Document Cargo installation and verified Metal/Vulkan decision backend

BENCHMARKS.md

@@ -2,7 +2,7 @@
 
 [Back to the model card](README.md)
 
-These are existing measurements of the downloadable Cortiq 0.7.8 model, not a new benchmark run. The charts are generated from [comparison.json](evidence/comparison.json); it includes the source report names and SHA-256 hashes.
+The quality and Jev/API comparisons below retain the original measurements of this CMF. New GPU measurements are documented separately in [Metal and Vulkan](GPU.md). The charts are generated from [comparison.json](evidence/comparison.json); it includes the source report names and SHA-256 hashes.
 
 ## Accuracy and coverage
 
@@ -51,7 +51,7 @@ The hybrid run replayed recorded oracle answers, with 24 additional live MASSIVE
 
 - Public test sets were reused across development iterations. This is not a new blind holdout.
 - Supervision is unequal: Cortiq was fitted on train + dev, while Jev received two training examples per label. Neither this result nor a confidence interval removes that difference.
-- English inputs, up to 512 tokens. The downloadable encoder is BAAI BGE-small-en-v1.5 with 75% MLP compression using Cortiq NVG, not the separate Embryo research model.
+- English inputs, up to 512 tokens. The downloadable model uses a Cortiq NVG-modified text encoder (75% MLP compression) and trained task topologies; it is distinct from the separate Embryo research model.
 - The confidence gate is calibrated in-domain. It is not a universal correctness or out-of-distribution guarantee.
 - Incremental fitting preserves other labels’ parameters; that alone does not guarantee unchanged decisions because labels can compete for the same input.
 - No hardware-normalized RAM, VRAM or energy comparison with Jev was measured. The file is 304,520,292 bytes; model-file size is not process memory.
@@ -61,19 +61,18 @@ The hybrid run replayed recorded oracle answers, with 24 additional live MASSIVE
 File SHA-256: `ed9b8ec2bbfe9e9fd30f14a5eaf82314f38bc7e7510a39772baa2de3801d79b1`.
 
 - Cortiq engine and CMF: Apache-2.0, [source](https://github.com/infosave2007/cmf).
-- [BGE-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5): BAAI, MIT; encoder compressed with Cortiq NVG.
+- Modified text-encoder component: MIT notices retained separately in [Component notices](NOTICES.md).
 - BANKING77: PolyAI, CC-BY-4.0. CLINC150: Larson et al., CC-BY-3.0. MASSIVE en-US: Amazon, CC-BY-4.0.
 - Resonance Routing — US Patent Application 19/452,440.
 
-The redesign changes documentation and figures only. No new accuracy, timing or paid oracle runs were performed for it.
+The original Jev/API charts are unchanged. The new GPU work adds local timing and CPU/GPU parity tests; it makes no paid oracle calls and does not retrain the model.
 
 ## Execution and language scope
 
-The measurements above use the **local decision path on CPU**. In Cortiq
-0.7.9, the decision encoder explicitly calls the host FP32 GEMM; it does not
-dispatch to Metal or Vulkan. GPU support elsewhere in the Cortiq engine does
-not establish GPU acceleration for this decision model. No GPU speedup is
-claimed here.
+The original local measurements above use CPU. A separate GPU-enabled development
+build now runs this same CMF on Metal and Vulkan. See [GPU measurements and usage](GPU.md)
+for request schedules, full-corpus parity, latency tails and release availability.
+Those GPU results do not replace the original Jev/API experiment.
 
