DecisionNode Edge 0.9.1

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+decisionnode-edge-0.9.1-q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
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LICENSE-base

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LICENSING.md

@@ -0,0 +1,19 @@
+# Licensing
+
+DecisionNode Edge and DecisionNode Edge Mini (the model weights and the `decisionnode_edge` runtime) are available under two licenses.
+
+## Open source: AGPL-3.0
+
+You may use, study, modify and share DecisionNode Edge under the GNU Affero General Public License, version 3 (`LICENSE`). This suits personal projects, education, research and open-source work.
+
+If you distribute DecisionNode Edge or a work based on it, or let others use such a work over a network, the AGPL requires you to make the complete corresponding source of that work available to them under the same license.
+
+## Commercial license
+
+To use DecisionNode Edge in a commercial product or service without the obligations of the AGPL, for example inside closed-source software, in a device you sell or in a service you run for your customers, you need a commercial license from Bynn Intelligence Inc.
+
+Write to licensing@bynn.com.
+
+## The base models
+
+DecisionNode Edge is built on Qwen3.5-2B-Base and DecisionNode Edge Mini on Qwen3.5-0.8B-Base. The Qwen team releases both under the Apache License, Version 2.0. That license is in `LICENSE-base`, and `NOTICE` lists the changes we made.

MANIFEST.sha256

@@ -0,0 +1,30 @@
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NOTICE

@@ -0,0 +1,10 @@
+DecisionNode Edge 0.9.1
+Copyright 2026 Bynn Intelligence Inc.
+
+DecisionNode Edge is licensed under the GNU Affero General Public License, version 3 (see LICENSE),
+or under a commercial license from Bynn Intelligence Inc. (licensing@bynn.com). See LICENSING.md.
+
+This model is a modified version of Qwen3.5-2B-Base (https://huggingface.co/Qwen/Qwen3.5-2B-Base),
+which is licensed under the Apache License, Version 2.0 (see LICENSE-base). Bynn Intelligence Inc.
+further trained the model and added a decision head and a calibration. The base model's multi-token
+prediction layer is not included.

README.md

@@ -0,0 +1,244 @@
+---
+license: agpl-3.0
+license_link: LICENSE
+base_model: Qwen/Qwen3.5-2B-Base
+base_model_relation: finetune
+library_name: transformers
+tags:
+  - decision-making
+  - classification
+  - calibration
+  - structured-output
+  - on-device
+  - multimodal
+---
+
+# DecisionNode Edge
+
+DecisionNode Edge is an open-weight decision model from [Bynn Intelligence](https://decisionnode.com). It reads text and images and answers each question with a typed, calibrated decision: a choice, a score, a yes-or-no probability or a number. It never generates free text. It runs entirely on your own hardware, from a datacenter GPU to a laptop CPU, and uses the same request and response format as the [DecisionNode API](https://decisionnode.com/docs/api).
+
+This repository contains DecisionNode Edge 0.9.1, the larger of the two DecisionNode Edge models. [DecisionNode Edge Mini](https://huggingface.co/bynn-intelligence-com/decisionnode-edge-mini) is a 0.85-billion-parameter sibling for CPUs and constrained devices.
+
+## Highlights
+
+- **Typed decisions instead of text.** Four question types, each answered as a probability distribution over the allowed values, ready to use in code without parsing.
+- **Calibrated.** Probabilities are calibrated on held-out data, so a fixed threshold such as 0.7 means the same thing from one question to the next.
+- **Constant cost per question.** Every question is a single forward pass, whether it has 2 options or 255.
+- **Fast everywhere.** Optimized backends for NVIDIA GPUs, Apple silicon, x86 CPUs and llama.cpp devices, selected automatically, with consistent answers across all of them.
+- **Private by design.** Inference runs offline. Nothing you send to the model leaves your machine.
+- **Compatible with the DecisionNode API.** Code written for the hosted API runs against DecisionNode Edge with only the transport changed.
+
+## The DecisionNode family
+
+DecisionNode Edge is the open-weight member of the DecisionNode family. The hosted models, DecisionNode and DecisionNode Flash, offer the highest accuracy and the full feature set through the [DecisionNode API](https://decisionnode.com/docs/api), at very low cost.
+
+| Model | Availability | Decision accuracy |
+|---|---|---|
+| DecisionNode | Hosted API | 81.7% |
+| DecisionNode Flash | Hosted API | 78.3% |
+| DecisionNode Edge | Open weights | 74.2% |
+| DecisionNode Edge Mini | Open weights | 69.2% |
+
+All four models are measured on the same 15-benchmark evaluation suite (see Evaluation).
+
+Available only on the hosted platform:
+
+- **Boosters.** Specialist models that read each image before the decision is made: Human Age, AI-generated image detection, Face detection, Face recognition, OCR, Depth Vision and Segment Vision. See [boosters](https://decisionnode.com/docs/concepts/boosters).
+- **Knowledge.** Connect your own documents, and the Knowledge booster retrieves the passages each decision needs. See [knowledge](https://decisionnode.com/docs/knowledge).
+- **Fine-tuning.** Adapt DecisionNode to your own decisions and data. See [fine-tuning](https://decisionnode.com/docs/fine-tuning).
+- **Video and localization.** Video input, and points and bounding boxes in images.
+- **Scale.** Streaming sessions for high-frequency decisions, batch jobs at a discount, file uploads and inputs of up to 65,536 tokens.
+- **Low cost.** Pricing starts at a few cents per million input tokens, output tokens are free, and every account includes a free daily allowance. See [pricing](https://decisionnode.com/#pricing).
+
+[Create a free account](https://decisionnode.com/console/keys) to get an API key.
+
+### Open models
+
+| Model | Parameters | Decision accuracy | NVIDIA RTX PRO 6000 (vLLM) | Apple M5 Max (MLX) | Desktop CPU (OpenVINO) |
+|---|---|---|---|---|---|
+| [DecisionNode Edge](https://huggingface.co/bynn-intelligence-com/decisionnode-edge) | 2.21B | 74.2% | 9.5 ms | 35 ms | 0.46 s |
+| [DecisionNode Edge Mini](https://huggingface.co/bynn-intelligence-com/decisionnode-edge-mini) | 0.85B | 69.2% | 7.3 ms | 17 ms | 0.24 s |
+
+Decision accuracy is the mean over our 15-benchmark evaluation suite (see Evaluation). Times are the median for one question at about 500 tokens of input; the desktop CPU is an 8-core AMD Ryzen 7 9800X3D.
+
+## Model details
+
+| | |
+|---|---|
+| Developer | Bynn Intelligence Inc. |
+| Version | 0.9.1 (October 2026) |
+| Parameters | 2.21 billion (1.88 billion in the language model, 0.33 billion in the vision encoder) |
+| Base model | [Qwen3.5-2B-Base](https://huggingface.co/Qwen/Qwen3.5-2B-Base) |
+| Input | Text, and up to 16 images per request (JPEG, PNG or WebP) |
+| Output | Typed decisions: `choice`, `score`, `truth` and `number` |
+| Context | Evaluated on inputs of up to 8,192 tokens; the runtime accepts up to 16,384 |
+| Weights | bfloat16 safetensors, plus an 8-bit GGUF build (2.0 GB) |
+| License | AGPL-3.0, or a commercial license from Bynn Intelligence |
+
+## Quick start
+
+Install the runtime from PyPI:
+
+```bash
+pip install decisionnode-edge            # PyTorch, Transformers and safetensors
+pip install "decisionnode-edge[images]"  # adds image support
+```
+
+Ask a question:
+
+```python
+from huggingface_hub import snapshot_download
+from decisionnode_edge import EdgeModel
+
+model = EdgeModel.load(snapshot_download("bynn-intelligence-com/decisionnode-edge"))
+response = model.decide({
+    "state": "Customer: I was charged twice and nobody has replied for 3 days.",
+    "questions": {
+        "route": {
+            "type": "choice",
+            "instructions": "Where should this go?",
+            "criteria": {"billing": "money", "bug": "broken", "account": "login"},
+        },
+        "urgency": {
+            "type": "score",
+            "instructions": "How urgent is this?",
+            "criteria": ["routine", "today", "urgent", "critical"],
+        },
+        "escalate": {"type": "truth", "instructions": "Escalate to a human now?"},
+    },
+})
+print(response["answers"]["route"]["choice"], response["answers"]["escalate"]["truth"])
+```
+
+The response follows the DecisionNode API:
+
+```json
+{
+  "model": "decisionnode-edge-0.9.1",
+  "answers": {
+    "route": {"type": "choice", "choice": "billing", "confidence": 0.93, "probabilities": {"account": 0.02, "billing": 0.95, "bug": 0.03}},
+    "urgency": {"type": "score", "score": 2.2, "confidence": 0.57, "legend": {"0": "routine", "1": "today", "2": "urgent", "3": "critical"}, "probabilities": {"0": 0.01, "1": 0.09, "2": 0.59, "3": 0.31}},
+    "escalate": {"type": "truth", "truth": 0.74}
+  },
+  "usage": {"input_tokens": 103, "output_tokens": 0}
+}
+```
+
+Images are passed as base64 `data` with a `media_type`, or as a local `path`:
+
+```python
+model.decide({
+    "state": {"claim": {"merchant": "Nordic Rail", "total": "740.00", "currency": "SEK"}},
+    "images": [{"id": "receipt", "path": "receipt.jpg"}],
+    "questions": {
+        "total_matches": {"type": "truth", "instructions": "Does the total on the receipt equal the total in the claim?"},
+        "items": {"type": "number", "instructions": "How many line items are on the receipt?", "min": 0, "max": 50},
+    },
+})
+```
+
+The runtime also runs from the command line (`python -m decisionnode_edge <model-folder> request.json`) and as a local HTTP server (`python -m decisionnode_edge serve <model-folder>`).
+
+## Question types
+
+| Type | You provide | You receive |
+|---|---|---|
+| `choice` | 1 to 255 options, each with an optional description | the selected option, a confidence and a probability for every option |
+| `score` | 1 to 10 ordered levels, from low to high | the expected level, a confidence and a probability for every level |
+| `truth` | a statement or question, with optional `true` and `false` criteria | the probability that the answer is yes |
+| `number` | `min`, `max` and `step` (up to 4,096 values) | the most probable value, the expected value, a confidence and the probable values |
+
+Write what the question means into `instructions` and `criteria`; the question's key is never shown to the model. Images are seen by every question in the request.
+
+## Performance
+
+`EdgeModel.load` selects the fastest backend installed on your machine. Every backend computes the same internal representation and applies the same decision head and calibration, so answers are consistent across devices. Median time for one question at about 500 and 2,000 tokens of input:
+
+| Device | Backend | Install | Time |
+|---|---|---|---|
+| NVIDIA GPU | vLLM | `pip install "decisionnode-edge[vllm]"` | 9.5 / 23 ms (RTX PRO 6000) |
+| Apple silicon | MLX | `pip install "decisionnode-edge[mlx]"` | 35 / 127 ms (M5 Max) |
+| x86 CPU | OpenVINO | `pip install "decisionnode-edge[openvino]"`, then a one-time build | 0.46 / 1.7 s (Ryzen 7 9800X3D) |
+| Any device llama.cpp supports, including NVIDIA Jetson | llama.cpp with the included 8-bit GGUF | `llama-server` on your path | 14 / 44 ms (RTX PRO 6000), 55 / 212 ms (M5 Max), 1.1 / 5.1 s (Ryzen 7 9800X3D) |
+| Any | PyTorch (reference) | included | 14 / 28 ms (RTX PRO 6000, with optional kernels) |
+
+Each backend reproduces the reference bfloat16 answer on at least 98.6% of a fixed set of held-out questions. Images, and inputs longer than a backend supports, are handled by the PyTorch backend automatically. A request with several questions costs one forward pass per question.
+
+GPU memory in bfloat16 with PyTorch, one request at a time:
+
+| Input | Peak memory |
+|---|---|
+| Weights alone | 4.43 GB |
+| 2,048 text tokens | 4.62 GB |
+| 8,192 text tokens | 5.09 GB |
+| 16,384 text tokens | 5.71 GB |
+| A 2,048-pixel image (3,109 tokens) | 5.10 GB |
+
+A GPU with 6 GB of memory serves requests of up to about 8,000 tokens in bfloat16; the 8-bit GGUF build needs roughly half the memory for the weights.
+
+## Evaluation
+
+We evaluate the DecisionNode models on an internal suite of 15 held-out decision benchmarks. The suite covers routing and triage, classification, scoring against rubrics, rule following, long documents, multilingual input, knowledge-intensive questions, decisions in web tasks and resistance to prompt injection.
+
+| Model | Mean accuracy |
+|---|---|
+| DecisionNode Edge 0.9.1 (bfloat16) | 74.2% |
+| DecisionNode Edge 0.9.1 (8-bit GGUF) | 74.2% |
+
+Compared with DecisionNode Edge Mini, Edge is about 5 points more accurate and roughly half as fast on a CPU. The 8-bit build matches the bfloat16 weights on all 15 benchmarks; 4-bit builds lose accuracy and are not offered.
+
+## Intended use
+
+DecisionNode Edge is designed for the frequent, well-defined judgments inside software, where hand-written rules are too brittle and a general-purpose language model is too slow, too costly or too far away:
+
+- routing and triage of tickets, messages, documents and alerts
+- classification and scoring against criteria you define in the request
+- validation and guardrail checks before an action runs
+- reading values from images, such as whether a total matches or how many items are present
+- applications that must run offline or keep data on the device
+
+## Limitations
+
+- **Decisions about people need human oversight.** Do not use DecisionNode Edge to make consequential decisions about individuals on its own. Uses in hiring, credit, housing, insurance, education, benefits, law enforcement or health care require a person who reviews each decision and can overrule it, in line with the DecisionNode acceptable use policy.
+- **Calibrated, not infallible.** An answer given with probability 0.9 is wrong about one time in ten. Use the probabilities to decide when to defer to a person or a larger model.
+- **Accuracy varies by domain and language.** Evaluate the model on your own data before relying on it.
+- **Input length.** The model was evaluated on inputs of up to 8,192 tokens. The runtime accepts up to 16,384 tokens and rejects longer inputs rather than truncating them.
+- **Not a generative model.** DecisionNode Edge does not produce text. Use it through the runtime; loading it in a text-generation pipeline yields meaningless output.
+- **Scope.** Video input, object localization, boosters, knowledge and fine-tuning are available on the hosted DecisionNode platform, not in DecisionNode Edge.
+
+## Files
+
+| File | Contents |
+|---|---|
+| `model.safetensors`, `config.json` | model weights (bfloat16) and configuration |
+| `readout.safetensors`, `calibration.json` | decision head and calibration |
+| `tokenizer.json`, `tokenizer_config.json` | tokenizer |
+| `preprocessor_config.json`, `video_preprocessor_config.json` | image preprocessing settings |
+| `decisionnode_edge.json` | prompt format and limits used by the runtime |
+| `decisionnode-edge-0.9.1-q8_0.gguf`, `decisionnode_edge_build.json` | 8-bit GGUF build for llama.cpp and its build record |
+| `runtime/` | the `decisionnode-edge` Python package, for offline installation (`pip install ./runtime`) |
+| `examples/request.json` | an example request |
+| `LICENSE`, `LICENSING.md`, `NOTICE` | license terms |
+| `LICENSE-base` | the license of the base model |
+| `MANIFEST.sha256` | checksums of every file |
+
+## License
+
+DecisionNode Edge is released under the [GNU Affero General Public License v3.0](LICENSE). For use in commercial products and services without the obligations of the AGPL, a commercial license is available from Bynn Intelligence Inc.; see [LICENSING.md](LICENSING.md).
+
+The base model, [Qwen3.5-2B-Base](https://huggingface.co/Qwen/Qwen3.5-2B-Base), is released by the Qwen team under the Apache License 2.0, reproduced in `LICENSE-base`.
+
+## Citation
+
+```bibtex
+@misc{bynn2026decisionnodeedge,
+  title  = {{DecisionNode Edge}},
+  author = {{Bynn Intelligence}},
+  year   = {2026},
+  url    = {https://huggingface.co/bynn-intelligence-com/decisionnode-edge}
+}
+```
+
+## Contact
+
+Commercial licensing, questions and reports: [licensing@bynn.com](mailto:licensing@bynn.com). The DecisionNode API and accounts: [decisionnode.com](https://decisionnode.com).

