fix(ci): exclude vllm-litellm deploy example from wheel-completeness check

The vllm-litellm/ deploy example (#686) lives under turnstone/ but ships in the repo, not the wheel — the package never references it; you clone the repo to run the compose stack. Add its files to the check's intentional-exclusion ALLOW list so the build job passes.
feat(deploy): add vLLM + LiteLLM unified-memory inference example (#686 )
2026-08-14 07:52:25 -06:00 · 2026-06-21 17:51:13 -07:00 · 2026-06-21 15:41:15 -07:00 · 2026-06-21 15:39:41 -07:00 · 2026-06-17 18:07:45 -07:00 · 2026-06-17 18:07:45 -07:00
10 changed files with 604 additions and 103 deletions
@@ -113,9 +113,16 @@ jobs:
            | grep -v '\.py$' | grep -v '\.dist-info' | grep -v '\.pyc' | grep -v '^File$' \
            | sort)

-          # Files intentionally excluded from the wheel (one per line)
+          # Files intentionally excluded from the wheel (one per line).
+          # The vllm-litellm/ deploy example ships in the repo, not the wheel
+          # (you clone the repo to run it; the package doesn't reference it).
          ALLOW="
          turnstone/core/storage/migrations/script.py.mako
+          turnstone/deploy/vllm-litellm/.env.example
+          turnstone/deploy/vllm-litellm/README.md
+          turnstone/deploy/vllm-litellm/docker-compose.yml
+          turnstone/deploy/vllm-litellm/gemma.Dockerfile
+          turnstone/deploy/vllm-litellm/litellm-config.yaml
          "

          MISSING=$(comm -23 <(echo "$SOURCE") <(echo "$WHEEL") \
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "turnstone"
-version = "1.6.9"
+version = "1.7.0a2"
 description = "Multi-node AI orchestration platform with tool use, agent routing, and cluster simulation."
 readme = "README.md"
 license = "Apache-2.0"
@@ -32,6 +32,8 @@ dependencies = [
    "sse-starlette>=2.0",
    "httpx-sse>=0.4",
    "pydantic>=2.0",
+    "pydantic-settings>=2.14.2",  # GHSA-4xgf-cpjx-pc3j: <2.14.2 advisory; pinned as a security floor for pip-audit
+
    "sqlalchemy>=2.0",
    "alembic>=1.14",
    "psycopg[binary]>=3.2",
@@ -44,6 +46,8 @@ dependencies = [
    "python-frontmatter>=1.0",
    "pypdfium2>=4",  # PDF text-extract + rasterize for models without native PDF input (core/pdf.py)
    "pillow>=10",  # PNG encoding for the PDF->images rasterize fallback (vision models, core/pdf.py)
+    "altair>=6.0",  # standard viz stack: Vega-Lite spec authoring; one spec renders to static SVG (vl-convert) AND interactive ui:// vega-embed panels. Light: pandas/numpy optional via narwhals.
+    "vl-convert-python>=1.6",  # Vega-Lite -> SVG/PNG, server-side (bundled Rust renderer; no browser/GDAL/chromium). BSD-3 + fully permissive dep closure (OFL font, BSD/MIT/ISC JS).
 ]

 [project.urls]
@@ -189,6 +193,10 @@ ignore_missing_imports = true
 module = ["pypdfium2", "pypdfium2.*"]
 ignore_missing_imports = true

+[[tool.mypy.overrides]]
+module = ["vl_convert", "vl_convert.*"]  # Rust wheel, ships no type stubs (altair is typed)
+ignore_missing_imports = true
+
 [[tool.mypy.overrides]]
 module = ["turnstone.channels.discord.*"]
 disallow_subclassing_any = false
@@ -55,14 +55,14 @@
      "license": "MIT"
    },
    "node_modules/@napi-rs/wasm-runtime": {
-      "version": "1.1.4",
-      "resolved": "https://registry.npmjs.org/@napi-rs/wasm-runtime/-/wasm-runtime-1.1.4.tgz",
-      "integrity": "sha512-3NQNNgA1YSlJb/kMH1ildASP9HW7/7kYnRI2szWJaofaS1hWmbGI4H+d3+22aGzXXN9IJ+n+GiFVcGipJP18ow==",
+      "version": "1.1.5",
+      "resolved": "https://registry.npmjs.org/@napi-rs/wasm-runtime/-/wasm-runtime-1.1.5.tgz",
