            WorkerRequest::NcclSanityCheck => self.nccl.sanity_check(),
-            WorkerRequest::Shutdown => WorkerResponse::Bye,
-        }
-    }
-}

crates/neuron/src/lib.rs

@@ -3,4 +3,3 @@ pub mod config;
 pub mod discovery;
 pub mod harness;
 pub mod health;
-pub mod startup;

crates/neuron/src/main.rs

@@ -1,52 +1,21 @@
 use anyhow::Result;
 use clap::Parser;
-use neuron::{
+use neuron::{api, config::NeuronConfig, discovery, harness::HarnessRegistry, health};
-    api,
-    config::NeuronConfig,
-    discovery,
-    harness::{HarnessRegistry, tp},
-    health, startup,
-};
 use std::sync::Arc;
 use std::time::Instant;
 use tokio::sync::RwLock;
 use tracing_subscriber::EnvFilter;
 
-/// Top-level CLI. The same binary runs as either the public neuron
-/// daemon (default) or a tensor-parallel worker subprocess (when
-/// `--worker` is set) spawned by the leader on the same host.
 #[derive(Parser)]
 #[command(name = "neuron")]
 #[command(about = "Per-node daemon for cortex inference clusters")]
 #[command(version)]
 struct Args {
-    /// Run in tensor-parallel worker mode. The leader process spawns
+    /// Port to listen on (overrides config file).
-    /// one of these per non-zero NCCL rank and drives it over
-    /// newline-delimited JSON on stdin/stdout. Worker mode skips
-    /// discovery, the HTTP listener, and the health poller — it's a
-    /// pure RPC loop.
-    #[arg(long, default_value_t = false)]
-    worker: bool,
-
-    /// NCCL rank for worker mode. Ignored when `--worker` is not set.
-    #[arg(long, default_value_t = 0)]
-    rank: u32,
-
-    /// Total NCCL world size for worker mode. Ignored when `--worker`
-    /// is not set.
-    #[arg(long, default_value_t = 1)]
-    tp_size: u32,
-
-    /// CUDA device index for worker mode. Ignored when `--worker` is
-    /// not set.
-    #[arg(long, default_value_t = 0)]
-    cuda_device: u32,
-
-    /// Port to listen on (overrides config file). Daemon mode only.
     #[arg(short, long)]
     port: Option<u16>,
 
-    /// Path to the neuron config file. Daemon mode only.
+    /// Path to the neuron config file.
     #[arg(short, long, default_value = "neuron.toml")]
     config: String,
 }
@@ -54,7 +23,6 @@ struct Args {
 #[tokio::main]
 async fn main() -> Result<()> {
     tracing_subscriber::fmt()
-        .with_writer(std::io::stderr)
         .with_env_filter(
             EnvFilter::try_from_default_env()
                 .unwrap_or_else(|_| EnvFilter::new("info,neuron=debug")),
@@ -63,26 +31,12 @@ async fn main() -> Result<()> {
 
     let args = Args::parse();
 
-    if args.worker {
-        return tp::worker::run(tp::worker::WorkerConfig {
-            rank: args.rank,
-            world_size: args.tp_size,
-            cuda_device: args.cuda_device,
-        })
-        .await;
-    }
-
-    daemon(args).await
-}
-
-async fn daemon(args: Args) -> Result<()> {
     let cfg = NeuronConfig::load(&args.config).unwrap_or_else(|e| {
         tracing::warn!(path = %args.config, error = %e, "config not found, using defaults");
         NeuronConfig::default()
     });
 
     let port = args.port.unwrap_or(cfg.port);
-    let bind_url = format!("http://localhost:{port}");
     let start_time = Instant::now();
 
     tracing::info!("running hardware discovery");
@@ -93,18 +47,9 @@ async fn daemon(args: Args) -> Result<()> {
         "discovery complete"
     );
 
-    // Build harness registry from config. In-process harnesses (candle)
+    // Build harness registry from config.
-    // need to know neuron's own bind URL so they can return it from
+    let registry = HarnessRegistry::from_configs(&cfg.harnesses);
-    // inference_endpoint.
-    let registry = HarnessRegistry::from_configs(&cfg.harnesses, &bind_url, &cfg.harness);
     discovery_result.harnesses = registry.names();
-    let candle = registry.candle();
-
-    // Activation: load default models before binding the listener.
-    // Each load may take tens of seconds to several minutes depending
-    // on model size and HF cache state — keep TimeoutStartSec in the
-    // systemd unit generous enough to cover the slowest entry.
-    startup::load_default_models(&registry, &cfg.default_models).await;
 
     let health_cache = Arc::new(health::HealthCache::new());
     health_cache
@@ -120,24 +65,13 @@ async fn daemon(args: Args) -> Result<()> {
         discovery: discovery_result,
         health_cache,
         registry: RwLock::new(registry),
-        candle,
     });
 
-    let app = api::neuron_routes().with_state(Arc::clone(&state));
+    let app = api::neuron_routes().with_state(state);
     let addr: std::net::SocketAddr = format!("0.0.0.0:{port}").parse()?;
     tracing::info!("neuron listening on {addr}");
     let listener = tokio::net::TcpListener::bind(addr).await?;
-    axum::serve(listener, app)
+    axum::serve(listener, app).await?;
-        .with_graceful_shutdown(startup::shutdown_signal())
-        .await?;
-
-    // Deactivation: serve has returned (graceful shutdown signal
-    // received and connections drained). Release CUDA contexts / VRAM
-    // by unloading every model before exiting; systemd's TimeoutStopSec
-    // bounds how long this phase may take.
-    let registry = state.registry.read().await;
-    startup::unload_all_models(&registry).await;
-    tracing::info!("shutdown complete");
 
     Ok(())
 }

crates/neuron/src/startup.rs

@@ -1,97 +0,0 @@
-//! Activation- and deactivation-time orchestration.
-//!
-//! Wired from `main.rs` around the HTTP listener — activation runs
-//! before bind, deactivation runs after axum returns from its
-//! graceful-shutdown future. Kept in its own module so the logic is
-//! unit-testable without spinning up a full neuron process.
-
-use crate::harness::HarnessRegistry;
-use cortex_core::harness::ModelSpec;
-use std::time::Instant;
-use tokio::signal;
-
-/// Load each spec sequentially against the registry, treating
-/// individual failures as warnings rather than fatal errors.
-///
-/// VRAM contention makes parallel loads risky; the sequential path is
-/// boring but correct. The function logs elapsed time per load so an
-/// operator can see which model is hogging activation.
-pub async fn load_default_models(registry: &HarnessRegistry, specs: &[ModelSpec]) {
-    if specs.is_empty() {
-        return;
-    }
-    tracing::info!(count = specs.len(), "loading default models");
-    for spec in specs {
-        let start = Instant::now();
-        match registry.load_model(spec).await {
-            Ok(()) => tracing::info!(
-                model = %spec.model_id,
-                elapsed_ms = start.elapsed().as_millis() as u64,
-                "loaded default model"
-            ),
-            Err(e) => tracing::warn!(
-                model = %spec.model_id,
-                error = %e,
-                elapsed_ms = start.elapsed().as_millis() as u64,
-                "failed to load default model, continuing"
-            ),
-        }
-    }
-}
-
-/// Future that resolves on SIGINT (Ctrl-C) or SIGTERM (systemd stop).
-///
-/// Wired into `axum::serve(...).with_graceful_shutdown(shutdown_signal())`
-/// so the HTTP listener stops accepting new connections, lets in-flight
-/// requests drain, and then yields control back to main for cleanup.
-pub async fn shutdown_signal() {
-    let ctrl_c = async {
-        signal::ctrl_c().await.ok();
-    };
-    let terminate = async {
-        signal::unix::signal(signal::unix::SignalKind::terminate())
-            .expect("install SIGTERM handler")
-            .recv()
-            .await;
-    };
-    tokio::select! {
-        _ = ctrl_c => tracing::info!("received SIGINT, shutting down"),
-        _ = terminate => tracing::info!("received SIGTERM, shutting down"),
-    }
-}
-
-/// Unload every model currently registered. Called from `main.rs` after
-/// axum's graceful shutdown future resolves, so CUDA contexts and VRAM
-/// are released before the process exits rather than left to the OS to
-/// reclaim. Per-model failures are logged and skipped — keep cleanup
-/// going even when one harness is unhealthy.
-pub async fn unload_all_models(registry: &HarnessRegistry) {
-    let listed = match registry.list_all_models().await {
-        Ok(m) => m,
-        Err(e) => {
-            tracing::warn!(error = %e, "failed to list models during shutdown");
-            return;
-        }
-    };
-
-    if listed.is_empty() {
-        return;
-    }
-
-    tracing::info!(count = listed.len(), "unloading models for shutdown");
-    for model in listed {
-        let start = Instant::now();
-        match registry.unload_model(&model.id).await {
-            Ok(()) => tracing::info!(
-                model = %model.id,
-                elapsed_ms = start.elapsed().as_millis() as u64,
-                "unloaded"
-            ),
-            Err(e) => tracing::warn!(
-                model = %model.id,
-                error = %e,
-                "unload failed during shutdown"
-            ),
-        }
-    }
-}