calibration.json

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config.json

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+  },
+  "vision_end_token_id": 248054,
+  "vision_start_token_id": 248053
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decisionnode_edge_build.json

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+  "format": "decisionnode-edge-build-1",
+  "backend": "llamacpp",
+  "model": "decisionnode-edge-0.9.1",
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+  "general_name": "DecisionNode Edge 0.9.1"
+}

examples/request.json

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+{
+  "state": {
+    "ticket": "Hi, I was charged twice for my March invoice and nobody has replied to my two emails since Monday. Please refund the second charge.",
+    "customer": {"plan": "business", "since": "2023-05-14"}
+  },
+  "questions": {
+    "route": {
+      "type": "choice",
+      "instructions": "Which team should handle `ticket`?",
+      "criteria": {
+        "billing": "charges, invoices, refunds",
+        "bug": "something in the product is broken",
+        "account": "login, access, settings"
+      }
+    },
+    "urgency": {
+      "type": "score",
+      "instructions": "How urgent is `ticket`?",
+      "criteria": ["routine", "today", "urgent", "critical"]
+    },
+    "refund": {
+      "type": "truth",
+      "instructions": "Does the customer ask for a refund?"
+    },
+    "emails": {
+      "type": "number",
+      "instructions": "How many emails has the customer sent before this one?",
+      "min": 0,
+      "max": 10
+    }
+  }
+}

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+    ],
+    "processor_class": "Qwen3VLProcessor",
+    "image_processor_type": "Qwen2VLImageProcessorFast"
+}

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runtime/LICENSE

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+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
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+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
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+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
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+
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+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