 The included seed skills and their reported scores were evaluated in English.
 The CMF format itself does not impose a language; local language coverage

GPU.md

@@ -0,0 +1,108 @@
+# Metal and Vulkan: the same CMF, accelerated
+
+The model file, skills and gates stay unchanged. The GPU prepares text features
+and computes reconstruction errors; it does not generate an answer.
+
+**Availability:** these results use a validated development build. The GPU decision
+backend is not yet in the published crates.io package. `cargo install cortiq-cli --locked`
+installs the current stable CLI; it must not be read as installing this GPU build.
+
+![GPU latency, within-host comparisons; different request schedules and independent scales.](figures/gpu.svg)
+
+## RTX PRO 4000 Blackwell · Vulkan
+
+| Dataset | CPU p50, ms | GPU p50, ms | GPU p95, ms | GPU p99, ms |
+|---|---:|---:|---:|---:|
+| BANKING77 | 24.05–24.30 | 1.24–1.24 | 1.77–1.78 | 2.23–2.30 |
+| CLINC150 | 21.07–21.11 | 1.18–1.20 | 1.41–1.43 | 1.56–1.59 |
+| MASSIVE | 16.23–16.26 | 1.10–1.15 | 1.33–1.37 | 1.50–1.56 |
+
+Two full runs, each with 3,080 BANKING77, 4,500 CLINC150 and 2,974 MASSIVE rows.
+CPU baseline: native portable-release path on the same Xeon E5-2690 v4 server.
+CPU references are computed first; after GPU warmup, GPU requests run serially
+without a CPU reference calculation between requests. Ranges span both runs.
+This is warm local latency, not HTTP, cold startup, concurrent throughput or a Jev test.
+
+**Idle behavior matters:** a final-build diagnostic alternates CPU/GPU requests
+on 400 deterministically sampled rows per dataset. The GPU waits while the CPU
+computes its reference; these gaps allow the RTX to lower its memory clock.
+
+| Dataset | Rows | CPU p50, ms | Vulkan p50 / p95, ms |
+|---|---:|---:|---:|
+| BANKING77 | 400 | 25.29 | 8.57 / 9.16 |
+| CLINC150 | 400 | 21.50 | 5.02 / 8.84 |
+| MASSIVE | 400 | 16.56 | 3.40 / 7.50 |
+
+Do not substitute continuous-stream latency for sparse-traffic latency.
+No clocks, power limits or background keep-alive loads were forced. These
+diagnostic samples are separate from the full-corpus quality checks above.
+
+## Apple M4 · Metal
+
+Default alternating CPU/GPU protocol, two full runs:
+
+| Dataset | CPU p50, ms | GPU p50, ms | GPU p95, ms | GPU p99, ms |
+|---|---:|---:|---:|---:|
+| BANKING77 | 3.45–3.46 | 2.57–2.58 | 3.75–4.43 | 6.02–7.54 |
+| CLINC150 | 3.26–3.27 | 2.52–2.55 | 3.38–3.54 | 4.09–6.37 |
+| MASSIVE | 2.66–2.68 | 2.28–2.31 | 3.07–3.86 | 3.37–7.17 |
+
+Separate serial streams, also two full runs:
+
+| Dataset | CPU p50, ms | GPU p50, ms | GPU p95, ms | GPU p99, ms |
+|---|---:|---:|---:|---:|
+| BANKING77 | 3.43–3.43 | 2.08–2.09 | 3.13–3.15 | 3.75–3.80 |
+| CLINC150 | 3.24–3.25 | 2.06–2.13 | 2.93–4.11 | 3.15–5.33 |
+| MASSIVE | 2.63–2.64 | 1.81–3.21 | 2.70–5.44 | 2.92–5.97 |
+
+The Mac had an active desktop. Metal shares the GPU with other applications;
+one MASSIVE serial-stream run was slower than CPU, and some p95/p99 tails also
+regressed. Acceleration is not a guarantee for every request or workload.
+CPU remains the default; GPU selection is explicit.
+
+## Correctness and resources
+
+Across the two full runs per mode, winners, abstentions and hash features match
+CPU exactly. This preserves the existing model's quality; it is not a new accuracy
+improvement. Embedding tolerance remains `1e-5`; scaled reconstruction-error
+difference is limited to `2e-5`. No retraining, oracle calls or lower precision.
+[All per-dataset measurements and numerical checks](evidence/gpu.json).
+
+The working path is used by CLI, `/v1/decisions` and `/v1/route`. Hardware tests cover
+empty/long/Unicode input, non-aligned dimensions, non-orthogonal bases, multiple
+skills and concurrent requests. GPU failures return errors, not a hidden CPU fallback.
+Resident GPU weights use additional memory; model file size is not RAM or VRAM.
+No energy comparison or hardware-normalized Jev resource comparison was measured.
+
+| Host | CPU RSS, MiB | GPU-mode RSS, MiB |
+|---|---:|---:|
+| Apple M4 | 478.5 | 808.1 |
+| Xeon + RTX | 437.6 | 694.8 |
+
+RSS snapshots after 11 successful HTTP requests per backend, all three skills
+loaded; not peak memory. The Vulkan process also reported **522 MiB of VRAM**.
+Four concurrent clients exercised routing; both decision API aliases were
+checked. Temporary servers were stopped. CPU/GPU timing excludes model loading,
+shader compilation, response serialization, network and external oracles.
+
+## Using a GPU-enabled source checkout
+
+```bash
+# Apple Silicon
+cargo build --release -p cortiq-cli
+CORTIQ_DECISION_DEVICE=metal ./target/release/cortiq decide cortiq-decision.cmf \
+  --skill banking77 -p "I still have not received my new card"
+
+# Linux with a hardware Vulkan driver
+cargo build --release -p cortiq-cli --features decision-vulkan
+CORTIQ_DECISION_DEVICE=vulkan CORTIQ_DECISION_VULKAN_ADAPTER="RTX PRO 4000" \
+  ./target/release/cortiq decide cortiq-decision.cmf \
+  --skill banking77 -p "I still have not received my new card"
+```
+
+The same environment variables apply to `cortiq serve`. `/healthz` reports the
+actual adapter and completed GPU submissions. `gpu` timing includes the joint
+encoder and reconstruction pass plus its wait, not a fictional kernel-only time.
+Other GPUs require their own validation; a software Vulkan adapter is rejected.
+
+[Back to model card](README.md) · [Existing Jev comparison](BENCHMARKS.md)