crates/neuron/tests/activation.rs

@@ -1,56 +0,0 @@
-//! Activation-time behaviour: load_default_models continues past
-//! individual failures so a single broken catalogue entry doesn't
-//! prevent the rest of the fleet from starting.
-
-use cortex_core::harness::{HarnessConfig, ModelSpec};
-use neuron::config::HarnessSettings;
-use neuron::harness::HarnessRegistry;
-use neuron::startup;
-
-#[tokio::test]
-async fn test_load_default_models_skips_unknown_harness() {
-    let registry = HarnessRegistry::from_configs(
-        &[HarnessConfig {
-            name: "candle".into(),
-        }],
-        "http://localhost:0",
-        &HarnessSettings::default(),
-    );
-
-    // Both entries fail synchronously inside the registry — no network
-    // call escapes (the harness lookup mismatches before hf-hub is
-    // touched). The function should still return cleanly.
-    let specs = vec![
-        ModelSpec {
-            model_id: "model-a".into(),
-            harness: "no-such-harness".into(),
-            quant: None,
-            tensor_parallel: None,
-            devices: None,
-        },
-        ModelSpec {
-            model_id: "model-b".into(),
-            harness: "no-such-harness".into(),
-            quant: None,
-            tensor_parallel: None,
-            devices: None,
-        },
-    ];
-
-    startup::load_default_models(&registry, &specs).await;
-
-    let listed = registry
-        .list_all_models()
-        .await
-        .expect("list_all_models should succeed");
-    assert!(
-        listed.is_empty(),
-        "no models should be loaded after failed entries"
-    );
-}
-
-#[tokio::test]
-async fn test_load_default_models_empty_is_noop() {
-    let registry = HarnessRegistry::new();
-    startup::load_default_models(&registry, &[]).await;
-}

crates/neuron/tests/api.rs

@@ -14,7 +14,6 @@ async fn spawn_neuron(discovery: DiscoveryResponse) -> String {
         discovery,
         health_cache,
         registry: RwLock::new(registry),
-        candle: None,
     });
 
     let app = api::neuron_routes().with_state(state);
@@ -136,30 +135,56 @@ async fn test_models_empty_registry() {
     assert!(body.as_array().unwrap().is_empty());
 }
 
-/// Verify the candle harness registers, list is empty by default, and a
+/// Spawn a mock mistral.rs backend and a neuron with the mistralrs harness
-/// load attempt for an obviously-bogus model id returns a 4xx error
+/// pointing at it, then test the full model lifecycle through neuron's API.
-/// without crashing the daemon. Real load/unload exercising actual GGUF
-/// download is covered by `tests/candle_lifecycle.rs` (cuda-integration).
 #[tokio::test]
-async fn test_candle_harness_registers_and_rejects_bogus_model() {
+async fn test_models_via_mistralrs_harness() {
+    use axum::routing::{get, post};
+    use axum::{Json, Router};
     use cortex_core::harness::HarnessConfig;
-    use neuron::config::HarnessSettings;
+    use serde_json::Value;
 
-    let registry = HarnessRegistry::from_configs(
+    // Mock mistral.rs backend.
-        &[HarnessConfig {
+    let mock_app = Router::new()
-            name: "candle".into(),
+        .route(
-        }],
+            "/v1/models",
-        "http://localhost:13131",
+            get(|| async {
-        &HarnessSettings::default(),
+                Json(json!({
-    );
+                    "data": [
+                        {"id": "test-model", "status": "loaded"},
+                        {"id": "other-model", "status": "unloaded"}
+                    ]
+                }))
+            }),
+        )
+        .route(
+            "/v1/models/unload",
+            post(|Json(_body): Json<Value>| async { Json(json!({"status": "ok"})) }),
+        )
+        .route(
+            "/v1/models/reload",
+            post(|Json(_body): Json<Value>| async { Json(json!({"status": "ok"})) }),
+        );
+
+    let mock_listener = tokio::net::TcpListener::bind("127.0.0.1:0").await.unwrap();
+    let mock_addr = mock_listener.local_addr().unwrap();
+    tokio::spawn(async move {
+        axum::serve(mock_listener, mock_app).await.unwrap();
+    });
+    let mock_url = format!("http://{mock_addr}");
+
+    // Build neuron with mistralrs harness pointing at mock.
+    let registry = HarnessRegistry::from_configs(&[HarnessConfig {
+        name: "mistralrs".into(),
+        endpoint: Some(mock_url.clone()),
+        systemd_unit: None,
+    }]);
 
-    let candle = registry.candle();
     let health_cache = Arc::new(HealthCache::new());
     let state = Arc::new(NeuronState {
         discovery: fake_discovery(),
         health_cache,
         registry: RwLock::new(registry),
-        candle,
     });
 
     let app = api::neuron_routes().with_state(state);
@@ -172,6 +197,7 @@ async fn test_candle_harness_registers_and_rejects_bogus_model() {
 
     let client = reqwest::Client::new();
 
+    // GET /models — should return models from mock mistralrs.
     let resp = client
         .get(format!("{neuron_url}/models"))
         .send()
@@ -179,140 +205,45 @@ async fn test_candle_harness_registers_and_rejects_bogus_model() {
         .unwrap();
     assert_eq!(resp.status(), 200);
     let models: Vec<serde_json::Value> = resp.json().await.unwrap();
-    assert!(models.is_empty());
+    assert_eq!(models.len(), 2);
+    assert_eq!(models[0]["id"], "test-model");
+    assert_eq!(models[0]["harness"], "mistralrs");
+    assert_eq!(models[0]["status"], "loaded");
+    assert_eq!(models[1]["id"], "other-model");
+    assert_eq!(models[1]["status"], "unloaded");
 
-    // Sending a wrong-harness spec should be rejected synchronously
+    // GET /models/test-model/endpoint — should return mock URL.
-    // without touching the network or the model registry.
+    let resp = client
+        .get(format!("{neuron_url}/models/test-model/endpoint"))
+        .send()
+        .await
+        .unwrap();
+    assert_eq!(resp.status(), 200);
+    let body: serde_json::Value = resp.json().await.unwrap();
+    assert_eq!(body["url"], mock_url);
+
+    // POST /models/unload — should succeed.
+    let resp = client
+        .post(format!("{neuron_url}/models/unload"))
+        .json(&json!({"model_id": "test-model"}))
+        .send()
+        .await
+        .unwrap();
+    assert_eq!(resp.status(), 200);
+    let body: serde_json::Value = resp.json().await.unwrap();
+    assert_eq!(body["status"], "unloaded");
+
+    // POST /models/load — should succeed.
     let resp = client
         .post(format!("{neuron_url}/models/load"))
-        .json(&json!({"model_id": "definitely/not-real", "harness": "not-candle"}))
-        .send()
-        .await
-        .unwrap();
-    assert_eq!(resp.status(), 400);
-
-    // Registry still empty.
-    let resp = client
-        .get(format!("{neuron_url}/models"))
-        .send()
-        .await
-        .unwrap();
-    let models: Vec<serde_json::Value> = resp.json().await.unwrap();
-    assert!(models.is_empty());
-}
-
-/// `/v1/chat/completions` returns 503 when no candle harness is registered.
-#[tokio::test]
-async fn test_chat_completions_no_candle_harness() {
-    let registry = HarnessRegistry::new();
-    let health_cache = Arc::new(HealthCache::new());
-    let state = Arc::new(NeuronState {
-        discovery: fake_discovery(),
-        health_cache,
-        registry: RwLock::new(registry),
-        candle: None,
-    });
-    let app = api::neuron_routes().with_state(state);
-    let listener = tokio::net::TcpListener::bind("127.0.0.1:0").await.unwrap();
-    let addr = listener.local_addr().unwrap();
-    tokio::spawn(async move {
-        axum::serve(listener, app).await.unwrap();
-    });
-    let url = format!("http://{addr}");
-
-    let resp = reqwest::Client::new()
-        .post(format!("{url}/v1/chat/completions"))
         .json(&json!({
-            "model": "anything",
+            "model_id": "test-model",
-            "messages": [{"role": "user", "content": "hi"}]
+            "harness": "mistralrs"
         }))
         .send()
         .await
         .unwrap();
-    assert_eq!(resp.status(), 503);
+    assert_eq!(resp.status(), 200);
-}
+    let body: serde_json::Value = resp.json().await.unwrap();
-
+    assert_eq!(body["status"], "loaded");
-/// `/v1/chat/completions` returns 404 when the requested model isn't loaded.
-#[tokio::test]
-async fn test_chat_completions_model_not_loaded() {
-    use cortex_core::harness::HarnessConfig;
-    use neuron::config::HarnessSettings;
-
-    let registry = HarnessRegistry::from_configs(
-        &[HarnessConfig {
-            name: "candle".into(),
-        }],
-        "http://localhost:0",
-        &HarnessSettings::default(),
-    );
-    let candle = registry.candle();
-    let health_cache = Arc::new(HealthCache::new());
-    let state = Arc::new(NeuronState {
-        discovery: fake_discovery(),
-        health_cache,
-        registry: RwLock::new(registry),
-        candle,
-    });
-    let app = api::neuron_routes().with_state(state);
-    let listener = tokio::net::TcpListener::bind("127.0.0.1:0").await.unwrap();
-    let addr = listener.local_addr().unwrap();
-    tokio::spawn(async move {
-        axum::serve(listener, app).await.unwrap();
-    });
-    let url = format!("http://{addr}");
-
-    let resp = reqwest::Client::new()
-        .post(format!("{url}/v1/chat/completions"))
-        .json(&json!({
-            "model": "definitely/not-loaded",
-            "messages": [{"role": "user", "content": "hi"}]
-        }))
-        .send()
-        .await
-        .unwrap();
-    assert_eq!(resp.status(), 404);
-}
-
-/// `/v1/chat/completions` with `stream: true` returns 404 when the
-/// model isn't loaded — same surface as the non-streaming path. The
-/// streaming code only kicks in once the model lookup succeeds.
-#[tokio::test]
-async fn test_chat_completions_streaming_model_not_loaded() {
-    use cortex_core::harness::HarnessConfig;
-    use neuron::config::HarnessSettings;
-
-    let registry = HarnessRegistry::from_configs(
-        &[HarnessConfig {
-            name: "candle".into(),
-        }],
-        "http://localhost:0",
-        &HarnessSettings::default(),
-    );
-    let candle = registry.candle();
-    let health_cache = Arc::new(HealthCache::new());
-    let state = Arc::new(NeuronState {
-        discovery: fake_discovery(),
-        health_cache,
-        registry: RwLock::new(registry),
-        candle,
-    });
-    let app = api::neuron_routes().with_state(state);
-    let listener = tokio::net::TcpListener::bind("127.0.0.1:0").await.unwrap();
-    let addr = listener.local_addr().unwrap();
-    tokio::spawn(async move {
-        axum::serve(listener, app).await.unwrap();
-    });
-    let url = format!("http://{addr}");
-
-    let resp = reqwest::Client::new()
-        .post(format!("{url}/v1/chat/completions"))
-        .json(&json!({
-            "model": "definitely/not-loaded",
-            "messages": [{"role": "user", "content": "hi"}],
-            "stream": true
-        }))
-        .send()
-        .await
-        .unwrap();
-    assert_eq!(resp.status(), 404);
 }