runtime/README.md

@@ -0,0 +1,238 @@
+# DecisionNode Edge runtime
+
+The official runtime for the DecisionNode Edge models from Bynn Intelligence. Load a model, send a request in the DecisionNode API format, and receive typed, calibrated decisions. It runs on NVIDIA GPUs, Apple silicon and CPUs, entirely offline.
+
+Models: [DecisionNode Edge](https://huggingface.co/bynn-intelligence-com/decisionnode-edge) and [DecisionNode Edge Mini](https://huggingface.co/bynn-intelligence-com/decisionnode-edge-mini) on Hugging Face.
+
+## Install
+
+```bash
+pip install decisionnode-edge              # torch, transformers, safetensors
+pip install "decisionnode-edge[images]"    # adds Pillow and torchvision, for pictures
+```
+
+The model folders carry a copy of this package in `runtime/` for machines without network access: `pip install ./runtime` (or `"./runtime[images]"`) from the model folder installs the same thing.
+
+Optional, for speed on NVIDIA GPUs with the PyTorch backend: `flash-linear-attention` and `causal-conv1d`. transformers uses them when they are installed. Without them the model gives the same answers about twice as slowly on long inputs.
+
+### The fastest install for your device
+
+The runtime reads the model with PyTorch unless a faster backend is installed. Install the one for your device:
+
+| Device | Install | Backend |
+|---|---|---|
+| NVIDIA GPU (Linux) | `pip install "decisionnode-edge[vllm]"` | `vllm` |
+| x86 CPU | `pip install "decisionnode-edge[openvino]"`, then build the OpenVINO files once ([below](#building-what-a-backend-loads)) | `openvino` |
+| Apple silicon | `pip install "decisionnode-edge[mlx]"` | `mlx` |
+| Any device llama.cpp supports, including NVIDIA Jetson | llama.cpp's `llama-server`; the model folder ships the 8-bit GGUF file ([below](#building-what-a-backend-loads)) | `llamacpp` |
+
+Every backend agrees with the PyTorch reference on at least 98% of top answers in our tests, and the remaining differences fall on near ties. A backend only computes the model's last hidden state; the runtime then applies the model's own answer table (`readout.safetensors`) and calibration (`calibration.json`) in float32, as it does on PyTorch.
+
+Measured with one `truth` question per request, one request at a time, median time of the whole `decide` call:
+
+| Device (as measured) | Backend | Edge, about 500 / 2,000 tokens | Edge Mini | Answers kept (Edge, Edge Mini) | PyTorch on the same device (Edge; Edge Mini) |
+|---|---|---|---|---|---|
+| NVIDIA RTX PRO 6000 Blackwell | `vllm` | 9.5 / 23 ms | 7.3 / 15 ms | 99.7%, 99.2% | 14 / 28 ms; 14 / 16 ms (with the optional kernels) |
+| AMD Ryzen 7 9800X3D (8 cores) | `openvino` | 0.46 / 1.7 s | 0.24 / 0.90 s | 98.6%, 98.4% | bfloat16: 0.49 / 2.6 s; 0.28 / 1.4 s. float32, the default: 1.6 / 7.8 s; 0.66 / 4.0 s |
+| Apple M5 Max | `mlx` | 35 / 127 ms | 17 / 65 ms | 99.5%, 99.3% | `device="mps"`: 0.30 / 1.3 s; 0.28 / 1.2 s |
+| Apple M5 Max | `llamacpp` (Metal) | 55 / 212 ms | 30 / 112 ms | 99.4%, 99.0% | |
+| NVIDIA RTX PRO 6000 Blackwell | `llamacpp` (CUDA) | 14 / 44 ms | 10.5 / 30 ms | 99.2%, 98.4% | |
+| AMD Ryzen 7 9800X3D (8 cores) | `llamacpp` (CPU) | 1.1 / 5.1 s | 0.58 / 2.7 s | 99.8%, 98.1% (412 questions up to 512 tokens) | |
+
+At about 8,000 tokens: `vllm` 87 ms (Edge) and 50 ms (Edge Mini), `mlx` 625 and 297 ms, `llamacpp` 218 and 160 ms on CUDA and 0.92 and 0.55 s on Metal. OpenVINO hands prompts of that length to PyTorch.
+
+"Answers kept" is how often the backend's top answer equals the PyTorch reference (bfloat16 on an NVIDIA GPU) on a fixed set of 2,056 held-out questions; for OpenVINO, on the 1,936 of them up to 2,048 tokens, the longest it is built for.
+
+The GPU is an NVIDIA RTX PRO 6000 Blackwell workstation card, the CPU an AMD Ryzen 7 9800X3D (8 cores, 16 threads), the Mac an Apple M5 Max. Versions: vLLM 0.32.0, OpenVINO 2026.4.1, MLX 0.32.4 with mlx-lm 0.32.2, llama.cpp at commit f174553, PyTorch 2.13 and 2.14, transformers 5.18 and 5.19. All backends ran in bfloat16 except llama.cpp, which ran the 8-bit (Q8_0) file.
+
+## Use
+
+```python
+from decisionnode_edge import EdgeModel
+
+model = EdgeModel.load("path/to/decisionnode-edge")   # CUDA if present, else the CPU
+
+response = model.decide({
+    "state": {"ticket": "I was charged twice and nobody has replied for 3 days."},
+    "questions": {
+        "route": {
+            "type": "choice",
+            "instructions": "Where should `ticket` go?",
+            "criteria": {"billing": "money", "bug": "something broken", "account": "login and access"},
+        },
+        "urgency": {
+            "type": "score",
+            "instructions": "How urgent is `ticket`?",
+            "criteria": ["routine", "today", "urgent", "critical"],
+        },
+        "escalate": {"type": "truth", "instructions": "Should a human take over now?"},
+        "days": {"type": "number", "instructions": "How many days has the customer waited?", "min": 0, "max": 30},
+    },
+})
+
+response["answers"]["route"]       # {"type": "choice", "choice": ..., "confidence": ..., "probabilities": {...}}
+response["answers"]["escalate"]    # {"type": "truth", "truth": ...}
+```
+
+`model.predict(request)` gives the unrounded calibrated probabilities per question instead: one per option or level in request order, `[yes, no]` for a truth question, one per grid value for a number question.
+
+A response names the model that answered: `decisionnode-edge-0.9.1` for DecisionNode Edge, `decisionnode-edge-mini-0.9.1` for DecisionNode Edge Mini (the `name` in the model directory's `decisionnode_edge.json`).
+
+From the command line:
+
+```bash
+python -m decisionnode_edge path/to/decisionnode-edge request.json
+cat request.json | python -m decisionnode_edge path/to/decisionnode-edge -
+```
+
+### Serving over HTTP
+
+```bash
+python -m decisionnode_edge serve path/to/decisionnode-edge --host 127.0.0.1 --port 8000
+python -m decisionnode_edge serve path/to/decisionnode-edge --api-key key.txt     # callers send Authorization: Bearer <key>
+```
+
+A small server on Python's standard library, with nothing more to install. It loads the model with the backend `EdgeModel.load` picks (or `--backend`, `--artifacts`, `--device`, `--dtype`), answers one request at a time, and listens on 127.0.0.1 unless `--host` says otherwise.
+
+| Route | What |
+|---|---|
+| `POST /v1/systemone` | the `/v1/systemone` format used by public decision-model leaderboards: `{"model", "state", "questions", "assets"}` with `noul`, `choice` and `score` questions. A `noul` is answered `{"type": "noul", "noul": p}`, a choice and a score as on `/v1/decide` (probabilities that sum to 1). `model` is required, and any name is accepted |
+| `POST /v1/decide` | the request and answers of this runtime ([below](#the-request)), `number` and `truth` included |
+| `GET /v1/models` | `{"models": [{"name", "description", "release_date"}]}` |
+| `GET /healthz` | `{"status": "ok", "model", "backend"}`, no key needed |
+
+Every answer carries `usage`: `input_tokens` and `output_tokens` (always 0). Pictures go as base64: the runtime's `images` with `data` (a local `path` is refused over HTTP, so a caller can never make the server read a file), or, on `/v1/systemone`, an `assets` map, `{"<ref>": {"type": "image", "format": "png", "data_base64": "..."}}` with format `png`, `jpg` or `jpeg`. Each asset becomes a picture whose id is its reference.
+
+Errors come as `{"detail": {"error_type": ..., "message": ...}}` (a 422 as a list of field errors). A request longer than the model's limit is a 400 whose message says "maximum context length"; a choice with more than 255 options is a 400 that says "options per choice".
+
+### Options
+
+`EdgeModel.load(path, device=None, dtype=None, backend=None, artifacts=None, options=None)`:
+
+- `device`: `"cuda"`, `"cuda:1"`, `"cpu"` or `"mps"`. Default: CUDA when present, else the CPU. It also sets the device class the backend is chosen for.
+- `dtype`: `"bfloat16"` or `"float32"`, for PyTorch. Default: bfloat16 on a GPU (the reference precision) and float32 on a CPU. On CPUs with bfloat16 support (recent AMD and Intel parts) `dtype="bfloat16"` was about three times faster in our tests, with answers as close to the GPU's as float32 gives.
+- `backend`: `"auto"` (the default), `"torch"`, `"vllm"`, `"openvino"`, `"mlx"` or `"llamacpp"`.
+- `artifacts`: the directory the build tool wrote, for `openvino` and `llamacpp`.
+- `options`: the backend's settings ([below](#backend-options)).
+
+### Choosing a backend
+
+By default `EdgeModel.load` takes the fastest backend that is installed and can run on the machine:
+
+| Device class | Order |
+|---|---|
+| NVIDIA GPU | `vllm`, `llamacpp`, `torch` |
+| Apple silicon | `mlx`, `llamacpp`, `torch` |
+| Any other CPU | `openvino`, `llamacpp`, `torch` |
+