NOTICES.md

@@ -0,0 +1,27 @@
+# Component notices
+
+Cortiq runtime, CMF implementation and Cortiq modifications: Apache-2.0.
+The modified text-encoder component retains the following third-party notice.
+This notice is not a description of the complete Cortiq Decision system.
+
+MIT License
+
+Copyright (c) the BAAI FlagEmbedding authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -2,7 +2,6 @@
 license: apache-2.0
 library_name: cortiq
 pipeline_tag: text-classification
-base_model: BAAI/bge-small-en-v1.5
 language:
   - en
 tags:
@@ -27,24 +26,20 @@ oracle for unfamiliar cases, then use its answers to grow your local skills.
 
 ## Quick start
 
-Download and unpack [Cortiq 0.7.9 for your platform](https://github.com/infosave2007/cmf/releases/tag/v0.7.9),
-then add `cortiq` to your PATH. No Rust or Hugging Face CLI required.
+Install the latest published CLI from crates.io (requires Rust):
 
 ```bash
-curl -fL -o cortiq-decision.cmf \
-  https://huggingface.co/infosave/cortiq-decision/resolve/main/cortiq-decision.cmf
-cortiq decide cortiq-decision.cmf --skill banking77 -p "I still have not received my new card"
+cargo install cortiq-cli --locked
 ```
 
-<details>
-<summary>Alternative: install with Cargo (requires Rust)</summary>
+Run the same command again to update. Download the model and make your first decision:
 
 ```bash
-cargo install cortiq-cli --version 0.7.9 --locked
+curl -fL -o cortiq-decision.cmf \
+  https://huggingface.co/infosave/cortiq-decision/resolve/main/cortiq-decision.cmf
+cortiq decide cortiq-decision.cmf --skill banking77 -p "I still have not received my new card"
 ```
 
-</details>
-
 **Result:** `card_arrival`, answered locally. No cloud key required.
 Included skills: `banking77`, `clinc150`, `massive`.
 