+      "integrity": "sha512-AWPoBRJ9tsnVhor4sjO7rkni+7p+2IAEFj6cx06UgP10jkQHqay/36uRV/bFkgrh18D9vb4cr8Q0Pthskgzy+Q==",
      "dev": true,
      "license": "MIT",
      "optional": true,
      "dependencies": {
-        "@tybys/wasm-util": "^0.10.1"
+        "@tybys/wasm-util": "^0.10.2"
      },
      "funding": {
        "type": "github",
@@ -409,16 +409,16 @@
      "license": "MIT"
    },
    "node_modules/@vitest/expect": {
-      "version": "4.1.8",
-      "resolved": "https://registry.npmjs.org/@vitest/expect/-/expect-4.1.8.tgz",
-      "integrity": "sha512-h3nDO677RDLEGlBxyQ5CW8RlMThSKSRLUePLOx09gNIWRL40edgA1GCZSZgf1W55MFAG6/Sw14KeaAnqv0NKdQ==",
+      "version": "4.1.9",
+      "resolved": "https://registry.npmjs.org/@vitest/expect/-/expect-4.1.9.tgz",
+      "integrity": "sha512-vl/rYsUKcBr3SnQn166+XR5ZQcgMx3DQhFWdfli/cWpLnLUmbxZvyrJZotLFUryib+LtArYMSTJ5RbQ57ZqrlA==",
      "dev": true,
      "license": "MIT",
      "dependencies": {
        "@standard-schema/spec": "^1.1.0",
        "@types/chai": "^5.2.2",
-        "@vitest/spy": "4.1.8",
-        "@vitest/utils": "4.1.8",
+        "@vitest/spy": "4.1.9",
+        "@vitest/utils": "4.1.9",
        "chai": "^6.2.2",
        "tinyrainbow": "^3.1.0"
      },
@@ -427,13 +427,13 @@
      }
    },
    "node_modules/@vitest/mocker": {
-      "version": "4.1.8",
-      "resolved": "https://registry.npmjs.org/@vitest/mocker/-/mocker-4.1.8.tgz",
-      "integrity": "sha512-LEiN/xe4OSIbKe9HQIp5OC24agGD9J5CnmMgsLohVVoOPWL9a2sBoR6VBx43jQZb7Kr1l4RCuyCJzcAa0+dojw==",
+      "version": "4.1.9",
+      "resolved": "https://registry.npmjs.org/@vitest/mocker/-/mocker-4.1.9.tgz",
+      "integrity": "sha512-EVkXzBjrPGM+cK8/ANWgBrkUCfJfb38/EfTSO8h7pWvKkyPkpWxvR7BkD2MyItMF62C97zAEoqdpUixwR/e+Rw==",
      "dev": true,
      "license": "MIT",
      "dependencies": {
-        "@vitest/spy": "4.1.8",
+        "@vitest/spy": "4.1.9",
        "estree-walker": "^3.0.3",
        "magic-string": "^0.30.21"
      },
@@ -454,9 +454,9 @@
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    },
    "node_modules/@vitest/pretty-format": {
-      "version": "4.1.8",
-      "resolved": "https://registry.npmjs.org/@vitest/pretty-format/-/pretty-format-4.1.8.tgz",
-      "integrity": "sha512-9GasEBxpZ1VYIpqHf/0+YGg121uSNwCKOJqIrTwWP/TB7DmFCiaBpNl3aPZzoLWfWkuqhbH8vJIVobZkvdo2cA==",
+      "version": "4.1.9",
+      "resolved": "https://registry.npmjs.org/@vitest/pretty-format/-/pretty-format-4.1.9.tgz",
+      "integrity": "sha512-s0iufns3iIFitdgm+YR7g1whCAaGtXz459VS9/PqyKDEEFgYIhsHOQmXgIgDuYCt7DeQmiZT0Qe2OA2p4ZPu5A==",
      "dev": true,
      "license": "MIT",
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@@ -467,13 +467,13 @@
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    "node_modules/@vitest/runner": {
-      "version": "4.1.8",
-      "resolved": "https://registry.npmjs.org/@vitest/runner/-/runner-4.1.8.tgz",
-      "integrity": "sha512-EmVxeBAfMJvycdjd6Hm+RbFBbA9fKvo0Kx37hNpBYoYeavH3RNsBXWDooR1mgD52dCrxIIuP7UotpfiwOikvcg==",
+      "version": "4.1.9",
+      "resolved": "https://registry.npmjs.org/@vitest/runner/-/runner-4.1.9.tgz",
+      "integrity": "sha512-KXLMDtc7oe70+3mJfGrPUWPesswH+3sTxAMAMl8DG7I8IUQT4XW718dY5ID3vPUcmlu27CcKfY4P3h3I29SLJg==",
      "dev": true,
      "license": "MIT",
      "dependencies": {
-        "@vitest/utils": "4.1.8",
+        "@vitest/utils": "4.1.9",
        "pathe": "^2.0.3"
      },
      "funding": {
@@ -481,14 +481,14 @@
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    },
    "node_modules/@vitest/snapshot": {