+`openvino` counts only when `artifacts` holds an OpenVINO build, and `llamacpp` only when `artifacts` holds a GGUF build and `llama-server` is found (or `options` names a running server). Asked for by name, `openvino` and `llamacpp` also find a build in the model folder itself, such as the GGUF file that ships there. `model.backend` says which one was taken.
+
+To choose yourself:
+
+```python
+model = EdgeModel.load("path/to/decisionnode-edge", backend="torch")
+model = EdgeModel.load("path/to/decisionnode-edge", backend="openvino", artifacts="path/to/edge-openvino")
+model = EdgeModel.load("path/to/decisionnode-edge", backend="llamacpp", options={"url": "http://127.0.0.1:8080"})
+```
+
+The same from the environment: `DECISIONNODE_EDGE_BACKEND`, `DECISIONNODE_EDGE_ARTIFACTS`, and `DECISIONNODE_EDGE_OPTIONS` (a JSON object). Arguments win over the environment. On the command line: `python -m decisionnode_edge path/to/decisionnode-edge request.json --backend openvino --artifacts path/to/edge-openvino`.
+
+The faster backends read text. PyTorch reads what they do not take: pictures, and prompts longer than the backend's limit (an OpenVINO build's longest length, 2,048 tokens by default; a llama.cpp context, 16,384 tokens by default). It is loaded the first time it is needed, on `device` in `dtype`, and stays loaded. If most of your requests carry pictures, use `backend="torch"`.
+
+### Building what a backend loads
+
+The build tools read the model directory and write a new directory. They never change the model directory.
+
+**OpenVINO.** One file per static input length, 512 and 2,048 tokens by default:
+
+```bash
+pip install "decisionnode-edge[openvino]"
+python -m decisionnode_edge.build openvino path/to/decisionnode-edge --out path/to/edge-openvino
+python -m decisionnode_edge.build openvino path/to/decisionnode-edge --out path/to/edge-openvino --lengths 512,1024,2048
+```
+
+The export runs on the GPU when there is one (`--device cuda`, then the default) and on the CPU otherwise. On our machine it took 14 minutes for 512 tokens and 19 minutes for 2,048, per length. The files keep the weights in float32 (exact copies of the bfloat16 weights) and OpenVINO runs them in bfloat16, so the build takes 6.2 GB for Edge Mini and 15.2 GB for Edge. Before it ends, the tool runs each file once against PyTorch.
+
+A prompt is padded to the next length it fits, so a 600-token prompt costs about what a 2,048-token one does. Add a length (for example `--lengths 512,1024,2048`) if many of your prompts fall between two lengths. Lengths above a few thousand tokens take hours to export; longer prompts go to PyTorch.
+
+**GGUF for llama.cpp.** The model folder ships the 8-bit file (`decisionnode-edge-0.9.1-q8_0.gguf`, `decisionnode-edge-mini-0.9.1-q8_0.gguf`) with its build record, so `EdgeModel.load("path/to/decisionnode-edge", backend="llamacpp")` uses it with no build step. To make it yourself (8-bit keeps the answers; 4-bit does not, so the tool does not offer it):
+
+```bash
+git clone https://github.com/ggml-org/llama.cpp
+cmake -S llama.cpp -B llama.cpp/build -DCMAKE_BUILD_TYPE=Release        # add -DGGML_CUDA=ON on an NVIDIA GPU or a Jetson
+cmake --build llama.cpp/build --target llama-server -j
+pip install -r llama.cpp/requirements/requirements-convert_hf_to_gguf.txt
+python -m decisionnode_edge.build gguf path/to/decisionnode-edge --out path/to/edge-gguf --llama-cpp llama.cpp
+```
+
+The conversion takes seconds and writes one file: 2.0 GB for Edge, 0.8 GB for Edge Mini. The same file serves a CPU, an NVIDIA GPU, a Jetson and a Mac; llama.cpp itself is built for each.
+
+The runtime then starts `llama-server` on 127.0.0.1 itself (from your `PATH`, or `options={"server": "path/to/llama-server"}`) and stops it with the model. To run the server yourself, start it with the embedding endpoint and the last-position pooling, with a batch as large as the context:
+
+```bash
+llama-server -m edge-gguf/model-q8_0.gguf --embeddings --pooling last -c 16384 -b 16384 -ub 16384 -ngl 999 --port 8080
+```
+
+and pass `options={"url": "http://127.0.0.1:8080"}`. The runtime sends token ids, so llama.cpp's own tokenizer is never used.
+
+### Backend options
+
+Options can be flat (`{"threads": 16}`) or under a backend's name (`{"openvino": {"threads": 16}}`), so one set of options can serve whichever backend is taken.
+
+| Backend | Option | Default |
+|---|---|---|
+| `vllm` | any vLLM engine argument, such as `gpu_memory_utilization` or `enforce_eager` | a 2 GiB KV cache; the share of the GPU that covers the weights, that cache and 4 GiB for activations |
+| `openvino` | `threads` | OpenVINO's choice |
+| `openvino` | `precision`: `"bf16"` or `"f32"` | `"bf16"` (OpenVINO falls back to f32 on CPUs without bfloat16) |
+| `openvino` | `config`: other OpenVINO CPU properties | |
+| `llamacpp` | `url`: a running `llama-server` | start one |
+| `llamacpp` | `server`, `gguf`: the program and the model file | `llama-server` on `PATH`, the build's file |
+| `llamacpp` | `gpu_layers`, `threads` | 999 (all layers on the GPU when there is one), llama.cpp's choice |
+| `llamacpp` | `context`: the longest prompt llama.cpp takes | 16,384 |
+
+vLLM runs its engine inside your process and uses the first visible GPU (choose it with `CUDA_VISIBLE_DEVICES`). Its first load takes about half a minute while it compiles. MLX uses the Apple GPU.
+
+### Pictures
+
+Send pictures beside the state, up to 16, each with an `id`:
+
+```python
+{"images": [{"id": "receipt", "data": "<base64>", "media_type": "image/jpeg"}]}   # as with the hosted API
+{"images": [{"id": "receipt", "path": "/data/receipt.jpg"}]}                       # a local file, Edge only
+```
+
+A picture is turned upright from its EXIF orientation, converted to RGB and scaled so its longest side is at most 2,048 pixels. Every question sees every picture. The prompt names a picture `Picture <n> (<id>)`, so instructions can refer to "Picture 2" or to "the receipt".
+
+## The request
+
+The request and the answers follow the DecisionNode API's `/v1/decide`:
+
+| Field | Notes |
+|---|---|
+| `state` | a string (used as it is), an object or an array (compact JSON, key order kept); missing, null and `""` are the empty state |
+| `questions` | id to question, at least one; the id is never shown to the model |
+| `images` | optional, see above |
+| `model` | ignored |
+
+| Question type | Fields |
+|---|---|
+| `choice` | `instructions` (optional), `criteria`: {option: description}, 1 to 255 options; a description may be a string, an object, an array or null |
+| `score` | `instructions` (optional), `criteria`: [level, ...] low to high, 1 to 10 levels |
+| `truth` | `instructions` and/or `criteria`: {"true": ..., "false": ...} |
+| `number` | `instructions`, `min`, `max`, `step` (defaults 0, 255, 1), up to 4,096 values; more than 256 values are answered in pages of 256 and composed |
+
+Answers are rounded as the API rounds them: probabilities to 2 decimals (3 for number questions), with the largest entries moved by 0.01 until a list sums to 1.
+
+A request the model cannot answer raises `decisionnode_edge.RequestError`, whose `status` is the HTTP status the hosted API gives the same fault (400 or 422). The runtime checks what the model needs; the hosted API's error bodies are not reproduced word for word. Requests longer than the model's limit (`limits.max_input_tokens` in `decisionnode_edge.json`, 16,384 tokens) are refused, never cut.
+
+Not on Edge: videos, points and boxes, boosters, image URLs and uploads, sessions and batch jobs. These are features of the hosted API.
+
+## How it works
+
+Each question becomes one prompt: the state block (pictures first, then the state text), then the question block (the type, the instructions, then one line per option, each led by an answer code token), then the word `Answer` and an answer marker. The codes of a question are drawn from a hash of the question alone, so the same question always gets the same codes. The model reads the prompt in one forward pass, and the last hidden state is scored against the rows of `readout.safetensors` that belong to the question's codes (or to its yes and no tokens). `calibration.json` turns those scores into probabilities. The model never generates text.
+
+The code is a few short modules: `template.py` (the prompt and the codes), `request.py` (reading a request), `answers.py` (calibration and the answer shapes), `backends.py` (what reads the backbone: PyTorch, vLLM, OpenVINO, MLX or llama.cpp), `build.py` (the OpenVINO and GGUF builds), `server.py` (the HTTP server), with `model.py` tying them to the weights.
+
+## License
+
+The runtime code, like the model weights, is licensed under the GNU Affero General Public License, version 3 only (SPDX: `AGPL-3.0-only`). For use in closed products and services, Bynn Intelligence Inc. offers a commercial license: licensing@bynn.com.