crates/neuron/tests/candle_lifecycle.rs

@@ -1,87 +0,0 @@
-//! Real model load/unload lifecycle through the candle harness.
-//!
-//! Gated behind the `cuda-integration` feature because it downloads a
-//! real (small) GGUF from HuggingFace and materialises tensors on the
-//! configured device. Run on a host with network access and either a
-//! CUDA GPU (when built with `--features cuda`) or enough CPU RAM to
-//! hold the model.
-//!
-//! Usage:
-//!   cargo test -p neuron --features cuda-integration --test candle_lifecycle
-//!
-//! Optional environment variables:
-//!   NEURON_TEST_MODEL_ID — HuggingFace repo to load (default: a small
-//!     public Qwen3 GGUF repo).
-//!   NEURON_TEST_QUANT    — quant substring matched against GGUF
-//!     filenames (default: "Q4_K_M").
-//!   HF_HOME              — HuggingFace cache directory.
-
-#![cfg(feature = "cuda-integration")]
-
-use cortex_core::harness::{HarnessConfig, ModelSpec};
-use neuron::config::HarnessSettings;
-use neuron::harness::HarnessRegistry;
-use std::path::PathBuf;
-
-#[tokio::test]
-async fn test_candle_qwen3_load_unload_lifecycle() {
-    let _ = tracing_subscriber::fmt()
-        .with_test_writer()
-        .with_env_filter("info,neuron=debug")
-        .try_init();
-
-    let model_id = std::env::var("NEURON_TEST_MODEL_ID")
-        .unwrap_or_else(|_| "Qwen/Qwen3-0.6B-GGUF".to_string());
-    let quant = std::env::var("NEURON_TEST_QUANT").unwrap_or_else(|_| "Q4_K_M".to_string());
-
-    let mut settings = HarnessSettings::default();
-    if let Ok(home) = std::env::var("HF_HOME") {
-        settings.candle.hf_cache = Some(PathBuf::from(home));
-    }
-
-    let registry = HarnessRegistry::from_configs(
-        &[HarnessConfig {
-            name: "candle".into(),
-        }],
-        "http://localhost:13131",
-        &settings,
-    );
-
-    let spec = ModelSpec {
-        model_id: model_id.clone(),
-        harness: "candle".into(),
-        quant: Some(quant),
-        tensor_parallel: None,
-        devices: Some(vec![0]),
-    };
-
-    registry
-        .load_model(&spec)
-        .await
-        .expect("load_model should succeed");
-
-    let models = registry.list_all_models().await.expect("list_all_models");
-    assert_eq!(models.len(), 1, "expected exactly one loaded model");
-    assert_eq!(models[0].id, model_id);
-    assert_eq!(models[0].harness, "candle");
-    assert_eq!(models[0].status, "loaded");
-
-    let url = registry.inference_endpoint(&model_id).await;
-    assert_eq!(url, Some("http://localhost:13131".into()));
-
-    // Re-loading the same model should be rejected.
-    let again = registry.load_model(&spec).await;
-    assert!(again.is_err(), "second load should error");
-
-    registry
-        .unload_model(&model_id)
-        .await
-        .expect("unload_model should succeed");
-
-    let models = registry.list_all_models().await.expect("list_all_models");
-    assert!(models.is_empty(), "registry should be empty after unload");
-
-    // Unloading a model that isn't loaded should error.
-    let err = registry.unload_model(&model_id).await;
-    assert!(err.is_err(), "unload of missing model should error");
-}

crates/neuron/tests/shutdown.rs

@@ -1,32 +0,0 @@
-//! Deactivation behaviour: unload_all_models tolerates an empty
-//! registry and continues past per-model unload failures.
-
-use cortex_core::harness::HarnessConfig;
-use neuron::config::HarnessSettings;
-use neuron::harness::HarnessRegistry;
-use neuron::startup;
-
-#[tokio::test]
-async fn test_unload_all_models_empty_registry_is_noop() {
-    let registry = HarnessRegistry::new();
-    startup::unload_all_models(&registry).await;
-}
-
-#[tokio::test]
-async fn test_unload_all_models_with_no_loaded_models() {
-    let registry = HarnessRegistry::from_configs(
-        &[HarnessConfig {
-            name: "candle".into(),
-        }],
-        "http://localhost:0",
-        &HarnessSettings::default(),
-    );
-
-    startup::unload_all_models(&registry).await;
-
-    let listed = registry
-        .list_all_models()
-        .await
-        .expect("list_all_models should still succeed after shutdown cleanup");
-    assert!(listed.is_empty());
-}

