chore: bump version to 1.6.9

test: address Copilot review on the leaked-thread guard
- The guard snapshotted live threads by `Thread.ident`, but idents are recycled after a thread exits — a new leaked thread reusing an exited thread's ident would be mistaken for pre-existing and missed (false negative). Snapshot the Thread OBJECTS and compare by identity instead. - Fix the `serve` fixture docstring: the factory returns the ephemeral port, not the server.
2026-08-14 07:52:25 -06:00 · 2026-06-17 18:21:01 -07:00 · 2026-06-17 18:16:34 -07:00 · 2026-06-17 18:16:34 -07:00 · 2026-06-17 18:16:34 -07:00 · 2026-06-17 18:16:34 -07:00
10 changed files with 103 additions and 604 deletions
@@ -113,16 +113,9 @@ jobs:
            | grep -v '\.py$' | grep -v '\.dist-info' | grep -v '\.pyc' | grep -v '^File$' \
            | sort)

-          # 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).
+          # Files intentionally excluded from the wheel (one per line)
          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 = "1.7.0a2"
+version = "1.6.9"
 description = "Multi-node AI orchestration platform with tool use, agent routing, and cluster simulation."
 readme = "README.md"
 license = "Apache-2.0"
@@ -32,8 +32,6 @@ 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",
@@ -46,8 +44,6 @@ 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]
@@ -193,10 +189,6 @@ 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": "1.1.5",
-      "resolved": "https://registry.npmjs.org/@napi-rs/wasm-runtime/-/wasm-runtime-1.1.5.tgz",
-      "integrity": "sha512-AWPoBRJ9tsnVhor4sjO7rkni+7p+2IAEFj6cx06UgP10jkQHqay/36uRV/bFkgrh18D9vb4cr8Q0Pthskgzy+Q==",
+      "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==",
      "dev": true,
      "license": "MIT",
      "optional": true,
      "dependencies": {
-        "@tybys/wasm-util": "^0.10.2"
+        "@tybys/wasm-util": "^0.10.1"
      },
      "funding": {
        "type": "github",
@@ -409,16 +409,16 @@
      "license": "MIT"
    },
    "node_modules/@vitest/expect": {
-      "version": "4.1.9",
-      "resolved": "https://registry.npmjs.org/@vitest/expect/-/expect-4.1.9.tgz",
-      "integrity": "sha512-vl/rYsUKcBr3SnQn166+XR5ZQcgMx3DQhFWdfli/cWpLnLUmbxZvyrJZotLFUryib+LtArYMSTJ5RbQ57ZqrlA==",
+      "version": "4.1.8",
+      "resolved": "https://registry.npmjs.org/@vitest/expect/-/expect-4.1.8.tgz",
+      "integrity": "sha512-h3nDO677RDLEGlBxyQ5CW8RlMThSKSRLUePLOx09gNIWRL40edgA1GCZSZgf1W55MFAG6/Sw14KeaAnqv0NKdQ==",
      "dev": true,
      "license": "MIT",
      "dependencies": {
        "@standard-schema/spec": "^1.1.0",
        "@types/chai": "^5.2.2",
-        "@vitest/spy": "4.1.9",
-        "@vitest/utils": "4.1.9",
+        "@vitest/spy": "4.1.8",
+        "@vitest/utils": "4.1.8",
        "chai": "^6.2.2",
        "tinyrainbow": "^3.1.0"
      },
@@ -427,13 +427,13 @@
      }
    },
    "node_modules/@vitest/mocker": {
-      "version": "4.1.9",
-      "resolved": "https://registry.npmjs.org/@vitest/mocker/-/mocker-4.1.9.tgz",