runtime/examples/request.json

@@ -0,0 +1,32 @@
+{
+  "state": {
+    "ticket": "Hi, I was charged twice for my March invoice and nobody has replied to my two emails since Monday. Please refund the second charge.",
+    "customer": {"plan": "business", "since": "2023-05-14"}
+  },
+  "questions": {
+    "route": {
+      "type": "choice",
+      "instructions": "Which team should handle `ticket`?",
+      "criteria": {
+        "billing": "charges, invoices, refunds",
+        "bug": "something in the product is broken",
+        "account": "login, access, settings"
+      }
+    },
+    "urgency": {
+      "type": "score",
+      "instructions": "How urgent is `ticket`?",
+      "criteria": ["routine", "today", "urgent", "critical"]
+    },
+    "refund": {
+      "type": "truth",
+      "instructions": "Does the customer ask for a refund?"
+    },
+    "emails": {
+      "type": "number",
+      "instructions": "How many emails has the customer sent before this one?",
+      "min": 0,
+      "max": 10
+    }
+  }
+}

runtime/pyproject.toml

@@ -0,0 +1,29 @@
+[build-system]
+requires = ["setuptools>=77"]  # a PEP 639 license expression
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "decisionnode-edge"
+version = "0.9.1"
+description = "Run DecisionNode Edge locally: typed, calibrated decisions in the shape of the DecisionNode API."
+readme = "README.md"
+requires-python = ">=3.10"
+license = "AGPL-3.0-only"  # and a commercial license from Bynn Intelligence Inc. (README, License)
+license-files = ["LICENSE"]
+authors = [{ name = "Bynn Intelligence Inc.", email = "licensing@bynn.com" }]
+dependencies = [
+    "torch>=2.6",
+    "transformers>=5.18",
+    "safetensors>=0.4",
+]
+
+[project.optional-dependencies]
+# pictures: Pillow decodes them, torchvision runs the backbone's fast image processor
+images = ["pillow>=10", "torchvision>=0.21"]
+# faster backends (src/decisionnode_edge/backends.py), each optional; llama.cpp is a program of its own, not a Python package
+vllm = ["vllm>=0.32; sys_platform == 'linux'"]
+openvino = ["openvino>=2026.4", "onnx>=1.23", "onnxscript>=0.7"]  # onnx and onnxscript for the build only
+mlx = ["mlx>=0.32; sys_platform == 'darwin' and platform_machine == 'arm64'", "mlx-lm>=0.32.2; sys_platform == 'darwin' and platform_machine == 'arm64'"]
+
+[tool.setuptools.packages.find]
+where = ["src"]

runtime/src/decisionnode_edge/__init__.py

@@ -0,0 +1,13 @@
+"""DecisionNode Edge: typed, calibrated decisions from a model that runs on your own hardware.
+
+    from decisionnode_edge import EdgeModel
+
+    model = EdgeModel.load("path/to/decisionnode-edge")
+    model.decide({"state": "...", "questions": {"escalate": {"type": "truth", "instructions": "Escalate now?"}}})
+"""
+
+from .model import EdgeModel
+from .request import RequestError
+
+__all__ = ["EdgeModel", "RequestError"]
+__version__ = "0.9.1"

runtime/src/decisionnode_edge/__main__.py

@@ -0,0 +1,42 @@
+"""Answer one request from the command line, or serve the model over HTTP (server.py).
+
+    python -m decisionnode_edge path/to/decisionnode-edge request.json      # or "-" to read the request from stdin
+    python -m decisionnode_edge serve path/to/decisionnode-edge --port 8000
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+
+from . import EdgeModel, RequestError
+
+
+def main(argv: list[str] | None = None) -> int:
+    argv = sys.argv[1:] if argv is None else argv
+    if argv[:1] == ["serve"]:
+        from .server import main as serve
+
+        return serve(argv[1:])
+    parser = argparse.ArgumentParser(prog="python -m decisionnode_edge", description="Answer a /v1/decide request with a local DecisionNode Edge model.")
+    parser.add_argument("model", help="the model directory")
+    parser.add_argument("request", help="a JSON file with the request body, or - for stdin")
+    parser.add_argument("--device", default=None, help="cuda or cpu (default: cuda when present)")
+    parser.add_argument("--dtype", default=None, help="bfloat16 or float32 (default: bfloat16 on cuda, float32 on cpu)")
+    parser.add_argument("--backend", default=None, help="auto, torch, vllm, openvino, mlx or llamacpp (default: auto, the fastest installed)")
+    parser.add_argument("--artifacts", default=None, help="the directory python -m decisionnode_edge.build wrote (openvino, llamacpp)")
+    args = parser.parse_args(argv)
+    body = json.load(sys.stdin if args.request == "-" else open(args.request, encoding="utf-8"))
+    model = EdgeModel.load(args.model, device=args.device, dtype=args.dtype, backend=args.backend, artifacts=args.artifacts)
+    try:
+        response = model.decide(body)
+    except RequestError as error:
+        print(json.dumps({"error": str(error), "status": error.status}), file=sys.stderr)
+        return 1
+    print(json.dumps(response, ensure_ascii=False, indent=2))
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