-      "version": "4.1.8",
-      "resolved": "https://registry.npmjs.org/@vitest/snapshot/-/snapshot-4.1.8.tgz",
-      "integrity": "sha512-acfZboRmAIf05DEKcBQy33VXojFJjtUdLyo7oOmV9kebb2xdU01UknNiPuPZoJZQyO7DF0gZdTGTpeAzET9QPQ==",
+      "version": "4.1.9",
+      "resolved": "https://registry.npmjs.org/@vitest/snapshot/-/snapshot-4.1.9.tgz",
+      "integrity": "sha512-Jc7RKGNBo8Z28WYIm0Niej4xdSPByRf6mU58VpHQkd6Zh05rlnA+twjbK5HyeIGHxrzsc3mJgS43uM0CZKzaIA==",
      "dev": true,
      "license": "MIT",
      "dependencies": {
-        "@vitest/pretty-format": "4.1.8",
-        "@vitest/utils": "4.1.8",
+        "@vitest/pretty-format": "4.1.9",
+        "@vitest/utils": "4.1.9",
        "magic-string": "^0.30.21",
        "pathe": "^2.0.3"
      },
@@ -497,9 +497,9 @@
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    "node_modules/@vitest/spy": {
-      "version": "4.1.8",
-      "resolved": "https://registry.npmjs.org/@vitest/spy/-/spy-4.1.8.tgz",
-      "integrity": "sha512-6EevtBp6OZOPF7bmz36HrGMeP3txgVSrgebWxHOafDXGkhIzfXK14f8KF6MuFfgXXUeHxmpD3BQxkV00/3s5mA==",
+      "version": "4.1.9",
+      "resolved": "https://registry.npmjs.org/@vitest/spy/-/spy-4.1.9.tgz",
+      "integrity": "sha512-fHpsS6mIi+PiEW+vcRVOMkX1oSaPKne3VOclSFICPcGOmfKgXPU5iAah+wcNcj2xPrCCmfq99IDGf+EojhhvhA==",
      "dev": true,
      "license": "MIT",
      "funding": {
@@ -507,13 +507,13 @@
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    "node_modules/@vitest/utils": {
-      "version": "4.1.8",
-      "resolved": "https://registry.npmjs.org/@vitest/utils/-/utils-4.1.8.tgz",
-      "integrity": "sha512-uOJamYALNhfJ6iolExyQM40yIQwDqYnkKtQ5VCiSe17E33H0aQ/u+1GlRuz4LZBk6Mm3sg90G9hEbmEt37C1Zg==",
+      "version": "4.1.9",
+      "resolved": "https://registry.npmjs.org/@vitest/utils/-/utils-4.1.9.tgz",
+      "integrity": "sha512-A51o8ymO5PpqlWNnBP9ZHPXDIpuMtTLlGSjN7la4US+LJzoUMyhwjA5QXlm39JexgwHKW4Xjs8Z2d3dLCXOeuA==",
      "dev": true,
      "license": "MIT",
      "dependencies": {
-        "@vitest/pretty-format": "4.1.8",
+        "@vitest/pretty-format": "4.1.9",
        "convert-source-map": "^2.0.0",
        "tinyrainbow": "^3.1.0"
      },
@@ -921,9 +921,9 @@
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    },
    "node_modules/obug": {
-      "version": "2.1.2",
-      "resolved": "https://registry.npmjs.org/obug/-/obug-2.1.2.tgz",
-      "integrity": "sha512-AWGB9WFcRXOQs48Z/udjI5ZcZMHXwX8XPByNpOydgcGsDLIzjGizhoMWJyKAWze7AVW/2W1i+/gPX4YtKe5cyg==",
+      "version": "2.1.3",
+      "resolved": "https://registry.npmjs.org/obug/-/obug-2.1.3.tgz",
+      "integrity": "sha512-9miFgM2OFba7hB+pRgvtV84pYTBaoTHohvmIgiRt6dRIzbwEOIaNaP+dIlGs2fNFoB0SeISs0Jz5WFVRid6Xyg==",
      "dev": true,
      "funding": [
        "https://github.com/sponsors/sxzz",
@@ -1200,19 +1200,19 @@
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    },
    "node_modules/vitest": {
-      "version": "4.1.8",
-      "resolved": "https://registry.npmjs.org/vitest/-/vitest-4.1.8.tgz",
-      "integrity": "sha512-flY6ScbCIt9HThs+C5HS7jvGOB560DJtk/Z15IQROTA6zEy49Nh8T/dofWTQL+n3vswqn87sbJNiuqw1SDp5Ig==",
+      "version": "4.1.9",
+      "resolved": "https://registry.npmjs.org/vitest/-/vitest-4.1.9.tgz",
+      "integrity": "sha512-nE3/LEyc0z87uHYLZebqCUOaJr2hdtuPp7BQ4BosVFnfltxgAvMG08NyrSGlPpOUWvR27c5flSmYFTNr78L9GQ==",
      "dev": true,
      "license": "MIT",
      "dependencies": {
-        "@vitest/expect": "4.1.8",
-        "@vitest/mocker": "4.1.8",
-        "@vitest/pretty-format": "4.1.8",
-        "@vitest/runner": "4.1.8",