@@ -65,6 +60,8 @@ the model answers **54.30–92.11%** of requests locally, depending on the task.
 Local times include the entire text-to-decision path, not just the resonance
 kernel. API times include the network; these are not hardware-normalized tests.
 
+[New Metal and Vulkan measurements — same CMF, unchanged decisions →](GPU.md)
+
 ### Cost: cloud only when needed
 
 ![API fees per million decisions: Cortiq plus an optional oracle $3.01, $3.53, $8.48; Jev $183.69, $271.61, $110.86 for BANKING77, CLINC150 and MASSIVE respectively.](figures/cost.png)
@@ -101,8 +98,8 @@ To serve over HTTP: `cortiq serve cortiq-decision.cmf`.
 ---
 
 **Benchmark scope:** reused public datasets; CMF trained on train + dev,
-Jev given two examples per label. The encoder is BGE-small compressed with
-Cortiq NVG. [Method, data and licenses](BENCHMARKS.md) ·
+Jev given two examples per label. The model combines a Cortiq NVG-modified text encoder with
+trained resonance topologies. [Method, data and licenses](BENCHMARKS.md) ·
 [Chart data](evidence/comparison.json)
 
 Resonance Routing — US 19/452,440.

README_RU.md

@@ -13,24 +13,20 @@
 
 ## Быстрый запуск
 
-Скачайте и распакуйте [Cortiq 0.7.9 для своей платформы](https://github.com/infosave2007/cmf/releases/tag/v0.7.9),
-затем добавьте `cortiq` в PATH. Rust и Hugging Face CLI не нужны.
+Установите актуальную опубликованную CLI из crates.io (нужен Rust):
 
 ```bash
-curl -fL -o cortiq-decision.cmf \
-  https://huggingface.co/infosave/cortiq-decision/resolve/main/cortiq-decision.cmf
-cortiq decide cortiq-decision.cmf --skill banking77 -p "I still have not received my new card"
+cargo install cortiq-cli --locked
 ```
 
-<details>
-<summary>Альтернатива: установка через Cargo (нужен Rust)</summary>
+Для обновления повторите ту же команду. Скачайте модель и получите первое решение:
 
 ```bash
-cargo install cortiq-cli --version 0.7.9 --locked
+curl -fL -o cortiq-decision.cmf \
+  https://huggingface.co/infosave/cortiq-decision/resolve/main/cortiq-decision.cmf
+cortiq decide cortiq-decision.cmf --skill banking77 -p "I still have not received my new card"
 ```
 
-</details>
-
 **Результат:** `card_arrival` — ожидание доставки карты. Решение принято локально,
 ключ облачного API не нужен. Готовые навыки: `banking77`, `clinc150`, `massive`.
 
@@ -52,6 +48,8 @@ cargo install cortiq-cli --version 0.7.9 --locked
 Локальный замер включает весь путь от текста до решения, не только ядро.
 В API-замеры входит сеть; оборудование и режимы параллельности различаются.
 
+[Новые замеры Metal и Vulkan — тот же CMF, те же решения →](GPU.md)
+
 ### Стоимость: облако только для сложных случаев
 
 ![Плата за API на миллион решений: Cortiq с оракулом — $3,01 / $3,53 / $8,48; Jev — $183,69 / $271,61 / $110,86.](figures/cost.png)
@@ -89,8 +87,8 @@ HTTP-сервер: `cortiq serve cortiq-decision.cmf`.
 ---
 
 **Условия сравнения:** повторно использованные открытые наборы; CMF обучен
-на train + dev, Jev получил по два примера на метку. Энкодер — BGE-small,
-сжатый технологией Cortiq NVG. [Методика и лицензии](BENCHMARKS.md) ·
+на train + dev, Jev получил по два примера на метку. В модели объединены энкодер, переработанный
+технологией Cortiq NVG, и обученные резонансные топологии. [Методика и лицензии](BENCHMARKS.md) ·
 [Данные графиков](evidence/comparison.json)
 
 Resonance Routing — US 19/452,440.