runtime/src/decisionnode_edge/answers.py

@@ -0,0 +1,182 @@
+"""Calibration and the answer objects of the hosted API.
+
+Calibration (calibration.json):
+
+    choice, score   p = softmax(z / T(K)),  T(K) = t0 + t1 * ln K, K clamped to the fitted range
+    truth           p_yes = sigmoid((z_yes - z_no) / T + b)
+
+A parameter set exists per input kind ("text", "image"); a missing one is the identity.
+
+The answers, rounded as the hosted API rounds them:
+
+    choice  {"type", "choice", "confidence", "probabilities": {option: p}}      options in alphabetical order
+    score   {"type", "score", "confidence", "legend": {"0": level}, "probabilities": {"0": p}}
+    truth   {"type", "truth"}
+    number  {"type", "number", "expected", "confidence", "probabilities": {value: p}}
+
+`choice`, `score`, `number` and the confidences come from the unrounded probabilities. A probability list is rounded
+to 2 decimals entry by entry, then its largest entries move by 0.01 until it sums to 1 (`round_probabilities`).
+"""
+
+from __future__ import annotations
+
+import math
+from decimal import Decimal, localcontext
+from typing import Sequence
+
+T_MIN, T_MAX = 0.05, 100.0
+UNIT = 0.01
+
+
+# ---------------------------------------------------------------------------
+# Calibration
+# ---------------------------------------------------------------------------
+
+
+def _temperature(params: dict, k: int) -> float:
+    if params.get("k_min") is not None:
+        k = min(max(k, params["k_min"]), params["k_max"])
+    t = float(params.get("t0", 1.0)) + float(params.get("t1", 0.0)) * math.log(k)
+    return min(T_MAX, max(T_MIN, t))
+
+
+def calibrated(logits: Sequence[float], kind: str, calibration: dict, has_images: bool) -> list[float]:
+    """Calibrated class probabilities from a question's code logits (class order: options or levels as given,
+    [yes, no] for truth)."""
+    params = (calibration.get("image" if has_images else "text") or {}).get("choice" if kind == "number" else kind) or {}
+    z = [float(x) for x in logits]
+    if kind == "truth":
+        x = (z[0] - z[1]) / float(params.get("temperature", 1.0)) + float(params.get("bias", 0.0))
+        p = 1.0 / (1.0 + math.exp(-x)) if x >= 0 else math.exp(x) / (1.0 + math.exp(x))
+        return [p, 1.0 - p]
+    if len(z) == 1:
+        return [1.0]
+    t = _temperature(params, len(z))
+    scaled = [x / t for x in z]
+    top = max(scaled)
+    log_total = math.log(sum(math.exp(x - top) for x in scaled))
+    return [math.exp(x - top - log_total) for x in scaled]
+
+
+# ---------------------------------------------------------------------------
+# Rounding
+# ---------------------------------------------------------------------------
+
+
+def _units(x: float) -> int:
+    """x in hundredths, halves up (the epsilon absorbs binary error: 0.805 rounds to 0.81)."""
+    return int(math.floor(x * 100.0 + 0.5 + 1e-9))
+
+
+def round2(x: float) -> float:
+    value = _units(x) / 100.0
+    return 0.0 if value == 0 else value
+
+
+def round_probabilities(probs: Sequence[float]) -> list[float]:
+    """Round to 2 decimals, then move the |n| largest entries by 0.01, n = int((1 - fsum(rounded)) / 0.01).
+    A moved entry is not rounded again (0.81 + 0.01 is 0.8200000000000001), as the hosted API returns it."""
+    p = [float(x) for x in probs]
+    units = [_units(x) for x in p]
+    rounded = [u / 100.0 for u in units]
+    n = int((1.0 - math.fsum(rounded)) / UNIT)
+    if n:
+        step = 1 if n > 0 else -1
+        for i in sorted(range(len(p)), key=lambda i: (-p[i], i))[: abs(n)]:
+            rounded[i] = rounded[i] + UNIT if step > 0 else rounded[i] - UNIT
+    return [0.0 if x == 0 else x for x in rounded]
+
+
+def _argmax_first(probs: Sequence[float]) -> int:
+    best = 0
+    for i, p in enumerate(probs):
+        if p > probs[best]:
+            best = i
+    return best
+
+
+# ---------------------------------------------------------------------------
+# Answers
+# ---------------------------------------------------------------------------
+
+
+def choice_answer(keys: Sequence[str], probs: Sequence[float]) -> dict:
+    best = _argmax_first(probs)
+    k = len(probs)
+    confidence = 1.0 if k == 1 else min(1.0, max(0.0, (k * probs[best] - 1.0) / (k - 1.0)))
+    shown = round_probabilities(probs)
+    return {"type": "choice", "choice": keys[best], "confidence": round2(confidence), "probabilities": dict(sorted(zip(keys, shown)))}
+
+
+def score_answer(levels: Sequence, probs: Sequence[float]) -> dict:
+    k = len(probs)
+    if k == 1:
+        confidence = 1.0
+    else:
+        mode = _argmax_first(probs)
+        spread = sum(p * abs(i - mode) for i, p in enumerate(probs))
+        confidence = min(1.0, max(0.0, 1.0 - spread / ((k * k // 4) / k)))
+    return {
+        "type": "score",
+        "score": round2(sum(i * p for i, p in enumerate(probs))),
+        "confidence": round2(confidence),
+        "legend": {str(i): level for i, level in enumerate(levels)},
+        "probabilities": {str(i): x for i, x in enumerate(round_probabilities(probs))},
+    }
+
+
+def truth_answer(probs: Sequence[float]) -> dict:
+    return {"type": "truth", "truth": round2(probs[0])}
+
+
+def _expected_text(value: float, decimals: int) -> str:
+    """Halves up to `decimals` (at most 15 significant digits), with a decimal point, never -0.0."""
+    with localcontext() as ctx:
+        ctx.prec = 200
+        exact = Decimal(value)
+        whole = abs(int(exact))
+        decimals = min(decimals, 15 - (len(str(whole)) if whole else 0))
+        units = (exact * Decimal(10) ** decimals + Decimal("0.5") + Decimal("1e-9")).to_integral_value(rounding="ROUND_FLOOR")
+        text = format(units * Decimal(10) ** -decimals, "f")
+    if "." in text:
+        text = text.rstrip("0")
+        text = text + "0" if text.endswith(".") else text
+    else:
+        text += ".0"
+    return "0.0" if text == "-0.0" else text
+
+
+def _number(text: str) -> int | float:
+    return float(text) if "." in text else int(text)
+
+
+def number_answer(grid, probs: Sequence[float]) -> dict:
+    """The most probable value, the probability-weighted mean (`expected`), the probability of the value
+    (`confidence`) and every value above 0.001 to 3 decimals (rounded entries that sum above 1 lose 0.001 each,
+    the largest first, the answer's own last)."""
+    best = _argmax_first(probs)
+    texts = grid.texts()
+    expected = math.fsum(float(text) * p for text, p in zip(texts, probs))
+    listed = [i for i in range(grid.n) if probs[i] > 0.001 or i == best]
+    millis = {i: int(math.floor(probs[i] * 1000.0 + 0.5 + 1e-9)) for i in listed}
+    excess = sum(millis.values()) - 1000
+    order = sorted((i for i in listed if i != best), key=lambda i: (-probs[i], i))
+    while excess > 0:
+        lowered = False
+        for i in order:
+            if excess <= 0:
+                break
+            if millis[i] > 0:
+                millis[i] -= 1
+                excess -= 1
+                lowered = True
+        if not lowered:
+            millis[best] -= 1
+            excess -= 1
+    return {
+        "type": "number",
+        "number": _number(texts[best]),
+        "expected": float(_expected_text(expected, max(2, grid.d))),
+        "confidence": round2(probs[best]),
+        "probabilities": {texts[i]: millis[i] / 1000.0 for i in listed},
+    }