-        "@vitest/snapshot": "4.1.8",
-        "@vitest/spy": "4.1.8",
-        "@vitest/utils": "4.1.8",
+        "@vitest/expect": "4.1.9",
+        "@vitest/mocker": "4.1.9",
+        "@vitest/pretty-format": "4.1.9",
+        "@vitest/runner": "4.1.9",
+        "@vitest/snapshot": "4.1.9",
+        "@vitest/spy": "4.1.9",
+        "@vitest/utils": "4.1.9",
        "es-module-lexer": "^2.0.0",
        "expect-type": "^1.3.0",
        "magic-string": "^0.30.21",
@@ -1240,12 +1240,12 @@
        "@edge-runtime/vm": "*",
        "@opentelemetry/api": "^1.9.0",
        "@types/node": "^20.0.0 || ^22.0.0 || >=24.0.0",
-        "@vitest/browser-playwright": "4.1.8",
-        "@vitest/browser-preview": "4.1.8",
-        "@vitest/browser-webdriverio": "4.1.8",
-        "@vitest/coverage-istanbul": "4.1.8",
-        "@vitest/coverage-v8": "4.1.8",
-        "@vitest/ui": "4.1.8",
+        "@vitest/browser-playwright": "4.1.9",
+        "@vitest/browser-preview": "4.1.9",
+        "@vitest/browser-webdriverio": "4.1.9",
+        "@vitest/coverage-istanbul": "4.1.9",
+        "@vitest/coverage-v8": "4.1.9",
+        "@vitest/ui": "4.1.9",
        "happy-dom": "*",
        "jsdom": "*",
        "vite": "^6.0.0 || ^7.0.0 || ^8.0.0"
@@ -1,3 +1,3 @@
 """turnstone - Multi-node AI orchestration platform with tool use, agent routing, and cluster simulation."""

-__version__ = "1.6.9"
+__version__ = "1.7.0a2"
@@ -0,0 +1,33 @@
+# Copy to .env and edit. Defaults target an NVIDIA DGX Spark (GB10, ~128 GiB).
+# For AMD Strix Halo, see README.md → "Setup — AMD Strix Halo".
+
+# === model weights (mounted read-only at /models) ============================
+# Directory holding the model subdirs, e.g. qwen3.6-27B-FP8/ and gemma-4-12B-it/
+MODELS_DIR=./models
+# HF_TOKEN=hf_...              # only needed for gated weights pulled at runtime
+
+# === image ===================================================================
+# NVIDIA/CUDA default. AMD: set to a ROCm vLLM build that targets gfx1151.
+VLLM_IMAGE=vllm/vllm-openai:latest
+LITELLM_TAG=main-stable
+
+# === models ==================================================================
+# NVIDIA (Blackwell) runs FP8 natively. AMD RDNA3.5 has no FP8 matmul — recent
+# ROCm/vLLM still load FP8 (saves ~half the weight memory) but at bf16 speed and
+# immature; bf16 is the polished path there: QWEN_MODEL=/models/qwen3.6-27B.
+QWEN_MODEL=/models/qwen3.6-27B-FP8
+GEMMA_MODEL=/models/gemma-4-12B-it
+
+# === memory tuning ===========================================================
+# gpu-memory-utilization is a fraction of TOTAL unified memory (NOT free). The
+# two fractions must sum below ~0.9. Qwen gets the larger share; if you run out
+# of memory, shrink GEMMA_UTIL first, then lower QWEN_MAXLEN.
+QWEN_UTIL=0.64
+QWEN_MAXLEN=262144            # full Qwen context; ~2.5x concurrency on a Spark
+GEMMA_UTIL=0.22
+GEMMA_MAXLEN=131072           # full Gemma context; ~1x concurrency
+
+# === published ports =========================================================
+LITELLM_PORT=4000             # Turnstone points here (both API routes)
+QWEN_PORT=8000
+GEMMA_PORT=8001
@@ -0,0 +1,231 @@
+# Turnstone local inference — vLLM + LiteLLM on one unified-memory box
+
+Run Turnstone's reasoning **and** perception models on a single unified-memory
+accelerator — an **NVIDIA DGX Spark (GB10)** or an **AMD Ryzen AI MAX "Strix
+Halo"** — behind one LiteLLM gateway.
+
+> Validated on a DGX Spark (GB10, 128 GiB). The AMD Strix Halo path is
+> **guidance, not yet hardware-tested** — the deltas are called out explicitly.