crates/neuron/tests/tp_worker_lifecycle.rs

@@ -1,145 +0,0 @@
-//! Stage 7a-i: confirm the TP worker subprocess lifecycle round-trips.
-//!
-//! Spawns two worker subprocesses via the leader→worker stdio RPC,
-//! pings each, and cleanly shuts them down. No CUDA required —
-//! `Init` and `NcclSanityCheck` are stubbed in 7a-i, so this test
-//! runs on any host the workspace builds on.
-
-use neuron::harness::tp::{WorkerPool, rpc::WorkerResponse};
-
-/// Path to the neuron binary built by cargo for this test process.
-/// cargo populates `CARGO_BIN_EXE_neuron` at compile time for sibling-
-/// binary tests; production paths in main.rs use `/proc/self/exe`.
-const NEURON_BIN: &str = env!("CARGO_BIN_EXE_neuron");
-
-/// Two workers (so we spawn one subprocess: rank 0 is in-process,
-/// rank 1 is the child). Verify the spawned worker responds to Ping
-/// with its own identity, then shut it down cleanly.
-#[tokio::test]
-async fn test_spawn_ping_shutdown() {
-    // cuda_devices: rank 0 → device 0 (leader, unused here),
-    //               rank 1 → device 1 (worker; not actually opened in 7a-i).
-    let mut pool = WorkerPool::spawn(NEURON_BIN.as_ref(), 2, &[0, 1])
-        .await
-        .expect("spawn worker pool");
-
-    let pongs = pool.ping_all().await.expect("ping all workers");
-    assert_eq!(pongs.len(), 1, "expected one Pong (rank 1 only)");
-    match &pongs[0] {
-        WorkerResponse::Pong {
-            rank,
-            world_size,
-            cuda_device,
-        } => {
-            assert_eq!(*rank, 1);
-            assert_eq!(*world_size, 2);
-            assert_eq!(*cuda_device, 1);
-        }
-        other => panic!("expected Pong, got {other:?}"),
-    }
-
-    pool.shutdown().await.expect("clean shutdown");
-}
-
-/// Three workers — exercise the loop in `ping_all` / `shutdown`.
-#[tokio::test]
-async fn test_spawn_three_workers() {
-    let mut pool = WorkerPool::spawn(NEURON_BIN.as_ref(), 3, &[0, 1, 2])
-        .await
-        .expect("spawn worker pool");
-
-    let pongs = pool.ping_all().await.expect("ping all workers");
-    assert_eq!(pongs.len(), 2, "expected two Pongs (ranks 1 and 2)");
-    for (i, resp) in pongs.iter().enumerate() {
-        match resp {
-            WorkerResponse::Pong {
-                rank,
-                world_size,
-                cuda_device,
-            } => {
-                let expected_rank = (i + 1) as u32;
-                assert_eq!(*rank, expected_rank);
-                assert_eq!(*world_size, 3);
-                assert_eq!(*cuda_device, expected_rank);
-            }
-            other => panic!("expected Pong, got {other:?}"),
-        }
-    }
-
-    pool.shutdown().await.expect("clean shutdown");
-}
-
-/// 7a-ii: without the cuda feature, Init must fail with a clear
-/// `cuda_feature_not_enabled` marker rather than silently succeeding.
-/// This is the local-dev-box test; the real NCCL handshake is exercised
-/// by `tp_worker_lifecycle_cuda.rs` (gated on `cuda-integration`).
-#[tokio::test]
-async fn test_init_returns_cuda_feature_not_enabled_without_cuda() {
-    use neuron::harness::tp::rpc::WorkerRequest;
-    use std::process::Stdio;
-    use tokio::io::{AsyncBufReadExt, AsyncWriteExt, BufReader};
-    use tokio::process::Command;
-
-    // Spawn a single worker by hand to send Init directly (the pool's
-    // public API doesn't expose Init yet — that lands in 7a-ii).
-    let mut child = Command::new(NEURON_BIN)
-        .arg("--worker")
-        .arg("--rank")
-        .arg("1")
-        .arg("--tp-size")
-        .arg("2")
-        .arg("--cuda-device")
-        .arg("1")
-        .stdin(Stdio::piped())
-        .stdout(Stdio::piped())
-        .stderr(Stdio::null())
-        .kill_on_drop(true)
-        .spawn()
-        .expect("spawn worker");
-
-    let mut stdin = child.stdin.take().expect("stdin");
-    let stdout = child.stdout.take().expect("stdout");
-    let mut lines = BufReader::new(stdout).lines();
-
-    let req = WorkerRequest::Init {
-        comm_id: "ff".repeat(128),
-    };
-    let mut payload = serde_json::to_string(&req).unwrap();
-    payload.push('\n');
-    stdin.write_all(payload.as_bytes()).await.unwrap();
-    stdin.flush().await.unwrap();
-
-    let reply = lines
-        .next_line()
-        .await
-        .expect("read line")
-        .expect("got line");
-    let resp: WorkerResponse = serde_json::from_str(&reply).expect("parse reply");
-    match resp {
-        WorkerResponse::Error { kind, .. } => {
-            #[cfg(feature = "cuda")]
-            {
-                // With cuda enabled the response depends on whether
-                // CUDA hardware is actually present. Accept either
-                // the success contract or a real NCCL failure.
-                let _ = kind;
-            }
-            #[cfg(not(feature = "cuda"))]
-            assert_eq!(kind, "cuda_feature_not_enabled");
-        }
-        WorkerResponse::InitOk => {
-            // Real NCCL succeeded — only possible with cuda feature
-            // AND a working NCCL stack AND another rank actually
-            // joining. Don't fail; just acknowledge.
-            #[cfg(not(feature = "cuda"))]
-            panic!("InitOk without cuda feature is impossible");
-        }
-        other => panic!("expected Error or InitOk, got {other:?}"),
-    }
-
-    // Clean shutdown.
-    stdin.write_all(b"{\"op\":\"shutdown\"}\n").await.unwrap();
-    stdin.flush().await.unwrap();
-    let _ = lines.next_line().await; // Bye
-    let _ = child.wait().await;
-}

crates/neuron/tests/tp_worker_lifecycle_cuda.rs

@@ -1,43 +0,0 @@
-//! Stage 7a-ii: real NCCL handshake across the worker pool.
-//!
-//! Gated behind the `cuda-integration` feature because it requires
-//! libnccl AND multiple CUDA devices on the running host. Run on
-//! beast (2× RTX 5090) via:
-//!
-//!   cargo test -p neuron --features cuda-integration \
-//!         --test tp_worker_lifecycle_cuda
-//!
-//! Steps: spawn N-1 workers, call `init_nccl`, run `nccl_sanity_check`
-//! (every rank `all_reduce`s `1u32` with Sum; expected total =
-//! world_size), shut down cleanly.
-
-#![cfg(feature = "cuda-integration")]
-
-use neuron::harness::tp::WorkerPool;
-
-const NEURON_BIN: &str = env!("CARGO_BIN_EXE_neuron");
-
-#[tokio::test]
-async fn test_init_and_sanity_check_two_ranks() {
-    let _ = tracing_subscriber::fmt()
-        .with_test_writer()
-        .with_env_filter("info,neuron=debug")
-        .try_init();
-
-    // 2 ranks: leader = rank 0 on device 0, worker = rank 1 on device 1.
-    let mut pool = WorkerPool::spawn(NEURON_BIN.as_ref(), 2, &[0, 1])
-        .await
-        .expect("spawn worker pool");
-
-    pool.ping_all().await.expect("pong all workers");
-
-    pool.init_nccl(0)
-        .await
-        .expect("init_nccl: NCCL handshake across all ranks");
-
-    pool.nccl_sanity_check()
-        .await
-        .expect("nccl_sanity_check: observed_sum == world_size on all ranks");
-
-    pool.shutdown().await.expect("clean shutdown");
-}

data/cortex-firewalld.xml

@@ -1,7 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<service>
-  <short>cortex</short>
-  <description>Cortex — inference gateway for multi-node GPU clusters</description>
-  <port protocol="tcp" port="31313"/>
-  <port protocol="tcp" port="31314"/>
-</service>

data/neuron-firewalld.xml

@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<service>
-  <short>helexa-neuron</short>
-  <description>Neuron — per-node GPU discovery and harness daemon for cortex</description>
-  <port protocol="tcp" port="13131"/>
-</service>

data/neuron.service

@@ -10,22 +10,6 @@ Restart=on-failure
 RestartSec=5
 User=neuron
 Group=neuron
-# /var/lib/neuron is the neuron user's $HOME — hf-hub writes its
-# default cache there (~/.cache/huggingface/hub). Without this directive
-# systemd doesn't create the directory and hf-hub downloads fail with
-# "fetch GGUF <file>: failed to create cache dir".
-StateDirectory=neuron
-StateDirectoryMode=0755
-# Loading default_models from neuron.toml happens before the HTTP
-# listener binds; large models can take many minutes to download and
-# materialise on first activation. systemd's default TimeoutStartSec
-# (90s) is far too short; allow 30 minutes.
-TimeoutStartSec=1800s
-# On stop, neuron drains in-flight requests then unloads every model
-# to release CUDA contexts cleanly. Allow generous time for big-model
-# unloads; systemd will SIGKILL after this bound.
-TimeoutStopSec=120s
-KillSignal=SIGTERM
 
 [Install]
 WantedBy=multi-user.target

models.example.toml

@@ -6,7 +6,7 @@
 
 [[models]]
 id = "your-org/large-model"
-harness = "candle"
+harness = "mistralrs"
 quant = "Q4_K_M"
 vram_mb = 19000
 min_devices = 2
@@ -15,7 +15,7 @@ pinned_on = ["gpu-large"]
 
 [[models]]
 id = "your-org/medium-model"
-harness = "candle"
+harness = "mistralrs"
 quant = "Q6_K"
 vram_mb = 12000
 min_devices = 1
@@ -23,7 +23,7 @@ pinned_on = ["gpu-medium"]
 
 [[models]]
 id = "your-org/embedding-model"
-harness = "candle"
+harness = "mistralrs"
 quant = "Q8_0"
 vram_mb = 8000
 min_devices = 1

neuron.example.toml

@@ -3,38 +3,14 @@
 # Copy to /etc/neuron/neuron.toml and adjust for your environment.
 #
 # Environment variable overrides use NEURON_ prefix with __ separators:
-#   NEURON_PORT=13131
+#   NEURON_PORT=9090
 
-port = 13131
+port = 9090
 
 # -- Harnesses ---------------------------------------------------------------
-# Each [[harnesses]] entry enables an inference engine. Currently only
+# Each [[harnesses]] entry declares an inference engine managed by neuron.
-# "candle" is supported — it runs in-process and uses huggingface/candle
-# for inference on local CUDA devices (or CPU when CUDA is unavailable).
 
 [[harnesses]]
-name = "candle"
+name = "mistralrs"
-
+endpoint = "http://localhost:8080"
-# -- Candle harness settings -------------------------------------------------
+systemd_unit = "mistralrs.service"
-# Optional tuning for the candle harness.
-
-[harness.candle]
-# HuggingFace cache directory for model weights. When unset, hf-hub's
-# default (~/.cache/huggingface) is used.
-# hf_cache = "/var/lib/neuron/hf-cache"
-
-# -- Default models ----------------------------------------------------------
-# Models listed here are loaded automatically when the neuron service
-# activates. Loading is sequential — a slow or failing entry doesn't
-# block the rest of the fleet, but it does push out the time before
-# neuron starts serving HTTP, so keep the list short. Operators can
-# load additional models on demand via POST /models/load.
-#
-# Make sure data/neuron.service's TimeoutStartSec is generous enough to
-# cover the slowest entry's first-time download + materialisation.
-
-# [[default_models]]
-# model_id = "Qwen/Qwen3-0.6B-GGUF"
-# harness = "candle"
-# quant = "Q4_K_M"
-# devices = [0]

neuron.spec

@@ -1,10 +1,7 @@
-Name:           helexa-neuron
+Name:           neuron
-Version:        0.1.16
+Version:        0.1.7
 Release:        1%{?dist}
 Summary:        Per-node GPU discovery and harness management daemon for cortex
-# Package name disambiguates from Fedora's existing "neuron" package
-# (NEURON neural simulation environment from Yale). Binary, systemd
-# unit, and system user are still called "neuron" for brevity.
 