evidence/gpu.json

@@ -0,0 +1,2807 @@
+{
+  "schema": "cortiq-decision-gpu-evidence/1",
+  "created_utc": "2026-09-28T05:26:36.972640+00:00",
+  "model_sha256": "ed9b8ec2bbfe9e9fd30f14a5eaf82314f38bc7e7510a39772baa2de3801d79b1",
+  "oracle_calls": 0,
+  "model_bytes_changed": false,
+  "precision": "FP32; no FP16/TF32; unchanged 1e-5 embedding tolerance",
+  "gpu_release_status": "Validated development build; GPU decision backend is not in the currently published crates.io package.",
+  "tests": [
+    {
+      "backend": "metal",
+      "mode": "paired",
+      "run": 1,
+      "source_sha256": "8b5d7b02728504308e209b21928965322a2dd1de30c2c37f8a774f8081a9924b",
+      "schema": "cortiq-decision-metal-comparison/2",
+      "model_sha": "386b6e43fd35d5aa9e9710529dfdf30aa868df98ab4434d8e45dae9829898f85",
+      "skill": "banking77",
+      "n": 3080,
+      "dataset_sha256": "649607fa7b124293c80b455d2effe5e41da713e1836ee936073fe34ab9dc6fba",
+      "device": "metal: Apple M4",
+      "warmup": 50,
+      "order": "paired serial; CPU-first/GPU-first alternates per row; same process, model, scorer and texts; no oracle",
+      "golden_max_abs": {
+        "cpu": 0.0,
+        "metal": 1.1920928955078125e-07
+      },
+      "max_embedding_abs": 1.6391277313232422e-07,
+      "max_reconstruction_error_abs": 1.1742115020751953e-05,
+      "max_reconstruction_error_scaled": 6.425947049137903e-06,
+      "winner_changes": 0,
+      "gate_changes": 0,
+      "hash_changes": 0,
+      "different_rows": [],
+      "correct": {
+        "cpu": 2875,
+        "metal": 2875
+      },
+      "accepted": {
+        "cpu": 2791,
+        "metal": 2791
+      },
+      "metal_command_buffers": 3080,
+      "cpu_command_buffers": 0,
+      "metal_resonance_dispatches": 3080,
+      "ms": {
+        "cpu_total": {
+          "p50": 3.458792,
+          "p95": 4.6185,
+          "p99": 6.479583
+        },
+        "metal_total": {
+          "p50": 2.5725409999999997,
+          "p95": 4.429583,
+          "p99": 7.538375
+        },
+        "cpu_encoder": {
+          "p50": 2.071917,
+          "p95": 3.203542,
+          "p99": 5.040916
+        },
+        "metal_split_encoder": {
+          "p50": 0.0,
+          "p95": 0.0,
+          "p99": 0.0
+        },
+        "cpu_resonance": {
+          "p50": 1.350666,
+          "p95": 1.4649159999999999,
+          "p99": 1.538958
+        },
+        "metal_ranking_cpu": {
+          "p50": 0.006333,
+          "p95": 0.010209,
+          "p99": 0.019042000000000003
+        },
+        "metal_joint_pipeline": {
+          "p50": 2.554542,
+          "p95": 4.40325,
+          "p99": 7.522084
+        }
+      },
+      "by_tokens": [
+        {
+          "tokens": "1-8",
+          "n": 230,
+          "cpu_ms": {
+            "p50": 3.093625,
+            "p95": 3.267084,
+            "p99": 3.378875
+          },
+          "gpu_ms": {
+            "p50": 2.420125,
+            "p95": 3.592583,
+            "p99": 5.5611250000000005
+          }
+        },
+        {
+          "tokens": "9-16",
+          "n": 2084,
+          "cpu_ms": {
+            "p50": 3.398666,
+            "p95": 3.682708,
+            "p99": 3.81625
+          },
+          "gpu_ms": {
+            "p50": 2.523584,
+            "p95": 3.9925409999999997,
+            "p99": 6.467167
+          }
+        },
+        {
+          "tokens": "17-32",
+          "n": 632,
+          "cpu_ms": {
+            "p50": 3.750125,
+            "p95": 4.5125,
+            "p99": 4.7405