+
+## What this is
+
+Two vLLM servers co-resident on one GPU, fronted by LiteLLM, which exposes
+**both** an OpenAI route and an Anthropic route on the same port:
+
+| Service | Model | Role | Turnstone reaches it via |
+|---|---|---|---|
+| `vllm-qwen` | Qwen 3.6 27B | reasoning + tools | LiteLLM `/v1/messages` (Anthropic) |
+| `vllm-gemma` | Gemma 4 12B | vision + audio (omni) | LiteLLM `/v1/chat/completions` (OpenAI) |
+| `litellm` | — | gateway, both routes | `:4000` |
+
+The **reranker runs on its own host** — it speaks the Cohere/Jina `/rerank` wire
+format, not chat, so Turnstone calls it directly (see *Reranker*, below).
+
+```
+                ┌─ /v1/messages         → vllm-qwen   (reasoning / tools)
+turnstone ─┬─ litellm :4000 ─┤
+           │   └─ /v1/chat/completions  → vllm-gemma  (vision + audio)
+           └──────────────── reranker host :8002/rerank  (direct, separate box)
+```
+
+**Why two routes.** vLLM serves both `/v1/messages` and `/v1/chat/completions`.
+- Qwen uses the **Anthropic** lane — vLLM's native `/v1/messages` is the stronger
+  path for reasoning/tool models.
+- Gemma uses the **OpenAI** lane — perception needs it: audio (`input_audio`) has
+  no equivalent in the Anthropic Messages API, so Turnstone gates audio roles to
+  OpenAI-SDK providers. Vision works on either lane; audio works only here.
+
+**Two rules for a single unified-memory card** (both enforced/automated below;
+rationale in *Troubleshooting*):
+1. **Drop the page cache before `up`** — vLLM profiles against free memory; a
+   warm cache makes it under-provision KV.
+2. **Sequential startup** — `vllm-gemma` waits for `vllm-qwen` to be healthy
+   (`depends_on`) so Qwen claims its KV against clean memory first.
+
+## Requirements
+
+- One unified-memory accelerator, ≈128 GiB (Spark GB10, or Strix Halo with the
+  iGPU given most of system RAM via UMA/GTT).
+- Docker + Compose v2, and either the **NVIDIA Container Toolkit** (Spark) or
+  **ROCm** with `/dev/kfd` + `/dev/dri` access (Strix Halo).
+- Weights on disk under `$MODELS_DIR`:
+  - `qwen3.6-27B-FP8/` (NVIDIA) or `qwen3.6-27B/` bf16 (AMD)
+  - `gemma-4-12B-it/`
+- `sudo` for `drop_caches`.
+- These examples pass `--trust-remote-code` to vLLM; only use this with trusted model repos (remove the flag if your checkpoint doesn’t require it).
+
+## Setup — NVIDIA DGX Spark (GB10)   ✅ validated
+
+```sh
+cp .env.example .env
+# edit .env: set MODELS_DIR to your weights dir (defaults otherwise suit a Spark)
+
+# 1. drop the page cache (REQUIRED on unified memory)
+sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches'
+
+# 2. bring it up — qwen loads first, then gemma, then litellm (auto-sequenced)
+docker compose up -d
+
+# 3. watch until both are healthy (qwen ~8 min: FP8 load + graph capture)
+watch -n5 'docker compose ps'
+```
+
+Verify the KV pools and a round-trip on each route:
+
+```sh
+# qwen should report "Maximum concurrency for 262144 ...: ~2.5x"
+docker compose logs vllm-qwen | grep "Maximum concurrency"
+docker compose logs vllm-gemma | grep "Maximum concurrency"   # ~1.0x at 131072
+
+# Anthropic route -> qwen
+curl -s localhost:4000/v1/messages -H 'content-type: application/json' \
+  -H 'anthropic-version: 2023-06-01' -H 'x-api-key: dummy' \
+  -d '{"model":"qwen3.6-27b","max_tokens":32,"messages":[{"role":"user","content":"hi"}]}'
+
+# OpenAI route -> gemma
+curl -s localhost:4000/v1/chat/completions -H 'content-type: application/json' \
+  -H 'authorization: Bearer dummy' \
+  -d '{"model":"gemma-4-12b","max_tokens":16,"messages":[{"role":"user","content":"hi"}]}'
+```
+
+Reference point (Spark, defaults): Qwen full 256K @ ~2.5× concurrency on default
+KV; Gemma full 131072 @ ~1.0×; ~108/121 GiB used.