 License:        GPL-3.0-or-later
 URL:            https://git.lair.cafe/helexa/cortex
@@ -24,22 +21,11 @@ BuildRequires:  systemd-rpm-macros
 
 Requires(pre):  shadow-utils
 Requires:       systemd
-Requires:       firewalld-filesystem
-
-# systemd-rpm-macros ships a unit dep generator that parses User=/Group=
-# from our .service file and emits Requires: user(neuron)/group(neuron).
-# rpm's sysusers provides-generator emits the unversioned form for groups
-# but only a versioned user(neuron) = <base64> for users with GECOS/home/
-# shell. Provide the unversioned user(neuron) explicitly so dnf can resolve
-# the auto-generated Requires. Without this, dnf5 silently filters the
-# package and reports "Nothing to do".
-Provides:       user(neuron)
 
 %description
 Neuron is a per-node daemon for cortex inference clusters. It discovers
-local GPU hardware via nvidia-smi, runs in-process inference via
+local GPU hardware via nvidia-smi, manages inference harnesses (mistral.rs,
-huggingface/candle, and exposes an HTTP API for model lifecycle
+llama.cpp), and exposes an HTTP API for model lifecycle management.
-management (load, unload, list, inference endpoint).
 
 %prep
 %autosetup
@@ -60,7 +46,6 @@ cargo build --release -p neuron
 install -Dm755 target/release/neuron %{buildroot}%{_bindir}/neuron
 install -Dm644 data/neuron.service %{buildroot}%{_unitdir}/neuron.service
 install -Dm644 data/neuron-sysusers.conf %{buildroot}%{_sysusersdir}/neuron.conf
-install -Dm644 data/neuron-firewalld.xml %{buildroot}%{_prefix}/lib/firewalld/services/helexa-neuron.xml
 install -dm755 %{buildroot}%{_sysconfdir}/neuron
 install -Dm644 neuron.example.toml %{buildroot}%{_sysconfdir}/neuron/neuron.toml
 
@@ -82,20 +67,9 @@ install -Dm644 neuron.example.toml %{buildroot}%{_sysconfdir}/neuron/neuron.toml
 %{_bindir}/neuron
 %{_unitdir}/neuron.service
 %{_sysusersdir}/neuron.conf
-%{_prefix}/lib/firewalld/services/helexa-neuron.xml
 %dir %{_sysconfdir}/neuron
 %config(noreplace) %{_sysconfdir}/neuron/neuron.toml
 
 %changelog
-* Thu Apr 16 2026 Gitea Actions <actions@git.lair.cafe> - 0.1.16-1
+* Tue Apr 15 2026 Rob Thijssen <grenade@rob.tn> - 0.1.0-1
-- chore: ignore local deploy script
-- chore: move default ports out of common-collision ranges
-- ci: drop actions/cache for cargo registry and target
-
-* Thu Apr 16 2026 Gitea Actions <actions@git.lair.cafe> - 0.1.14-1
-- ci: publish both packages to a single helexa/helexa COPR project
-- fix(rpm): rename neuron package to helexa-neuron
-- ci: commit generated %changelog entries back to main
-
-* Wed Apr 15 2026 Rob Thijssen <grenade@rob.tn> - 0.1.0-1
 - Initial package

rpm/cortex-prerelease.spec

@@ -1,106 +0,0 @@
-# Prebuilt-binary spec for cortex.
-#
-# Unlike cortex.spec (which builds from source via cargo), this spec
-# wraps a pre-built `cortex` binary produced by an upstream CI job and
-# packages it for rpm.lair.cafe. The %build phase is a no-op.
-#
-# Required defines at rpmbuild time:
-#   cortex_version    e.g. "0.1.16"
-#   cortex_prerelease e.g. "0.1.20260518140530.gitabcdef0"
-#                            ^^^^^^^^^^^^^^^^^^ ^^^^^^^^
-#                            commit time (sec)  commit sha
-#                           (used as Release; the timestamp prefix
-#                            keeps same-day builds strictly ordered.)
-
-%global _build_id_links none
-%global debug_package %{nil}
-%global __strip /usr/bin/true
-
-%{!?cortex_version: %global cortex_version 0.0.0}
-%if 0%{?cortex_prerelease:1}
-%global cortex_release %{cortex_prerelease}
-%else
-%global cortex_release 1
-%endif
-
-Name:           cortex
-Version:        %{cortex_version}
-Release:        %{cortex_release}%{?dist}
-Summary:        Inference gateway for multi-node GPU clusters (prebuilt)
-
-License:        GPL-3.0-or-later
-URL:            https://git.lair.cafe/helexa/cortex
-
-Source0:        cortex
-Source1:        cortex.service
-Source2:        cortex-sysusers.conf
-Source3:        cortex-firewalld.xml
-Source4:        cortex.example.toml
-Source5:        models.example.toml
-Source6:        LICENSE
-
-ExclusiveArch:  x86_64
-
-Requires(pre):  shadow-utils
-Requires:       systemd
-Requires:       firewalld-filesystem
-
-Provides:       user(cortex)
-
-%description
-Cortex is a Rust reverse-proxy that sits in front of multiple neuron
-inference daemons and presents a unified OpenAI and Anthropic
-compatible API surface.
-
-This package wraps a binary built upstream in CI; the source-build
-spec (cortex.spec) remains available for stable releases.
-
-%prep
-cp %{SOURCE0} ./cortex
-cp %{SOURCE1} .
-cp %{SOURCE2} .
-cp %{SOURCE3} .
-cp %{SOURCE4} .
-cp %{SOURCE5} .
-cp %{SOURCE6} .
-
-%build
-# Already built in the upstream CI build job.
-
-%install
-install -Dm755 cortex %{buildroot}%{_bindir}/cortex
-install -Dm644 cortex.service %{buildroot}%{_unitdir}/cortex.service
-install -Dm644 cortex-sysusers.conf %{buildroot}%{_sysusersdir}/cortex.conf
-install -Dm644 cortex-firewalld.xml %{buildroot}%{_prefix}/lib/firewalld/services/cortex.xml
-install -dm755 %{buildroot}%{_sysconfdir}/cortex
-install -Dm644 cortex.example.toml %{buildroot}%{_sysconfdir}/cortex/cortex.toml
-install -Dm644 models.example.toml %{buildroot}%{_sysconfdir}/cortex/models.toml
-
-%pre
-getent group cortex >/dev/null || groupadd -r cortex
-getent passwd cortex >/dev/null || \
-    useradd -r -g cortex -d /var/lib/cortex -s /sbin/nologin \
-        -c "Cortex inference gateway" cortex
-
-%post
-%systemd_post cortex.service
-
-%preun
-%systemd_preun cortex.service
-
-%postun
-%systemd_postun_with_restart cortex.service
-
-%files
-%license LICENSE
-%{_bindir}/cortex
-%{_unitdir}/cortex.service
-%{_sysusersdir}/cortex.conf
-%{_prefix}/lib/firewalld/services/cortex.xml
-%dir %{_sysconfdir}/cortex
-%config(noreplace) %{_sysconfdir}/cortex/cortex.toml
-%config(noreplace) %{_sysconfdir}/cortex/models.toml
-
-%changelog
-* Mon May 18 2026 Gitea Actions <actions@git.lair.cafe> - %{cortex_version}-%{cortex_release}
-- Prerelease build from upstream CI binary.