runtime/src/decisionnode_edge/backends.py

@@ -0,0 +1,397 @@
+"""Backends: what reads the backbone. Every backend gives, for a prompt's token ids, the hidden state at the last position
+after the final norm (float32, one vector). The package's readout table and calibration go on top in model.py, so the
+answers are the package's whichever backend reads the prompt.
+
+    torch      transformers; every input, pictures included (always installed)               NVIDIA GPU, CPU, Apple (mps)
+    vllm       vLLM's pooling runner, LAST pooling with no normalization                        NVIDIA GPU
+    openvino   OpenVINO, bf16, one IR per static length (python -m decisionnode_edge.build)     x86 CPU
+    mlx        mlx-lm's Qwen3.5 text model, reading the model directory as it is                Apple silicon
+    llamacpp   llama.cpp's llama-server (--embeddings --pooling last) on a GGUF Q8_0 file      any machine llama.cpp builds for
+
+The fast backends read text. A prompt one of them cannot take (pictures; a prompt longer than an OpenVINO build's largest
+length or a llama.cpp context) is read by torch, loaded on first use.
+
+Choosing: `backend` (EdgeModel.load) or DECISIONNODE_EDGE_BACKEND; "auto" (the default) takes the first that can run here,
+in the order of `ORDER` for the device class. A backend that needs built files (openvino, llamacpp) runs only with the
+directory the build tool wrote (`artifacts`, or DECISIONNODE_EDGE_ARTIFACTS). Options are flat, or under a backend's name
+({"openvino": {"threads": 16}}) so one set can serve whichever backend auto takes.
+"""
+
+from __future__ import annotations
+
+import dataclasses
+import hashlib
+import importlib.util
+import json
+import logging
+import os
+import platform
+import shutil
+import socket
+import subprocess
+import sys
+import tempfile
+import time
+import urllib.error
+import urllib.request
+import weakref
+from typing import Any, Sequence
+
+NAMES = ("torch", "vllm", "openvino", "mlx", "llamacpp")
+# per device class, fastest first (the runtime README's table)
+ORDER = {
+    "cuda": ("vllm", "llamacpp", "torch"),
+    "apple": ("mlx", "llamacpp", "torch"),
+    "cpu": ("openvino", "llamacpp", "torch"),
+}
+ENV_BACKEND = "DECISIONNODE_EDGE_BACKEND"
+ENV_ARTIFACTS = "DECISIONNODE_EDGE_ARTIFACTS"
+ENV_OPTIONS = "DECISIONNODE_EDGE_OPTIONS"
+BUILD_FILE = "decisionnode_edge_build.json"
+BUILD_FORMAT = "decisionnode-edge-build-1"
+# what a backend that reads the model directory needs from it: never readout.safetensors (vLLM loads every .safetensors file)
+MODEL_FILES = ("config.json", "model.safetensors", "tokenizer.json", "tokenizer_config.json", "preprocessor_config.json", "video_preprocessor_config.json")
+
+log = logging.getLogger("decisionnode_edge")
+
+
+# ---------------------------------------------------------------------------
+# Choosing
+# ---------------------------------------------------------------------------
+
+
+def apple_silicon() -> bool:
+    return sys.platform == "darwin" and platform.machine() == "arm64"
+
+
+def linux() -> bool:
+    return sys.platform.startswith("linux")
+
+
+def device_class(device: str | None) -> str:
+    """"cuda", "apple" or "cpu": the device given, else CUDA when present, else Apple silicon, else the CPU."""
+    if device is not None:
+        device = str(device)
+        return "cuda" if device.startswith("cuda") else "apple" if device.startswith("mps") else "cpu"
+    import torch
+
+    if torch.cuda.is_available():
+        return "cuda"
+    return "apple" if apple_silicon() else "cpu"
+
+
+def env_options() -> dict:
+    raw = os.environ.get(ENV_OPTIONS)
+    if not raw:
+        return {}
+    value = json.loads(raw)
+    if not isinstance(value, dict):
+        raise ValueError(f"{ENV_OPTIONS} must be a JSON object")
+    return value
+
+
+def read_build(artifacts: str | None) -> dict | None:
+    """The build tool's record in `artifacts` (decisionnode_edge_build.json), or None."""
+    if not artifacts or not os.path.isfile(os.path.join(artifacts, BUILD_FILE)):
+        return None
+    with open(os.path.join(artifacts, BUILD_FILE), encoding="utf-8") as f:
+        build = json.load(f)
+    if build.get("format") != BUILD_FORMAT:
+        raise ValueError(f"{artifacts}: not a build of this runtime ({build.get('format')!r})")
+    return build
+
+
+def own_options(name: str, options: dict) -> dict:
+    """The options for one backend: the flat ones, then the ones under its name."""
+    return {**{k: v for k, v in options.items() if k not in NAMES}, **(options.get(name) or {})}
+
+
+def _installed(*modules: str) -> bool:
+    return all(importlib.util.find_spec(m) is not None for m in modules)
+
+
+def unavailable(name: str, device: str | None, artifacts: str | None, options: dict) -> str | None:
+    """Why `name` cannot run here, or None when it can."""
+    if name == "torch":
+        return None
+    if name == "vllm":
+        if not linux() or not _installed("vllm"):
+            return "vLLM is not installed (pip install 'decisionnode-edge[vllm]', Linux with an NVIDIA GPU)"
+        return None if device_class(device) == "cuda" else "vLLM needs an NVIDIA GPU"
+    if name == "mlx":
+        if not apple_silicon():
+            return "MLX needs Apple silicon"
+        return None if _installed("mlx", "mlx_lm") else "MLX is not installed (pip install 'decisionnode-edge[mlx]')"
+    build = read_build(artifacts)
+    if name == "openvino":
+        if not _installed("openvino"):
+            return "OpenVINO is not installed (pip install 'decisionnode-edge[openvino]')"
+        if not build or build.get("backend") != "openvino":
+            return "OpenVINO needs a build: python -m decisionnode_edge.build openvino <model> --out <dir>, then artifacts=<dir>"
+        return None
+    if name == "llamacpp":
+        options = own_options(name, options)
+        if options.get("url"):
+            return None
+        if not build or build.get("backend") != "llamacpp":
+            return "llama.cpp needs a GGUF build (python -m decisionnode_edge.build gguf) as artifacts, or options={'url': <a running llama-server>}"
+        if not (options.get("server") or shutil.which("llama-server")):
+            return "llama-server is not on PATH: give options={'server': <path to llama-server>} or {'url': <a running llama-server>}"
+        return None
+    raise ValueError(f"unknown backend {name!r}: one of {', '.join(NAMES)} or auto")
+
+
+def choose(requested: str | None, device: str | None, artifacts: str | None, options: dict) -> str:
+    """The backend to load: the one asked for (argument, else DECISIONNODE_EDGE_BACKEND), or for "auto" the first of the
+    device class's order that can run here."""
+    name = (requested or os.environ.get(ENV_BACKEND) or "auto").strip().lower()
+    if name != "auto":
+        reason = unavailable(name, device, artifacts, options)
+        if reason:
+            raise RuntimeError(f"backend {name!r}: {reason}")
+        return name
+    for candidate in ORDER[device_class(device)]:
+        reason = unavailable(candidate, device, artifacts, options)
+        if reason is None:
+            return candidate
+        log.info("auto: not %s: %s", candidate, reason)
+    return "torch"
+
+
+def load(name: str, path: str, config: dict, artifacts: str | None, options: dict, device: str | None):
+    """The backend object for a name other than torch (model.py loads torch itself)."""
+    max_tokens = int(config["limits"]["max_input_tokens"])
+    options = own_options(name, options)
+    if name == "vllm":
+        return VLLM(path, max_tokens, options, device)
+    if name == "mlx":
+        return MLX(path)
+    build = read_build(artifacts)
+    if build is not None and build.get("model_fingerprint") != fingerprint(path):
+        raise ValueError(f"{artifacts} was built from another model than {path}")
+    if name == "openvino":
+        return OpenVINO(artifacts, build, options)
+    if name == "llamacpp":
+        return LlamaCpp(artifacts, build, max_tokens, options)
+    raise ValueError(f"unknown backend {name!r}")
+
+
+def fingerprint(path: str) -> str:
+    """A quick identity of a model directory's weights, kept in every build: the file's size, its safetensors header and
+    1 MiB every 256 MiB plus the last MiB (hashing all of it would add seconds to every load)."""
+    weights = os.path.join(path, "model.safetensors")
+    size = os.path.getsize(weights)
+    h = hashlib.sha256(str(size).encode())
+    with open(weights, "rb") as f:
+        n = int.from_bytes(f.read(8), "little")
+        h.update(f.read(n))
+        for offset in list(range(8 + n, size, 256 << 20)) + [max(0, size - (1 << 20))]:
+            f.seek(offset)
+            h.update(f.read(1 << 20))
+    return h.hexdigest()
+
+
+def link_model_files(path: str, target: str) -> None:
+    """Links in `target` to the model directory's backbone files (MODEL_FILES), never its readout."""
+    for name in MODEL_FILES:
+        src = os.path.join(os.path.abspath(path), name)
+        if os.path.exists(src):
+            os.symlink(src, os.path.join(target, name))
+
+
+# ---------------------------------------------------------------------------
+# Backends: hidden(ids) gives the float32 hidden state of the last position; accepts(n) says whether a prompt of n
+# tokens is theirs (torch reads the others)
+# ---------------------------------------------------------------------------
+
+
+class VLLM:
+    """vLLM's pooling runner (convert "embed", LAST pooling, use_activation False: the raw hidden state after the final norm)
+    on a temporary directory of links to the model files. Text only: the multimodal limits are 0. Its engine runs in this
+    process (VLLM_ENABLE_V1_MULTIPROCESSING 0 unless set): as fast at batch 1, and a child process would need the caller's
+    script to be importable (vLLM starts it with spawn once CUDA is up)."""
+
+    name = "vllm"
+    KV_CACHE_BYTES = 2 << 30  # several prompts of the full 16,384 tokens
+    HEADROOM_BYTES = 4 << 30  # activations at 16,384 tokens, CUDA graphs, the CUDA context
+
+    def __init__(self, path: str, max_tokens: int, options: dict, device: str | None):
+        if device is not None and str(device) not in ("cuda", "cuda:0"):
+            raise ValueError("vLLM runs on the first visible GPU: choose the card with CUDA_VISIBLE_DEVICES")
+        os.environ.setdefault("VLLM_ENABLE_V1_MULTIPROCESSING", "0")
+        from vllm import LLM
+        from vllm.config import PoolerConfig
+        from vllm.engine.arg_utils import EngineArgs
+        from vllm.platforms import current_platform
+
+        self.dir = tempfile.mkdtemp(prefix="decisionnode-edge-vllm-")
+        self._cleanup = weakref.finalize(self, shutil.rmtree, self.dir, True)
+        link_model_files(path, self.dir)
+        # the KV cache is sized, not a share of the card: the share only has to cover it, the weights and the headroom
+        total = current_platform.get_device_total_memory(0)
+        need = os.path.getsize(os.path.join(path, "model.safetensors")) + self.KV_CACHE_BYTES + self.HEADROOM_BYTES
+        args: dict[str, Any] = {
+            "model": self.dir,
+            "runner": "pooling",
+            "convert": "embed",
+            "pooler_config": PoolerConfig(pooling_type="LAST", use_activation=False),
+            "dtype": "bfloat16",
+            "max_model_len": max_tokens,
+            "kv_cache_memory_bytes": self.KV_CACHE_BYTES,
+            "gpu_memory_utilization": round(min(0.92, need / total), 3),
+            "enable_prefix_caching": False,
+            "limit_mm_per_prompt": {"image": 0, "video": 0},
+            "seed": 0,
+            "disable_log_stats": True,
+        }
+        fields = {f.name for f in dataclasses.fields(EngineArgs)}
+        args.update({k: v for k, v in options.items() if k in fields})  # any vLLM engine argument, as given
+        if set(options) - fields:
+            log.warning("options vLLM does not know, not used: %s", sorted(set(options) - fields))
+        self.llm = LLM(**args)
+
+    def accepts(self, n: int) -> bool:
+        return True
+
+    def hidden(self, ids: Sequence[int]):
+        import torch
+
+        out = self.llm.embed([{"prompt_token_ids": list(ids)}], use_tqdm=False)[0]
+        return torch.tensor(out.outputs.embedding, dtype=torch.float32)
+
+
+class OpenVINO:
+    """OpenVINO on the CPU, one compiled model per static length of the build. A prompt is padded on the right to the
+    smallest length that holds it and the row of its last token is read: the backbone is causal (attention, the short
+    convolution and the delta rule), so the padding never reaches the earlier rows. The weights are float32 in the IR (the
+    bf16 weights, exactly) and run with OpenVINO's bf16 inference precision."""
+
+    name = "openvino"
+
+    def __init__(self, artifacts: str, build: dict, options: dict):
+        import openvino as ov
+
+        config = {"PERFORMANCE_HINT": "LATENCY", "INFERENCE_PRECISION_HINT": options.get("precision", "bf16")}
+        if options.get("threads"):
+            config["INFERENCE_NUM_THREADS"] = int(options["threads"])
+        config.update(options.get("config") or {})
+        core = ov.Core()
+        self.pad = int(build["pad_token_id"])
+        self.runners = {}
+        for length, file in sorted(((int(L), f) for L, f in build["files"].items())):
+            compiled = core.compile_model(os.path.join(artifacts, file), "CPU", config)
+            self.runners[length] = (compiled.create_infer_request(), compiled.output(0))
+        self.lengths = sorted(self.runners)
+
+    def accepts(self, n: int) -> bool:
+        return n <= self.lengths[-1]
+
+    def hidden(self, ids: Sequence[int]):
+        import numpy as np
+        import torch
+
+        n = len(ids)
+        length = next(L for L in self.lengths if L >= n)
+        request, output = self.runners[length]
+        x = np.full((1, length), self.pad, dtype=np.int64)
+        x[0, :n] = ids
+        out = request.infer({0: x})[output]
+        return torch.from_numpy(np.asarray(out[0, n - 1], dtype=np.float32))
+
+
+class MLX:
+    """mlx-lm's Qwen3.5 model on the model directory (it reads model*.safetensors, leaves the picture encoder out and never
+    touches readout.safetensors); the text model's output is the hidden state after the final norm."""
+
+    name = "mlx"
+
+    def __init__(self, path: str):
+        from pathlib import Path
+
+        import mlx.core as mx
+        from mlx_lm.utils import load_model
+
+        self.mx = mx
+        model, _ = load_model(Path(path))
+        self.text = model.language_model.model
+
+    def accepts(self, n: int) -> bool:
+        return True
+
+    def hidden(self, ids: Sequence[int]):
+        import numpy as np
+        import torch
+
+        h = self.text(self.mx.array([list(ids)]))[0, -1].astype(self.mx.float32)
+        self.mx.eval(h)
+        return torch.from_numpy(np.array(h))
+
+
+class LlamaCpp:
+    """llama.cpp's llama-server answering /embedding with the last position's hidden state after the final norm,
+    unnormalized (embd_normalize -1). The prompt goes as token ids, so llama.cpp's tokenizer is never used. Either a server
+    you run (options["url"]) or one started here on the build's GGUF, on 127.0.0.1, stopped with the model."""
+
+    name = "llamacpp"
+
+    def __init__(self, artifacts: str | None, build: dict | None, max_tokens: int, options: dict):
+        self.context = int(options.get("context", max_tokens))
+        self.process = None
+        url = options.get("url")
+        if not url:
+            gguf = options.get("gguf") or os.path.join(artifacts, build["files"]["gguf"])
+            server = options.get("server") or shutil.which("llama-server")
+            with socket.socket() as s:
+                s.bind(("127.0.0.1", 0))
+                port = s.getsockname()[1]
+            # the embedding endpoint needs the whole prompt in one micro-batch: batch and micro-batch are the context
+            command = [server, "-m", gguf, "--embeddings", "--pooling", "last", "-c", str(self.context), "-b", str(self.context), "-ub", str(self.context),
+                       "-ngl", str(options.get("gpu_layers", 999)), "--parallel", "1", "--host", "127.0.0.1", "--port", str(port)]  # fmt: skip
+            if options.get("threads"):
+                command += ["-t", str(int(options["threads"]))]
+            self.output = tempfile.TemporaryFile()
+            self.process = subprocess.Popen(command, stdout=self.output, stderr=subprocess.STDOUT, stdin=subprocess.DEVNULL)
+            self._stop = weakref.finalize(self, _stop, self.process)
+            url = f"http://127.0.0.1:{port}"
+            self._wait(url, float(options.get("start_timeout", 600)))
+        self.url = url.rstrip("/") + "/embedding"
+
+    def _wait(self, url: str, timeout: float) -> None:
+        deadline = time.monotonic() + timeout
+        while time.monotonic() < deadline:
+            if self.process.poll() is not None:
+                self.output.seek(0)
+                tail = self.output.read().decode("utf-8", "replace")[-2000:]
+                raise RuntimeError(f"llama-server stopped (exit {self.process.returncode}):\n{tail}")
+            try:
+                with urllib.request.urlopen(url + "/health", timeout=5) as r:
+                    if r.status == 200:
+                        return
+            except (urllib.error.URLError, OSError):
+                pass
+            time.sleep(0.5)
+        raise RuntimeError(f"llama-server did not answer within {timeout:.0f} s")
+
+    def accepts(self, n: int) -> bool:
+        return n <= self.context
+
+    def hidden(self, ids: Sequence[int]):
+        import torch
+
+        body = json.dumps({"content": list(ids), "embd_normalize": -1, "cache_prompt": False}).encode()
+        request = urllib.request.Request(self.url, data=body, headers={"Content-Type": "application/json"})
+        with urllib.request.urlopen(request, timeout=600) as r:
+            out = json.loads(r.read())
+        vector = (out[0] if isinstance(out, list) else out)["embedding"]
+        if vector and isinstance(vector[0], list):  # one row per position: the last
+            vector = vector[-1]
+        return torch.tensor(vector, dtype=torch.float32)
+
+
+def _stop(process: subprocess.Popen) -> None:
+    if process.poll() is None:
+        process.terminate()
+        try:
+            process.wait(10)
+        except subprocess.TimeoutExpired:
+            process.kill()