+
+## Setup — AMD Strix Halo (Ryzen AI MAX, ROCm)   ⚠️ guidance, untested here
+
+Same compose, three deltas in `.env` + the GPU device block:
+
+1. **Image** — the stock `vllm/vllm-openai` is CUDA-only; you need a ROCm build for
+   `gfx1151`. Easiest is a community Strix Halo setup —
+   [kyuz0/amd-strix-halo-vllm-toolboxes](https://github.com/kyuz0/amd-strix-halo-vllm-toolboxes)
+   or the TheRock-ROCm build in [hec-ovi/vllm-qwen](https://github.com/hec-ovi/vllm-qwen)
+   — then point `VLLM_IMAGE` at it.
+2. **Weights — BF16 is the polished path.** gfx1151 has no FP8 matmul, so even though
+   recent ROCm/vLLM *can* load an FP8 checkpoint, the compute falls back to BF16 speed —
+   FP8's only win here is ~half the weight memory, and it's still rough (very slow first
+   load). Prefer BF16; qwen3.6-27B BF16 at long context is reported working on Strix Halo
+   (hec-ovi, above). BF16 27B weights ≈ 54 GiB, so leave less KV room:
+   ```sh
+   QWEN_MODEL=/models/qwen3.6-27B   # BF16. The -FP8 dir loads too (memory saving, slow/immature)
+   QWEN_MAXLEN=131072               # start here co-resident; raise toward 262144 and watch the KV log
+   QWEN_UTIL=0.62
+   ```
+3. **GPU access** — ROCm doesn't use the `deploy:` nvidia reservation. In
+   `docker-compose.yml`, **delete the `deploy:` block** on *both* vLLM services
+   and replace it with:
+   ```yaml
+       devices:
+         - /dev/kfd
+         - /dev/dri
+       group_add:
+         - video
+         - render
+       ipc: host
+       security_opt:
+         - seccomp:unconfined
+   ```
+   If the runtime rejects the arch, add `HSA_OVERRIDE_GFX_VERSION=11.5.1` to each
+   service's `environment:` and retry (value depends on your ROCm build).
+
+Also ensure the iGPU can actually reach ~128 GiB: set the BIOS UMA/"VRAM" split
+high, or rely on Linux GTT (`amdgpu.gttsize=...` kernel param) — otherwise vLLM
+sees only the small carved-out VRAM. Then follow the Spark steps (drop caches →
+`up` → verify).
+
+## Point Turnstone at it
+
+Run Turnstone separately and add the models (e.g. in
+`~/.config/turnstone/config.toml`). Note the **two different lanes**: Qwen omits
+`/v1` (the SDK appends `/v1/messages`); Gemma includes `/v1`:
+
+```toml
+[models.qwen]
+model = "qwen3.6-27b"
+provider = "anthropic-compatible"
+base_url = "http://INFERENCE_HOST:4000"           # /v1/messages (no /v1 suffix)
+api_key = "dummy"
+context_window = 262144
+
+[models.gemma]
+model = "gemma-4-12b"
+provider = "openai-compatible"
+base_url = "http://INFERENCE_HOST:4000/v1"         # /v1/chat/completions (perception)
+api_key = "dummy"
+context_window = 131072
+[models.gemma.capabilities]
+supports_vision = true
+supports_audio_input = true
+
+[models.reranker]
+model = "qwen3-reranker-8b"
+provider = "openai-compatible"                      # moot — reranker uses the /rerank client
+base_url = "http://RERANKER_HOST:8002/rerank"       # direct, not via LiteLLM
+[models.reranker.capabilities]
+supports_rerank = true
+
+[model]
+default = "qwen"
+```
+
+Set `default = "qwen"` for reasoning; pick Gemma as the workstream model (or the
+**STT** role under Models → Roles) for vision/voice; select the reranker under
+**Models → Roles → Reranker**.
+
+## Reranker (separate host)
+
+The reranker is light but doesn't co-reside well with Qwen at full context on one
+128 GiB card, and it speaks a different wire format — so run it on its own box:
+
+```sh
+vllm serve /models/qwen3-reranker-8b \
+  --served-model-name qwen3-reranker-8b --runner pooling \
+  --hf-overrides '{"architectures":["Qwen3ForSequenceClassification"],"classifier_from_token":["no","yes"],"is_original_qwen3_reranker":true}' \
+  --chat-template /models/qwen3-reranker-8b/chat_template.jinja \
+  --gpu-memory-utilization 0.5 --port 8002
+```
+
+Point Turnstone's reranker `base_url` at `http://RERANKER_HOST:8002/rerank`.
+
+## Troubleshooting
+
+**`max seq len (X) larger than available KV cache (Y)` — Qwen won't start.**
+Not enough KV for the context. In order: (1) you forgot to drop the page cache —
+`docker compose down`, drop caches, `up`; (2) raise `QWEN_UTIL`; (3) lower
+`GEMMA_UTIL` to hand memory over; (4) lower `QWEN_MAXLEN`.
+
+**`available KV cache memory (0.45 GiB)` despite plenty of free RAM.** The two
+vLLMs profiled memory *simultaneously* and raced — one grabbed almost everything.