+          },
+          "gpu_ms": {
+            "p50": 2.7897920000000003,
+            "p95": 4.739041,
+            "p99": 8.428542
+          }
+        },
+        {
+          "tokens": "33-64",
+          "n": 129,
+          "cpu_ms": {
+            "p50": 5.745417,
+            "p95": 7.550709,
+            "p99": 8.154875
+          },
+          "gpu_ms": {
+            "p50": 3.735917,
+            "p95": 6.810416,
+            "p99": 9.454334
+          }
+        },
+        {
+          "tokens": "65+",
+          "n": 5,
+          "cpu_ms": {
+            "p50": 10.160499999999999,
+            "p95": 11.220374999999999,
+            "p99": 11.220374999999999
+          },
+          "gpu_ms": {
+            "p50": 5.704416,
+            "p95": 15.93725,
+            "p99": 15.93725
+          }
+        }
+      ],
+      "gpu_scope": "One command buffer: embedding lookup, transformer layers, row-order pooling and FP32 L2, reconstruction errors; tokenization, hashing and ranking/gates on CPU. Joint GPU time is not split into fictitious encoder/resonance wall times."
+    },
+    {
+      "backend": "metal",
+      "mode": "paired",
+      "run": 2,
+      "source_sha256": "17972babb78332434ae98acddc9ceb5b3635aa60811fa9863ad636a0bae95015",
+      "schema": "cortiq-decision-metal-comparison/2",
+      "model_sha": "386b6e43fd35d5aa9e9710529dfdf30aa868df98ab4434d8e45dae9829898f85",
+      "skill": "banking77",
+      "n": 3080,
+      "dataset_sha256": "649607fa7b124293c80b455d2effe5e41da713e1836ee936073fe34ab9dc6fba",
+      "device": "metal: Apple M4",
+      "warmup": 50,
+      "order": "paired serial; CPU-first/GPU-first alternates per row; same process, model, scorer and texts; no oracle",
+      "golden_max_abs": {
+        "cpu": 0.0,
+        "metal": 1.1920928955078125e-07
+      },
+      "max_embedding_abs": 1.6391277313232422e-07,
+      "max_reconstruction_error_abs": 1.1742115020751953e-05,
+      "max_reconstruction_error_scaled": 6.425947049137903e-06,
+      "winner_changes": 0,
+      "gate_changes": 0,
+      "hash_changes": 0,
+      "different_rows": [],
+      "correct": {
+        "cpu": 2875,
+        "metal": 2875
+      },
+      "accepted": {
+        "cpu": 2791,
+        "metal": 2791
+      },
+      "metal_command_buffers": 3080,
+      "cpu_command_buffers": 0,
+      "metal_resonance_dispatches": 3080,
+      "ms": {
+        "cpu_total": {
+          "p50": 3.4517089999999997,
+          "p95": 4.600292,
+          "p99": 6.439208
+        },
+        "metal_total": {
+          "p50": 2.583042,
+          "p95": 3.750917,
+          "p99": 6.0215
+        },
+        "cpu_encoder": {
+          "p50": 2.066834,
+          "p95": 3.2210419999999997,
+          "p99": 5.067708
+        },
+        "metal_split_encoder": {
+          "p50": 0.0,
+          "p95": 0.0,
+          "p99": 0.0
+        },
+        "cpu_resonance": {
+          "p50": 1.3495409999999999,
+          "p95": 1.467708,
+          "p99": 1.539374
+        },
+        "metal_ranking_cpu": {
+          "p50": 0.006416,
+          "p95": 0.015667,
+          "p99": 0.01925
+        },
+        "metal_joint_pipeline": {
+          "p50": 2.563375,
+          "p95": 3.721,
+          "p99": 6.00075
+        }
+      },
+      "by_tokens": [
+        {
+          "tokens": "1-8",
+          "n": 230,
+          "cpu_ms": {
+            "p50": 3.1007919999999998,
+            "p95": 3.277709,
+            "p99": 3.465416
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