rpm/helexa-neuron-prerelease.spec

@@ -1,126 +0,0 @@
-# Prebuilt-binary spec for helexa-neuron flavoured by CUDA compute capability.
-#
-# Unlike helexa-neuron.spec (which builds from source via cargo), this
-# spec wraps a pre-built `neuron-{flavour}` binary produced by an
-# upstream CI job and packages it for rpm.lair.cafe. The %build phase
-# is a no-op.
-#
-# Required defines at rpmbuild time:
-#   neuron_version    e.g. "0.1.16"
-#   neuron_flavour    e.g. "ada", "blackwell"  — matches the CI build
-#                     matrix's compute_cap label.
-#   neuron_prerelease e.g. "0.1.20260518140530.gitabcdef0"
-#                            ^^^^^^^^^^^^^^^^^^ ^^^^^^^^
-#                            commit time (sec)  commit sha
-#                           (used as Release; the timestamp prefix
-#                            keeps same-day builds strictly ordered.)
-#
-# One flavour can be installed at a time on a given host; flavour
-# packages Conflict with each other.
-
-%global _build_id_links none
-%global debug_package %{nil}
-%global __strip /usr/bin/true
-
-%{!?neuron_version: %global neuron_version 0.0.0}
-%{!?neuron_flavour: %global neuron_flavour blackwell}
-%if 0%{?neuron_prerelease:1}
-%global neuron_release %{neuron_prerelease}
-%else
-%global neuron_release 1
-%endif
-
-Name:           helexa-neuron-%{neuron_flavour}
-Version:        %{neuron_version}
-Release:        %{neuron_release}%{?dist}
-Summary:        Per-node GPU inference daemon (candle, %{neuron_flavour} flavour)
-
-License:        GPL-3.0-or-later
-URL:            https://git.lair.cafe/helexa/cortex
-
-Source0:        neuron-%{neuron_flavour}
-Source1:        neuron.service
-Source2:        neuron-sysusers.conf
-Source3:        neuron-firewalld.xml
-Source4:        neuron.example.toml
-Source5:        LICENSE
-
-ExclusiveArch:  x86_64
-
-# Binary links against the CUDA runtime, cuDNN, NCCL, etc. Suppress
-# auto-detected exact soname deps — users may have CUDA from various
-# sources (rpmfusion, nvidia-direct) at different compatible versions;
-# a runtime dlopen failure surfaces a clearer error than rpm dep
-# resolution would.
-%global __requires_exclude ^lib(cuda|cudart|cudnn|cublas|cublasLt|curand|nvrtc|nccl)
-
-Requires(pre):  shadow-utils
-Requires:       systemd
-Requires:       firewalld-filesystem
-
-Provides:       helexa-neuron = %{neuron_version}-%{neuron_release}
-Provides:       user(neuron)
-
-# Mutual exclusion across flavours and the source-build variant.
-Conflicts:      helexa-neuron
-Conflicts:      helexa-neuron-ada
-Conflicts:      helexa-neuron-ampere
-Conflicts:      helexa-neuron-blackwell
-# (The Conflicts: with self is filtered by rpm at install time.)
-
-%description
-Neuron is the per-node daemon for cortex inference clusters. It
-discovers local GPU hardware via nvidia-smi, runs in-process
-inference via huggingface/candle, and exposes an HTTP API for model
-lifecycle management (load, unload, list, inference endpoint).
-
-This is the %{neuron_flavour} flavour, built for that CUDA compute
-capability. Install the flavour matching the GPUs on this host.
-
-%prep
-cp %{SOURCE0} ./neuron
-cp %{SOURCE1} .
-cp %{SOURCE2} .
-cp %{SOURCE3} .
-cp %{SOURCE4} .
-cp %{SOURCE5} .
-
-%build
-# Already built in the upstream CI build job (with --features cuda).
-
-%install
-install -Dm755 neuron %{buildroot}%{_bindir}/neuron
-install -Dm644 neuron.service %{buildroot}%{_unitdir}/neuron.service
-install -Dm644 neuron-sysusers.conf %{buildroot}%{_sysusersdir}/neuron.conf
-install -Dm644 neuron-firewalld.xml %{buildroot}%{_prefix}/lib/firewalld/services/helexa-neuron.xml
-install -dm755 %{buildroot}%{_sysconfdir}/neuron
-install -Dm644 neuron.example.toml %{buildroot}%{_sysconfdir}/neuron/neuron.toml
-
-%pre
-getent group neuron >/dev/null || groupadd -r neuron
-getent passwd neuron >/dev/null || \
-    useradd -r -g neuron -d /var/lib/neuron -s /sbin/nologin \
-        -G video,render \
-        -c "Neuron GPU node daemon" neuron
-
-%post
-%systemd_post neuron.service
-
-%preun
-%systemd_preun neuron.service
-
-%postun
-%systemd_postun_with_restart neuron.service
-
-%files
-%license LICENSE
-%{_bindir}/neuron
-%{_unitdir}/neuron.service
-%{_sysusersdir}/neuron.conf
-%{_prefix}/lib/firewalld/services/helexa-neuron.xml
-%dir %{_sysconfdir}/neuron
-%config(noreplace) %{_sysconfdir}/neuron/neuron.toml
-
-%changelog
-* Mon May 18 2026 Gitea Actions <actions@git.lair.cafe> - %{neuron_version}-%{neuron_release}
-- Prerelease build from upstream CI binary (%{neuron_flavour} flavour).