-      "integrity": "sha512-EVkXzBjrPGM+cK8/ANWgBrkUCfJfb38/EfTSO8h7pWvKkyPkpWxvR7BkD2MyItMF62C97zAEoqdpUixwR/e+Rw==",
+      "version": "4.1.8",
+      "resolved": "https://registry.npmjs.org/@vitest/mocker/-/mocker-4.1.8.tgz",
+      "integrity": "sha512-LEiN/xe4OSIbKe9HQIp5OC24agGD9J5CnmMgsLohVVoOPWL9a2sBoR6VBx43jQZb7Kr1l4RCuyCJzcAa0+dojw==",
      "dev": true,
      "license": "MIT",
      "dependencies": {
-        "@vitest/spy": "4.1.9",
+        "@vitest/spy": "4.1.8",
        "estree-walker": "^3.0.3",
        "magic-string": "^0.30.21"
      },
@@ -454,9 +454,9 @@
      }
    },
    "node_modules/@vitest/pretty-format": {
-      "version": "4.1.9",
-      "resolved": "https://registry.npmjs.org/@vitest/pretty-format/-/pretty-format-4.1.9.tgz",
-      "integrity": "sha512-s0iufns3iIFitdgm+YR7g1whCAaGtXz459VS9/PqyKDEEFgYIhsHOQmXgIgDuYCt7DeQmiZT0Qe2OA2p4ZPu5A==",
+      "version": "4.1.8",
+      "resolved": "https://registry.npmjs.org/@vitest/pretty-format/-/pretty-format-4.1.8.tgz",
+      "integrity": "sha512-9GasEBxpZ1VYIpqHf/0+YGg121uSNwCKOJqIrTwWP/TB7DmFCiaBpNl3aPZzoLWfWkuqhbH8vJIVobZkvdo2cA==",
      "dev": true,
      "license": "MIT",
      "dependencies": {
@@ -467,13 +467,13 @@
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    },
    "node_modules/@vitest/runner": {
-      "version": "4.1.9",
-      "resolved": "https://registry.npmjs.org/@vitest/runner/-/runner-4.1.9.tgz",
-      "integrity": "sha512-KXLMDtc7oe70+3mJfGrPUWPesswH+3sTxAMAMl8DG7I8IUQT4XW718dY5ID3vPUcmlu27CcKfY4P3h3I29SLJg==",
+      "version": "4.1.8",
+      "resolved": "https://registry.npmjs.org/@vitest/runner/-/runner-4.1.8.tgz",
+      "integrity": "sha512-EmVxeBAfMJvycdjd6Hm+RbFBbA9fKvo0Kx37hNpBYoYeavH3RNsBXWDooR1mgD52dCrxIIuP7UotpfiwOikvcg==",
      "dev": true,
      "license": "MIT",
      "dependencies": {
-        "@vitest/utils": "4.1.9",
+        "@vitest/utils": "4.1.8",
        "pathe": "^2.0.3"
      },
      "funding": {
@@ -481,14 +481,14 @@
      }
    },
    "node_modules/@vitest/snapshot": {
-      "version": "4.1.9",
-      "resolved": "https://registry.npmjs.org/@vitest/snapshot/-/snapshot-4.1.9.tgz",
-      "integrity": "sha512-Jc7RKGNBo8Z28WYIm0Niej4xdSPByRf6mU58VpHQkd6Zh05rlnA+twjbK5HyeIGHxrzsc3mJgS43uM0CZKzaIA==",
+      "version": "4.1.8",
+      "resolved": "https://registry.npmjs.org/@vitest/snapshot/-/snapshot-4.1.8.tgz",
+      "integrity": "sha512-acfZboRmAIf05DEKcBQy33VXojFJjtUdLyo7oOmV9kebb2xdU01UknNiPuPZoJZQyO7DF0gZdTGTpeAzET9QPQ==",
      "dev": true,
      "license": "MIT",
      "dependencies": {
-        "@vitest/pretty-format": "4.1.9",
-        "@vitest/utils": "4.1.9",
+        "@vitest/pretty-format": "4.1.8",
+        "@vitest/utils": "4.1.8",
        "magic-string": "^0.30.21",
        "pathe": "^2.0.3"
      },
@@ -497,9 +497,9 @@
      }
    },
    "node_modules/@vitest/spy": {
-      "version": "4.1.9",
-      "resolved": "https://registry.npmjs.org/@vitest/spy/-/spy-4.1.9.tgz",
-      "integrity": "sha512-fHpsS6mIi+PiEW+vcRVOMkX1oSaPKne3VOclSFICPcGOmfKgXPU5iAah+wcNcj2xPrCCmfq99IDGf+EojhhvhA==",
+      "version": "4.1.8",