+Keep the `depends_on: vllm-qwen: condition: service_healthy` on gemma (sequential
+startup). Never start them together on one card.
+
+**`dependency failed to start: container is unhealthy` during `up`.** The cold
+load (weights + graph capture) exceeded the healthcheck `start_period`. We set
+900 s; raise it if your disk is slow. The container keeps loading and recovers
+(`restart: unless-stopped`) — it's slow, not broken.
+
+**LiteLLM returns connection-refused right after `up`.** It starts in seconds but
+the vLLMs take minutes; `depends_on` gates it. If you restart *only* LiteLLM,
+give it ~10 s before the first request.
+
+**Gemma audio: "invalid audio file", or audio silently ignored.** Two causes:
+(1) the image lacks `vllm[audio]` — use the bundled `gemma.Dockerfile` (default);
+(2) Gemma is being reached on the Anthropic lane — audio only rides
+`/v1/chat/completions`, so Gemma must be `provider=openai-compatible` in Turnstone
+and `openai/...` in `litellm-config.yaml`.
+
+**Qwen replies are only a `thinking` block, no text.** `preserve_thinking` is on;
+give it a larger `max_tokens`, or render the thinking separately. Not an error.
+
+**Slow generation (single-digit tokens/s).** Expected for a 27B co-resident with
+another model on one Spark/Strix Halo APU — it's memory-bandwidth bound. For more
+speed, drop co-residence or context.
+
+**Want more headroom / fitting tighter.** `--kv-cache-dtype fp8` (add to the qwen
+command) ~halves KV memory if you're squeezed; default (fp16/bf16) KV is higher
+fidelity and usually fits once the cache is dropped. Gemma over-provisioned (high
+"concurrency" multiple)? Raise `GEMMA_MAXLEN` to spend the headroom on context,
+or lower `GEMMA_UTIL` to free memory.
+
+**AMD: `HIP error` / "gfx not supported".** Use a ROCm vLLM build for `gfx1151`,
+set `HSA_OVERRIDE_GFX_VERSION`, confirm `/dev/kfd` + `/dev/dri` are passed and the
+user is in the `video`/`render` groups.
@@ -0,0 +1,143 @@
+# Turnstone local inference — two models co-resident on ONE unified-memory
+# accelerator (NVIDIA DGX Spark / GB10, or AMD Ryzen AI MAX "Strix Halo"),
+# behind a LiteLLM gateway that serves BOTH API routes at once:
+#
+#   /v1/messages          (Anthropic)  -> qwen   reasoning + tools
+#   /v1/chat/completions  (OpenAI)     -> gemma  perception (vision + audio)
+#
+# This file targets NVIDIA/CUDA (the default image + GPU reservation). For AMD
+# ROCm, see README.md → "Setup — AMD Strix Halo" (swap VLLM_IMAGE and the GPU
+# device-access block). The reranker runs on its own host (README) — it is not
+# co-resident here.
+#
+# TWO HARD REQUIREMENTS on a single unified-memory card (see README →
+# Troubleshooting for the why):
+#   1. Drop the page cache before `up`:  sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches'
+#   2. Sequential startup — enforced below: gemma waits for qwen to be healthy,
+#      so qwen claims its KV against clean memory first (simultaneous starts
+#      race and starve whichever profiles second).
+#
+# All tuning knobs are in .env.
+name: turnstone-inference
+
+services:
+  # --- reasoning + tools : Qwen 3.6 27B, full context via the Anthropic lane --
+  vllm-qwen:
+    image: ${VLLM_IMAGE:-vllm/vllm-openai:latest}
+    command:
+      - --model
+      - ${QWEN_MODEL:-/models/qwen3.6-27B-FP8}
+      - --served-model-name
+      - qwen3.6-27b
+      - --host
+      - "0.0.0.0"
+      - --port
+      - "8000"
+      - --trust-remote-code
+      - --gpu-memory-utilization
+      - "${QWEN_UTIL:-0.64}"
+      - --max-model-len
+      - "${QWEN_MAXLEN:-262144}"
+      - --max-num-batched-tokens
+      - "4096"
+      - --max-num-seqs
+      - "2"
+      - --enable-prefix-caching
+      - --reasoning-parser
+      - qwen3
+      - --enable-auto-tool-choice
+      - --tool-call-parser
+      - qwen3_coder
+      - --default-chat-template-kwargs
+      - '{"preserve_thinking": true}'
+    environment:
+      HUGGING_FACE_HUB_TOKEN: ${HF_TOKEN:-}
+    volumes:
+      - ${MODELS_DIR:-./models}:/models:ro
+    ports:
+      - "${QWEN_PORT:-8000}:8000"
+    healthcheck:
+      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"]
+      interval: 15s
+      timeout: 5s
+      retries: 80
+      start_period: 900s          # cold load + graph capture at full ctx is slow
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia       # AMD: delete this `deploy:` block, see README
+              device_ids: ["0"]
+              capabilities: [gpu]
+    restart: unless-stopped
+
+  # --- perception : Gemma 4 12B (vision + audio) via the OpenAI lane ----------
+  # Built from gemma.Dockerfile to add vllm[audio]; audio rides /v1/chat/completions.