rpm/rpmmacros

@@ -1 +0,0 @@
-%_openpgp_sign_id @GPG_NAME@

script/deploy.sh

@@ -1,256 +0,0 @@
-#!/bin/env bash
-#
-# Rolling deploy across the helexa fleet, driven by asset/manifest.yml.
-# Installs / upgrades cortex on the gateway host and the appropriate
-# helexa-neuron-<flavour> package on each neuron host, then restarts
-# their services.
-
-set -euo pipefail
-
-SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-REPO_DIR="$(cd "${SCRIPT_DIR}/.." && pwd)"
-MANIFEST="${REPO_DIR}/asset/manifest.yml"
-
-if [[ ! -f "${MANIFEST}" ]]; then
-    echo "fatal: manifest not found at ${MANIFEST}" >&2
-    exit 1
-fi
-
-# Parse the manifest with yq. NOTE: this expects the pip-installed yq
-# (a jq wrapper using jq syntax) — `pip install yq`. The Fedora rpm
-# `yq` is mikefarah/yq and uses different (yaml-native) syntax; if a
-# host has that one instead these queries will fail.
-cortex_host=$(yq -r '.cortex.host' "${MANIFEST}")
-
-# Emit one TAB-separated 'host\tflavour' line per neuron.
-mapfile -t neuron_entries < <(
-    yq -r '.neurons[] | .host + "\t" + .flavour' "${MANIFEST}"
-)
-
-# Return the installed package's "version-release" string, or
-# "(not installed)" when rpm reports the package as absent. Capture
-# rpm's output into a variable so its "package X is not installed"
-# stdout message (rpm writes that to stdout, not stderr, when -q fails)
-# doesn't leak into the result.
-installed_nvr() {
-    local host="$1" pkg="$2"
-    local nvr
-    if nvr=$(ssh "${host}" "rpm -q --qf '%{version}-%{release}' ${pkg} 2>/dev/null"); then
-        echo "${nvr}"
-    else
-        echo "(not installed)"
-    fi
-}
-
-# Ensure the rpm.lair.cafe unstable repo is configured AND enabled on
-# the remote host.
-#
-# The upstream .repo file at https://rpm.lair.cafe/lair-cafe-unstable.repo
-# ships with `enabled=0` so a host that just fetched it won't start
-# pulling unstable packages by accident. We have to explicitly flip
-# enabled=1 via `dnf config-manager setopt`. Both addrepo and setopt
-# are idempotent.
-#
-# Non-fatal — if either step fails the subsequent `dnf install` will
-# surface a clearer diagnostic on its own.
-ensure_lair_repo() {
-    local host="$1"
-    if ! ssh "${host}" "test -f /etc/yum.repos.d/lair-cafe-unstable.repo" 2>/dev/null; then
-        echo "[${host}] adding rpm.lair.cafe unstable repo"
-        if ! ssh "${host}" sudo dnf config-manager addrepo \
-            --from-repofile=https://rpm.lair.cafe/lair-cafe-unstable.repo \
-            >/dev/null 2>&1; then
-            echo "[${host}] WARNING: failed to add lair.cafe repo file (proceeding anyway)"
-            return 0
-        fi
-    fi
-    # The .repo file ships enabled=0; flip it on. Cheap, idempotent.
-    if ! ssh "${host}" sudo dnf config-manager setopt \
-        lair-cafe-unstable.enabled=1 >/dev/null 2>&1; then
-        echo "[${host}] WARNING: failed to enable lair-cafe-unstable (proceeding anyway)"
-    fi
-}
-
-# Ensure libcudnn.so.9 is resolvable on the remote host so the
-# neuron binary (built with --features cudnn) doesn't fail at startup
-# with "cannot open shared object file: No such file or directory".
-#
-# Probes ldconfig first — if cuDNN was installed manually (.tar/.run
-# install), it'll be cached by ldconfig and we don't touch it.
-# Otherwise adds NVIDIA's RHEL9 CUDA repo (the Fedora 43 CUDA repo
-# doesn't ship cuDNN packages — only the RHEL9 one does) and installs
-# libcudnn9-cuda-13.
-ensure_cudnn_runtime() {
-    local host="$1"
-    if ssh "${host}" "ldconfig -p | grep -q libcudnn.so.9" 2>/dev/null; then
-        return 0
-    fi
-    echo "[${host}] installing cuDNN runtime"
-    if ! ssh "${host}" "test -f /etc/yum.repos.d/cuda-rhel9-x86_64.repo" 2>/dev/null; then
-        if ! ssh "${host}" sudo dnf config-manager addrepo \
-            --from-repofile=https://developer.download.nvidia.com/compute/cuda/repos/rhel9/x86_64/cuda-rhel9.repo \
-            >/dev/null 2>&1; then
-            echo "[${host}] WARNING: failed to add rhel9 CUDA repo (proceeding anyway)"
-        fi
-    fi
-    if ! ssh "${host}" sudo dnf install -y libcudnn9-cuda-13 >/dev/null 2>&1; then
-        echo "[${host}] WARNING: failed to install libcudnn9-cuda-13"
-        echo "[${host}]   neuron may fail to start; install cuDNN manually if so"
-    fi
-}
-
-# True when the named package needs to be installed or upgraded on the
-# remote host — either it's not present, or a newer version exists in
-# the repo. False only when the installed version is current.
-#
-# `dnf check-update <pkg>` returns 0 when the package isn't installed
-# at all (there's nothing to update), so we have to probe with rpm -q
-# first to distinguish "absent" from "current". Other dnf failures
-# collapse into "needs update" so the subsequent install step surfaces
-# the real diagnostic rather than this check swallowing it.
-needs_update() {
-    local host="$1" pkg="$2"
-    # Not installed → needs work.
-    if ! ssh "${host}" "rpm -q ${pkg}" >/dev/null 2>&1; then
-        return 0
-    fi
-    # Installed; ask dnf whether the repo has something newer.
-    if ssh "${host}" sudo dnf check-update --refresh -q "${pkg}" >/dev/null 2>&1; then
-        return 1
-    else
-        return 0
-    fi
-}
-
-# True if the named package is currently installed on the remote host.
-# Used to decide between `dnf install` (fresh) and `dnf upgrade` (stale):
-# dnf5's `install` is a no-op when the package is already present at
-# any version — it does NOT auto-upgrade to the latest available — so
-# the wrong command silently leaves the host on an old build.
-is_installed() {
-    local host="$1" pkg="$2"
-    ssh "${host}" "rpm -q ${pkg}" >/dev/null 2>&1
-}
-
-# Install or upgrade the named package on the remote, picking the
-# right dnf verb based on the installed-or-not state. Returns 0 with
-# dnf's combined stdout/stderr captured in __DNF_OUTPUT__ on success,
-# and 1 with the same captured output on failure.
-__DNF_OUTPUT__=""
-install_or_upgrade() {
-    local host="$1" pkg="$2"
-    local cmd
-    if is_installed "${host}" "${pkg}"; then
-        cmd="upgrade"
-    else
-        cmd="install"
-    fi
-    if __DNF_OUTPUT__=$(
-        ssh "${host}" sudo dnf "${cmd}" --refresh --allowerasing -y "${pkg}" 2>&1
-    ); then
-        return 0
-    else
-        return 1
-    fi
-}
-
-# ---------------------------------------------------------------------------
-# cortex (gateway)
-# ---------------------------------------------------------------------------
-
-ensure_lair_repo "${cortex_host}"
-cortex_nvr=$(installed_nvr "${cortex_host}" cortex)
-if needs_update "${cortex_host}" cortex; then
-    echo "[${cortex_host}] cortex update available (current: ${cortex_nvr})"
-    # Stop the service only if the unit file exists — fresh installs
-    # don't have it, and `systemctl stop` on a missing unit returns
-    # non-zero, which would otherwise short-circuit the install branch
-    # under set -e.
-    if ssh "${cortex_host}" "[ ! -f /usr/lib/systemd/system/cortex.service ] || sudo systemctl stop cortex.service"; then
-        echo "[${cortex_host}] stopped cortex service"
-        if install_or_upgrade "${cortex_host}" cortex; then
-            cortex_nvr=$(installed_nvr "${cortex_host}" cortex)
-            echo "[${cortex_host}] installed/upgraded cortex to ${cortex_nvr}"
-        else
-            echo "[${cortex_host}] failed to install/upgrade cortex:"
-            echo "${__DNF_OUTPUT__}" | sed "s/^/[${cortex_host}]   /"
-        fi
-    else
-        echo "[${cortex_host}] failed to stop cortex service"
-    fi
-else
-    echo "[${cortex_host}] cortex is up to date (${cortex_nvr})"
-    ssh "${cortex_host}" sudo systemctl stop cortex.service || true
-fi
-
-# Sync cortex.toml whether the package was upgraded or not — the config
-# can change without a package bump.
-if rsync \
-    --archive \
-    --compress \
-    --rsync-path 'sudo rsync' \
-    --chown root:root \
-    --chmod 644 \
-    "${REPO_DIR}/cortex.toml" \
-    "${cortex_host}:/etc/cortex/cortex.toml"; then
-    echo "[${cortex_host}] sync'd cortex.toml"
-else
-    echo "[${cortex_host}] failed to sync cortex.toml"
-fi
-
-ssh "${cortex_host}" sudo systemctl daemon-reload
-if ssh "${cortex_host}" systemctl is-active --quiet cortex.service; then
-    echo "[${cortex_host}] cortex service is active"
-elif ssh "${cortex_host}" sudo systemctl start cortex.service; then
-    echo "[${cortex_host}] started cortex service"
-else
-    echo "[${cortex_host}] failed to start cortex service"
-fi
-
-# ---------------------------------------------------------------------------
-# neuron (per-host, flavour from manifest)
-# ---------------------------------------------------------------------------
-
-for entry in "${neuron_entries[@]}"; do
-    IFS=$'\t' read -r neuron_host neuron_flavour <<< "${entry}"
-    package="helexa-neuron-${neuron_flavour}"
-
-    ensure_lair_repo "${neuron_host}"
-    ensure_cudnn_runtime "${neuron_host}"
-    neuron_nvr=$(installed_nvr "${neuron_host}" "${package}")
-    if needs_update "${neuron_host}" "${package}"; then
-        echo "[${neuron_host}] ${package} update available (current: ${neuron_nvr})"
-        if ssh "${neuron_host}" "[ ! -f /usr/lib/systemd/system/neuron.service ] || sudo systemctl stop neuron.service"; then
-            echo "[${neuron_host}] stopped neuron service"
-            # --allowerasing lets dnf swap out a previously-installed
-            # bare helexa-neuron or a different flavour without manual
-            # intervention. The Conflicts: clauses in the spec ensure
-            # only one flavour is ever resident.
-            if install_or_upgrade "${neuron_host}" "${package}"; then
-                neuron_nvr=$(installed_nvr "${neuron_host}" "${package}")
-                echo "[${neuron_host}] installed/upgraded ${package} to ${neuron_nvr}"
-                # Ensure firewalld allows neuron port
-                ssh "${neuron_host}" "sudo firewall-cmd --query-service=helexa-neuron --quiet 2>/dev/null || sudo firewall-cmd --add-service=helexa-neuron --permanent && sudo firewall-cmd --reload" 2>/dev/null || true
-                if ssh "${neuron_host}" "sudo systemctl daemon-reload && sudo systemctl start neuron.service"; then
-                    echo "[${neuron_host}] started neuron service"
-                else
-                    echo "[${neuron_host}] failed to start neuron service"
-                fi
-            else
-                echo "[${neuron_host}] failed to install ${package}:"
-                echo "${__DNF_OUTPUT__}" | sed "s/^/[${neuron_host}]   /"
-            fi
-        else
-            echo "[${neuron_host}] failed to stop neuron service"
-        fi
-    else
-        echo "[${neuron_host}] ${package} is up to date (${neuron_nvr})"
-        if ssh "${neuron_host}" systemctl is-active --quiet neuron.service; then
-            echo "[${neuron_host}] neuron service is active"
-        elif ssh "${neuron_host}" sudo systemctl start neuron.service; then
-            echo "[${neuron_host}] started neuron service"
-        else
-            echo "[${neuron_host}] failed to start neuron service"
-        fi
-    fi
-done

script/generate-packages-json.py

@@ -1,154 +0,0 @@
-#!/usr/bin/env python3
-"""Parse RPM repodata and emit a packages.json manifest for the UI."""
-
-import argparse
-import gzip
-import json
-import os
-import subprocess
-import sys
-import xml.etree.ElementTree as ET
-from datetime import datetime, timezone
-
-RPM_NS = "http://linux.duke.edu/metadata/common"
-OTHER_NS = "http://linux.duke.edu/metadata/other"
-REPO_NS = "http://linux.duke.edu/metadata/repo"
-
-
-def find_repodata_file(repodata_dir, data_type):
-    """Read repomd.xml and return the path to a specific data type's file."""
-    repomd_path = os.path.join(repodata_dir, "repomd.xml")
-    tree = ET.parse(repomd_path)
-    root = tree.getroot()
-
-    for data in root.findall(f"{{{REPO_NS}}}data"):
-        if data.get("type") == data_type:
-            location = data.find(f"{{{REPO_NS}}}location")
-            if location is not None:
-                href = location.get("href", "")
-                return os.path.join(os.path.dirname(repodata_dir), href)
-
-    return None
-
-
-def open_compressed(path):
-    """Open a gzip or zstd compressed file for reading."""
-    if path.endswith(".zst"):
-        result = subprocess.run(
-            ["zstdcat", path], capture_output=True, check=True
-        )
-        import io
-        return io.BytesIO(result.stdout)
-    else:
-        return gzip.open(path, "rb")
-
-
-def parse_primary(repodata_dir):
-    """Parse primary.xml.{gz,zst} and return package metadata."""
-    path = find_repodata_file(repodata_dir, "primary")
-    if not path:
-        print("error: primary metadata not found in repomd.xml", file=sys.stderr)
-        sys.exit(1)
-
-    packages = {}
-    with open_compressed(path) as f:
-        tree = ET.parse(f)
-
-    for pkg in tree.getroot().findall(f"{{{RPM_NS}}}package"):
-        if pkg.get("type") != "rpm":
-            continue
-
-        name = pkg.findtext(f"{{{RPM_NS}}}name", "")
-        version_el = pkg.find(f"{{{RPM_NS}}}version")
-        ver = version_el.get("ver", "") if version_el is not None else ""
-        rel = version_el.get("rel", "") if version_el is not None else ""
-        arch = pkg.findtext(f"{{{RPM_NS}}}arch", "")
-
-        size_el = pkg.find(f"{{{RPM_NS}}}size")
-        size = int(size_el.get("package", "0")) if size_el is not None else 0
-
-        time_el = pkg.find(f"{{{RPM_NS}}}time")
-        build_time = int(time_el.get("build", "0")) if time_el is not None else 0
-
-        location_el = pkg.find(f"{{{RPM_NS}}}location")
-        filename = os.path.basename(location_el.get("href", "")) if location_el is not None else ""
-
-        key = f"{name}-{ver}-{rel}"
-        packages[key] = {
-            "name": name,
-            "version": ver,
-            "release": rel,
-            "arch": arch,
-            "summary": pkg.findtext(f"{{{RPM_NS}}}summary", ""),
-            "size": size,
-            "buildTime": build_time,
-            "rpmFilename": filename,
-            "changelog": [],
-        }
-
-    return packages
-
-
-def parse_other(repodata_dir, packages):
-    """Parse other.xml.gz and attach changelog entries to packages."""
-    path = find_repodata_file(repodata_dir, "other")
-    if not path:
-        return
-
-    with open_compressed(path) as f:
-        tree = ET.parse(f)
-
-    for pkg in tree.getroot().findall(f"{{{OTHER_NS}}}package"):
-        name = pkg.get("name", "")
-        version_el = pkg.find(f"{{{OTHER_NS}}}version")
-        ver = version_el.get("ver", "") if version_el is not None else ""
-        rel = version_el.get("rel", "") if version_el is not None else ""
-        key = f"{name}-{ver}-{rel}"
-
-        if key not in packages:
-            continue
-
-        for entry in pkg.findall(f"{{{OTHER_NS}}}changelog"):
-            packages[key]["changelog"].append({
-                "author": entry.get("author", ""),
-                "date": int(entry.get("date", "0")),
-                "text": (entry.text or "").strip(),
-            })
-
-
-def main():
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument(
-        "--repodata-dir",
-        required=True,
-        help="path to the repodata/ directory",
-    )
-    parser.add_argument(
-        "--output",
-        required=True,
-        help="path to write packages.json",
-    )
-    parser.add_argument(
-        "--base-url",
-        required=True,
-        help="public base URL for the repo (e.g. https://rpm.lair.cafe/fedora/43/x86_64)",
-    )
-    args = parser.parse_args()
-
-    packages = parse_primary(args.repodata_dir)
-    parse_other(args.repodata_dir, packages)
-
-    manifest = {
-        "generated": datetime.now(timezone.utc).isoformat(),
-        "baseUrl": args.base_url,
-        "packages": list(packages.values()),
-    }
-
-    with open(args.output, "w") as f:
-        json.dump(manifest, f, indent=2)
-
-    print(f"wrote {len(packages)} packages to {args.output}")
-
-
-if __name__ == "__main__":
-    main()