+      "resolved": "https://registry.npmjs.org/@vitest/spy/-/spy-4.1.8.tgz",
+      "integrity": "sha512-6EevtBp6OZOPF7bmz36HrGMeP3txgVSrgebWxHOafDXGkhIzfXK14f8KF6MuFfgXXUeHxmpD3BQxkV00/3s5mA==",
      "dev": true,
      "license": "MIT",
      "funding": {
@@ -507,13 +507,13 @@
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    },
    "node_modules/@vitest/utils": {
-      "version": "4.1.9",
-      "resolved": "https://registry.npmjs.org/@vitest/utils/-/utils-4.1.9.tgz",
-      "integrity": "sha512-A51o8ymO5PpqlWNnBP9ZHPXDIpuMtTLlGSjN7la4US+LJzoUMyhwjA5QXlm39JexgwHKW4Xjs8Z2d3dLCXOeuA==",
+      "version": "4.1.8",
+      "resolved": "https://registry.npmjs.org/@vitest/utils/-/utils-4.1.8.tgz",
+      "integrity": "sha512-uOJamYALNhfJ6iolExyQM40yIQwDqYnkKtQ5VCiSe17E33H0aQ/u+1GlRuz4LZBk6Mm3sg90G9hEbmEt37C1Zg==",
      "dev": true,
      "license": "MIT",
      "dependencies": {
-        "@vitest/pretty-format": "4.1.9",
+        "@vitest/pretty-format": "4.1.8",
        "convert-source-map": "^2.0.0",
        "tinyrainbow": "^3.1.0"
      },
@@ -921,9 +921,9 @@
      }
    },
    "node_modules/obug": {
-      "version": "2.1.3",
-      "resolved": "https://registry.npmjs.org/obug/-/obug-2.1.3.tgz",
-      "integrity": "sha512-9miFgM2OFba7hB+pRgvtV84pYTBaoTHohvmIgiRt6dRIzbwEOIaNaP+dIlGs2fNFoB0SeISs0Jz5WFVRid6Xyg==",
+      "version": "2.1.2",
+      "resolved": "https://registry.npmjs.org/obug/-/obug-2.1.2.tgz",
+      "integrity": "sha512-AWGB9WFcRXOQs48Z/udjI5ZcZMHXwX8XPByNpOydgcGsDLIzjGizhoMWJyKAWze7AVW/2W1i+/gPX4YtKe5cyg==",
      "dev": true,
      "funding": [
        "https://github.com/sponsors/sxzz",
@@ -1200,19 +1200,19 @@
      }
    },
    "node_modules/vitest": {
-      "version": "4.1.9",
-      "resolved": "https://registry.npmjs.org/vitest/-/vitest-4.1.9.tgz",
-      "integrity": "sha512-nE3/LEyc0z87uHYLZebqCUOaJr2hdtuPp7BQ4BosVFnfltxgAvMG08NyrSGlPpOUWvR27c5flSmYFTNr78L9GQ==",
+      "version": "4.1.8",
+      "resolved": "https://registry.npmjs.org/vitest/-/vitest-4.1.8.tgz",
+      "integrity": "sha512-flY6ScbCIt9HThs+C5HS7jvGOB560DJtk/Z15IQROTA6zEy49Nh8T/dofWTQL+n3vswqn87sbJNiuqw1SDp5Ig==",
      "dev": true,
      "license": "MIT",
      "dependencies": {
-        "@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",
+        "@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",
        "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": "4.1.9",
-        "@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",
+        "@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",
        "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__ = "1.7.0a2"
+__version__ = "1.6.9"
@@ -1,33 +0,0 @@
-# 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
@@ -1,231 +0,0 @@
-# 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.
@@ -1,143 +0,0 @@
-# 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
@@ -1,10 +0,0 @@
-# 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]"
@@ -1,27 +0,0 @@
-# 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 = "0.7.0"
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