+  vllm-gemma:
+    build:
+      context: .
+      dockerfile: gemma.Dockerfile
+      args:
+        VLLM_IMAGE: ${VLLM_IMAGE:-vllm/vllm-openai:latest}
+    image: turnstone-inference/vllm-gemma-audio
+    command:
+      - --model
+      - ${GEMMA_MODEL:-/models/gemma-4-12B-it}
+      - --served-model-name
+      - gemma-4-12b
+      - --host
+      - "0.0.0.0"
+      - --port
+      - "8000"
+      - --trust-remote-code
+      - --gpu-memory-utilization
+      - "${GEMMA_UTIL:-0.22}"
+      - --max-model-len
+      - "${GEMMA_MAXLEN:-131072}"
+      - --max-num-batched-tokens
+      - "4096"
+      - --enable-auto-tool-choice
+      - --reasoning-parser
+      - gemma4
+      - --tool-call-parser
+      - gemma4
+      - --default-chat-template-kwargs
+      - '{"enable_thinking": false}'
+    environment:
+      HUGGING_FACE_HUB_TOKEN: ${HF_TOKEN:-}
+    volumes:
+      - ${MODELS_DIR:-./models}:/models:ro
+    ports:
+      - "${GEMMA_PORT:-8001}:8000"
+    healthcheck:
+      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"]
+      interval: 15s
+      timeout: 5s
+      retries: 60
+      start_period: 900s
+    depends_on:
+      vllm-qwen:
+        condition: service_healthy   # SEQUENTIAL STARTUP — do not remove
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia          # AMD: delete this `deploy:` block, see README
+              device_ids: ["0"]
+              capabilities: [gpu]
+    restart: unless-stopped
+
+  # --- gateway : both API routes on :4000 ------------------------------------
+  litellm:
+    image: ghcr.io/berriai/litellm:${LITELLM_TAG:-main-stable}
+    command: ["--config", "/etc/litellm/config.yaml", "--port", "4000"]
+    volumes:
+      - ./litellm-config.yaml:/etc/litellm/config.yaml:ro
+    ports:
+      - "${LITELLM_PORT:-4000}:4000"
+    depends_on:
+      vllm-qwen:
+        condition: service_healthy
+      vllm-gemma:
+        condition: service_healthy
+    restart: unless-stopped
@@ -0,0 +1,10 @@
+# Gemma 4 is an omni model (vision + audio). The stock vLLM image ships without
+# the audio extra, so audio uploads fail with a misleading "invalid audio file".
+# This layer adds vllm[audio]. Builds on top of whatever VLLM_IMAGE you use, so
+# it works for both the NVIDIA and ROCm base images.
+#
+# Vision works WITHOUT this — if you only need vision, point vllm-gemma at
+# ${VLLM_IMAGE} directly in docker-compose.yml and drop the build: block.
+ARG VLLM_IMAGE=vllm/vllm-openai:latest
+FROM ${VLLM_IMAGE}
+RUN python3 -m pip install --no-cache-dir "vllm[audio]"
@@ -0,0 +1,27 @@
+# LiteLLM gateway config — serves BOTH API routes on :4000 at once, each model
+# on the backend protocol its role needs:
+#
+#   /v1/messages          (Anthropic)  -> qwen   (reasoning; vLLM's native
+#                                          /v1/messages beats its OpenAI lane)
+#   /v1/chat/completions  (OpenAI)     -> gemma  (perception: vision + audio;
+#                                          audio `input_audio` has no Anthropic
+#                                          Messages equivalent, so it MUST use
+#                                          the OpenAI chat lane)
+#
+# Turnstone reaches qwen as provider=anthropic-compatible (base_url :4000, no /v1)
+# and gemma as provider=openai-compatible (base_url :4000/v1). The reranker is
+# NOT here — Cohere/Jina /rerank wire format; Turnstone calls it directly.
+model_list:
+  - model_name: qwen3.6-27b
+    litellm_params:
+      model: anthropic/qwen3.6-27b
+      api_base: http://vllm-qwen:8000
+      api_key: dummy
+  - model_name: gemma-4-12b
+    litellm_params:
+      model: openai/gemma-4-12b
+      api_base: http://vllm-gemma:8000/v1
+      api_key: dummy
+
+litellm_settings:
+  drop_params: true        # tolerate params a backend doesn't accept
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+version = "6.2.1"
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