script/validate-neuron.sh

@@ -1,162 +0,0 @@
-#!/bin/env bash
-#
-# End-to-end smoke test for a deployed neuron.
-#
-# Confirms the daemon is reachable, loads a small public Qwen3 GGUF,
-# fires a reasoning probe at /v1/chat/completions, and prints the
-# answer. Used to validate the candle harness on a real GPU host
-# before trusting it for production traffic, and as a regression test
-# after pushing new neuron builds.
-#
-# Usage:
-#   script/validate-neuron.sh [host] [model_id] [quant]
-#
-# Defaults:
-#   host     = beast.hanzalova.internal
-#   model_id = unsloth/Qwen3-0.6B-GGUF  (official Qwen3-*-GGUF repos
-#              ship Q8_0 only; unsloth's mirror ships the full Q-spectrum
-#              including Q4_K_M)
-#   quant    = Q4_K_M
-
-set -euo pipefail
-
-HOST="${1:-beast.hanzalova.internal}"
-MODEL_ID="${2:-unsloth/Qwen3-0.6B-GGUF}"
-# `${3-Q4_K_M}` (no colon) only uses the default when the arg is
-# UNSET — passing an explicit empty string drives the dense path.
-QUANT="${3-Q4_K_M}"
-PORT="${NEURON_PORT:-13131}"
-BASE="http://${HOST}:${PORT}"
-
-# Reasoning probe — concrete, low-temperature answer that small models
-# can still get right. "Paris" is a strong signal of basic competence
-# beyond gibberish.
-PROBE_PROMPT='What is the capital of Georgia (Caucasus)? Respond with the city name only, no punctuation.'
-EXPECT_SUBSTR='Tbilisi'
-# Qwen3 prepends <think>...</think> reasoning before the answer when the
-# chat template enables thinking mode, which eats most of a small token
-# budget. 256 leaves enough room for thinking + final answer.
-MAX_TOKENS=256
-
-# /models/load is synchronous — neuron blocks the response until the
-# hf-hub download + GGUF parse + tensor materialisation is done. A
-# fresh 0.6B-Q4_K_M is ~400 MB; on a slow link or cold cache that's
-# easily a minute. Pick a generous ceiling.
-LOAD_TIMEOUT=600
-INFER_TIMEOUT=120
-
-# Status messages go to stderr so command substitutions like
-# `raw=$(run_probe)` capture only the function's intended return value
-# (an HTTP body), not the progress chatter.
-say() { printf '[%s] %s\n' "${HOST}" "$*" >&2; }
-die() { say "FAIL: $*"; exit 1; }
-
-probe_health() {
-    curl --silent --fail --max-time 5 "${BASE}/health" >/dev/null \
-        || die "neuron not reachable at ${BASE}/health"
-}
-
-list_loaded_ids() {
-    # The manifest is YAML and uses yq; HTTP responses are JSON and use
-    # jq directly. pip-yq parses input as YAML by default, which trips
-    # on JSON content that happens to look like YAML aliases (chatcmpl
-    # ids, escaped quotes inside `<think>...</think>` blocks, etc.).
-    curl --silent --fail "${BASE}/models" | jq -r '.[].id'
-}
-
-is_loaded() {
-    list_loaded_ids 2>/dev/null | grep -Fxq "${MODEL_ID}"
-}
-
-trigger_load() {
-    say "POST /models/load ${MODEL_ID} (quant=${QUANT:-<dense>}, device=[0])"
-    say "  (synchronous; may take a minute on first run while HF downloads)"
-    # Build the payload via jq so the optional `quant` field is
-    # omitted entirely when empty — that's the signal to the harness
-    # to take the dense safetensors load path rather than GGUF.
-    local payload
-    if [[ -z "${QUANT}" ]]; then
-        payload=$(jq -n -c \
-            --arg id "${MODEL_ID}" \
-            '{model_id: $id, harness: "candle", devices: [0]}')
-    else
-        payload=$(jq -n -c \
-            --arg id "${MODEL_ID}" \
-            --arg q "${QUANT}" \
-            '{model_id: $id, harness: "candle", quant: $q, devices: [0]}')
-    fi
-    # --write-out captures the response code on a separate line so we
-    # can surface a real diagnostic instead of relying on --fail.
-    local resp http_code body
-    resp=$(curl --silent --show-error --max-time "${LOAD_TIMEOUT}" \
-        --write-out '\n__HTTP__%{http_code}' \
-        -X POST "${BASE}/models/load" \
-        -H 'content-type: application/json' \
-        --data "${payload}") || die "curl /models/load failed: $?"
-    http_code=$(echo "${resp}" | grep -oP '(?<=__HTTP__)\d+$' | tail -1)
-    body=$(echo "${resp}" | sed '$ s/__HTTP__.*$//')
-    if [[ "${http_code}" != "200" ]]; then
-        die "load returned HTTP ${http_code}: ${body}"
-    fi
-    say "load returned ${http_code}: ${body}"
-}
-
-run_probe() {
-    say "POST /v1/chat/completions (probe: ${PROBE_PROMPT})"
-    local payload
-    payload=$(jq -n -c \
-        --arg model "${MODEL_ID}" \
-        --arg content "${PROBE_PROMPT}" \
-        --argjson tokens "${MAX_TOKENS}" \
-        '{
-            model: $model,
-            messages: [{role: "user", content: $content}],
-            temperature: 0.1,
-            max_tokens: $tokens
-        }')
-    local resp http_code body
-    resp=$(curl --silent --show-error --max-time "${INFER_TIMEOUT}" \
-        --write-out '\n__HTTP__%{http_code}' \
-        -X POST "${BASE}/v1/chat/completions" \
-        -H 'content-type: application/json' \
-        --data "${payload}") || die "curl /v1/chat/completions failed: $?"
-    http_code=$(echo "${resp}" | grep -oP '(?<=__HTTP__)\d+$' | tail -1)
-    body=$(echo "${resp}" | sed '$ s/__HTTP__.*$//')
-    if [[ "${http_code}" != "200" ]]; then
-        die "inference returned HTTP ${http_code}: ${body}"
-    fi
-    echo "${body}"
-}
-
-say "validating neuron at ${BASE}"
-probe_health
-say "/health OK"
-
-if is_loaded; then
-    say "${MODEL_ID} already loaded"
-else
-    trigger_load
-fi
-
-raw=$(run_probe)
-echo "---"
-# Dump the raw JSON. Don't pipe through `yq -r '.'` — yq's default
-# YAML output mode chokes on JSON strings that contain `<` (and the
-# `<think>` markers Qwen3 emits during reasoning are a perfect
-# example). The targeted `yq -r '.path'` calls below work fine
-# because jq's path filter mode bypasses the YAML re-emit.
-echo "${raw}"
-echo "---"
-
-content=$(echo "${raw}" | jq -r '.choices[0].message.content // empty')
-if [[ -z "${content}" ]]; then
-    die "no content in chat completion response"
-fi
-say "assistant said: ${content}"
-
-if echo "${content}" | grep -qiF "${EXPECT_SUBSTR}"; then
-    say "PASS — response contains expected substring '${EXPECT_SUBSTR}'"
-    exit 0
-else
-    die "response did not contain '${EXPECT_SUBSTR}'"
-fi