runtime/src/decisionnode_edge/build.py

@@ -0,0 +1,169 @@
+"""Build the files a faster backend loads, from a model directory (only read) into a new directory.
+
+    python -m decisionnode_edge.build openvino path/to/model --out path/to/model-openvino [--lengths 512,2048]
+    python -m decisionnode_edge.build gguf path/to/model --out path/to/model-gguf --llama-cpp path/to/llama.cpp
+
+openvino  The backbone's text path (the token embedding, the layers, the final norm: input_ids [1, L] to the hidden
+          states [1, L, hidden]) exported with torch.onnx's dynamo exporter in float32 at each static length, then
+          saved as OpenVINO IR with float32 weights (the bf16 weights, exactly); the runtime runs it in bf16. One IR per
+          length, since the export unrolls the delta rule over the length: each takes 15 to 20 minutes, and lengths
+          above a few thousand tokens take hours. Then each IR is run once against torch. Needs the `openvino` extra
+          (openvino, onnx, onnxscript); `--device cuda` (the default when a GPU is present) keeps the weights on the card.
+gguf      llama.cpp's converter (convert_hf_to_gguf.py --no-mtp) on links to the backbone files, Q8_0 by default: 8-bit
+          keeps the answers, 4-bit does not. Needs a llama.cpp checkout and its converter's Python requirements, in the
+          environment of `--python` (default: this one).
+
+Both write decisionnode_edge_build.json (the backend, the files, the model's fingerprint, versions, times), which
+EdgeModel.load reads from `artifacts`.
+"""
+
+from __future__ import annotations
+
+import argparse
+import importlib.metadata
+import json
+import os
+import shutil
+import subprocess
+import sys
+import time
+
+from .backends import BUILD_FILE, BUILD_FORMAT, fingerprint, link_model_files
+
+DEFAULT_LENGTHS = (512, 2048)
+
+
+def versions(*names: str) -> dict:
+    out = {}
+    for name in names:
+        try:
+            out[name] = importlib.metadata.version(name)
+        except importlib.metadata.PackageNotFoundError:
+            out[name] = None
+    return out
+
+
+def new_directory(out: str) -> None:
+    if os.path.exists(out):
+        raise SystemExit(f"{out} exists: the build writes a new directory")
+    os.makedirs(out)
+
+
+def write_record(out: str, record: dict) -> None:
+    with open(os.path.join(out, BUILD_FILE), "w", encoding="utf-8") as f:
+        json.dump(record, f, indent=2)
+        f.write("\n")
+
+
+def model_name(model: str) -> str:
+    with open(os.path.join(model, "decisionnode_edge.json"), encoding="utf-8") as f:
+        return json.load(f)["name"]
+
+
+def build_openvino(model: str, out: str, lengths: list[int], device: str | None) -> dict:
+    import numpy as np
+    import openvino as ov
+    import torch
+    from transformers import AutoModelForImageTextToText, AutoTokenizer
+
+    device = device or ("cuda" if torch.cuda.is_available() else "cpu")
+    record = {"format": BUILD_FORMAT, "backend": "openvino", "model": model_name(model), "model_fingerprint": fingerprint(model), "files": {},
+              "weights": "float32 (the bf16 weights, exactly); run in bf16", "export_device": device,
+              "versions": versions("torch", "transformers", "onnx", "onnxscript", "openvino"), "seconds": {}, "check": {}}  # fmt: skip
+    tokenizer = AutoTokenizer.from_pretrained(model)
+    record["pad_token_id"] = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0
+
+    class LastHidden(torch.nn.Module):
+        def __init__(self, text):
+            super().__init__()
+            self.text = text
+
+        def forward(self, input_ids):
+            return self.text(input_ids=input_ids, use_cache=False).last_hidden_state
+
+    full = AutoModelForImageTextToText.from_pretrained(model, dtype=torch.float32).to(device).eval()
+    text = LastHidden(full.model.language_model).eval()
+    generator = torch.Generator().manual_seed(0)
+    for length in lengths:
+        work = os.path.join(out, f"onnx-{length}")
+        os.makedirs(work)
+        onnx_file = os.path.join(work, "edge.onnx")
+        ids = torch.randint(10, 200000, (1, length), generator=generator).to(device)
+        t0 = time.perf_counter()
+        with torch.inference_mode():
+            torch.onnx.export(text, (ids,), onnx_file, dynamo=True, external_data=True, input_names=["input_ids"], output_names=["last_hidden_state"], optimize=True)
+        t1 = time.perf_counter()
+        ir = ov.convert_model(onnx_file)
+        name = f"openvino-{length}.xml"
+        ov.save_model(ir, os.path.join(out, name), compress_to_fp16=False)
+        del ir
+        shutil.rmtree(work)
+        record["files"][str(length)] = name
+        record["seconds"][str(length)] = {"export": round(t1 - t0, 1), "convert": round(time.perf_counter() - t1, 1)}
+        print(json.dumps({"length": length, **record["seconds"][str(length)]}), flush=True)
+
+    # each IR against torch (float32) on a prompt shorter than its length, so the padding is read past as at run time
+    core = ov.Core()
+    for length in lengths:
+        n = length - 37
+        ids = torch.randint(10, 200000, (1, n), generator=generator)
+        with torch.inference_mode():
+            want = text(ids.to(device))[0, -1].float().cpu()
+        compiled = core.compile_model(os.path.join(out, record["files"][str(length)]), "CPU", {"INFERENCE_PRECISION_HINT": "bf16"})
+        x = np.full((1, length), record["pad_token_id"], dtype=np.int64)
+        x[0, :n] = ids[0].numpy()
+        got = torch.from_numpy(np.asarray(compiled.create_infer_request().infer({0: x})[compiled.output(0)][0, n - 1], dtype=np.float32))
+        cosine = float(torch.nn.functional.cosine_similarity(got, want, dim=0))
+        record["check"][str(length)] = {"tokens": n, "cosine_to_torch_float32": round(cosine, 6), "largest_difference": round(float((got - want).abs().max()), 4)}
+        print(json.dumps({"check": length, **record["check"][str(length)]}), flush=True)
+        del compiled
+        if not torch.isfinite(got).all() or cosine < 0.99:
+            raise SystemExit(f"the IR at {length} tokens does not reproduce the backbone (cosine {cosine:.4f}): {out} is incomplete")
+    return record
+
+
+def build_gguf(model: str, out: str, llama_cpp: str, outtype: str, python: str) -> dict:
+    converter = os.path.join(llama_cpp, "convert_hf_to_gguf.py")
+    if not os.path.isfile(converter):
+        raise SystemExit(f"{converter} not found: --llama-cpp is the llama.cpp checkout")
+    source = os.path.join(out, "source")  # the converter reads links to the backbone files, never the readout
+    os.makedirs(source)
+    link_model_files(model, source)
+    name = f"model-{outtype}.gguf"
+    t0 = time.perf_counter()
+    subprocess.run([python, converter, source, "--no-mtp", "--outfile", os.path.join(out, name), "--outtype", outtype], check=True)
+    shutil.rmtree(source)
+    commit = subprocess.run(["git", "-C", llama_cpp, "rev-parse", "--short", "HEAD"], capture_output=True, text=True).stdout.strip() or None
+    return {"format": BUILD_FORMAT, "backend": "llamacpp", "model": model_name(model), "model_fingerprint": fingerprint(model), "files": {"gguf": name},
+            "type": outtype, "bytes": os.path.getsize(os.path.join(out, name)), "llama_cpp": commit, "seconds": round(time.perf_counter() - t0, 1)}  # fmt: skip
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(prog="python -m decisionnode_edge.build", description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    parser.add_argument("backend", choices=("openvino", "gguf"))
+    parser.add_argument("model", help="the model directory (only read)")
+    parser.add_argument("--out", required=True, help="the directory to write (must not exist)")
+    parser.add_argument("--lengths", default=",".join(map(str, DEFAULT_LENGTHS)), help="openvino: the static lengths in tokens (default 512,2048)")
+    parser.add_argument("--device", default=None, help="openvino: where the export runs, cuda or cpu (default: cuda when present)")
+    parser.add_argument("--llama-cpp", help="gguf: the llama.cpp checkout (its convert_hf_to_gguf.py)")
+    parser.add_argument("--type", default="q8_0", choices=("q8_0", "bf16"), help="gguf: the weight type (default q8_0)")
+    parser.add_argument("--python", default=sys.executable, help="gguf: the Python that runs llama.cpp's converter (default: this one)")
+    args = parser.parse_args(argv)
+    for name in ("config.json", "model.safetensors", "decisionnode_edge.json"):
+        if not os.path.isfile(os.path.join(args.model, name)):
+            raise SystemExit(f"{args.model} is not a DecisionNode Edge model directory (no {name})")
+    if args.backend == "gguf" and not args.llama_cpp:
+        raise SystemExit("gguf needs --llama-cpp")
+    new_directory(args.out)
+    if args.backend == "openvino":
+        lengths = sorted({int(x) for x in args.lengths.split(",") if x.strip()})
+        record = build_openvino(args.model, args.out, lengths, args.device)
+    else:
+        record = build_gguf(args.model, args.out, args.llama_cpp, args.type, args.python)
+    write_record(args.out, record)
+    print(json.dumps({k: v for k, v in record.items() if k != "versions"}), flush=True)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

runtime/src/decisionnode_edge/model.py

@@ -0,0 +1,215 @@
+"""Load a DecisionNode Edge model directory and answer requests.
+
+    from decisionnode_edge import EdgeModel
+
+    model = EdgeModel.load("path/to/decisionnode-edge")
+    response = model.decide({"state": "...", "questions": {"route": {"type": "choice", "criteria": {...}}}})
+
+The model reads the prompt once per question and never generates text. Its answer is read at the
+<|answer|> position: the last hidden state there is scored against a small readout table
+(readout.safetensors), one row per answer code, and only the rows of the question's codes are read.
+Calibration (calibration.json) turns those scores into probabilities.
+
+The backbone is read by torch (transformers) or by a faster backend (backends.py: vLLM, OpenVINO, MLX,
+llama.cpp); the readout and calibration are this module's in every case.
+"""
+
+from __future__ import annotations
+
+import base64
+import io
+import json
+import logging
+import os
+from typing import Any
+
+import torch
+from safetensors.torch import load_file
+
+from . import answers, backends, request as requests
+from .template import Template
+
+CONFIG_FILE = "decisionnode_edge.json"
+
+log = logging.getLogger("decisionnode_edge")
+
+
+def load_backbone(path: str, device: str | None, dtype: str | torch.dtype | None) -> tuple[Any, bool]:
+    """The transformers backbone on `device` (default CUDA when present, else the CPU) in `dtype` (default bfloat16 on
+    CUDA, float32 elsewhere), and whether it runs under bf16 autocast."""
+    from transformers import AutoModelForImageTextToText
+
+    device = device or ("cuda" if torch.cuda.is_available() else "cpu")
+    if dtype is None:
+        dtype = torch.bfloat16 if str(device).startswith("cuda") else torch.float32
+    dtype = getattr(torch, dtype) if isinstance(dtype, str) else dtype
+    backbone = AutoModelForImageTextToText.from_pretrained(path, dtype=dtype).to(device).eval()
+    # bf16 weights run under bf16 autocast, the reference numerics: some operations then compute in float32
+    return backbone, dtype == torch.bfloat16
+
+
+class EdgeModel:
+    def __init__(self, backbone: Any, tokenizer: Any, readout: torch.Tensor, config: dict, calibration: dict, path: str, autocast: bool,
+                 runner: Any = None, torch_options: dict | None = None):  # fmt: skip
+        self.backbone = backbone  # the transformers model; None until torch is needed when a faster backend reads the prompts
+        self.tokenizer = tokenizer
+        self.readout = readout
+        self.config = config
+        self.calibration = calibration
+        self.path = path
+        self.autocast = autocast
+        self.runner = runner  # the backend object (backends.py), or None: torch reads every prompt
+        self.torch_options = torch_options or {}  # device and dtype for torch when it is loaded on first use
+        self.template = Template(tokenizer, bytes.fromhex(config["template"]["code_seed_hex"]).decode("utf-8"))
+        if self.template.first_id != config["readout"]["first_token_id"]:
+            raise ValueError("the tokenizer and the readout table disagree on the ids of the added tokens")
+        self.limits = config["limits"]
+        self._images = None
+
+    @classmethod
+    def load(cls, path: str, device: str | None = None, dtype: str | torch.dtype | None = None, backend: str | None = None,
+             artifacts: str | None = None, options: dict | None = None) -> "EdgeModel":  # fmt: skip
+        """Load the model directory.
+
+        `backend`: "auto" (the default, or DECISIONNODE_EDGE_BACKEND), "torch", "vllm", "openvino", "mlx" or "llamacpp".
+        Auto takes the fastest that is installed and can run here (backends.ORDER). `artifacts`: the directory the build
+        tool wrote for openvino or llamacpp (or DECISIONNODE_EDGE_ARTIFACTS); for either asked for by name it defaults to
+        the model directory when that holds a build (the GGUF that ships with the model). `options`: the backend's settings (the
+        README; DECISIONNODE_EDGE_OPTIONS as JSON is read under them).
+
+        `device` defaults to CUDA when present, else the CPU, and `dtype` to bfloat16 on CUDA (the reference
+        precision) and float32 elsewhere; both are torch's, also when torch only reads the prompts a
+        faster backend cannot take."""
+        from transformers import AutoTokenizer
+
+        with open(os.path.join(path, CONFIG_FILE), encoding="utf-8") as f:
+            config = json.load(f)
+        with open(os.path.join(path, config["calibration"]), encoding="utf-8") as f:
+            calibration = json.load(f)
+        tokenizer = AutoTokenizer.from_pretrained(path)
+        table = load_file(os.path.join(path, config["readout"]["file"]))[config["readout"]["tensor"]]
+        artifacts = artifacts or os.environ.get(backends.ENV_ARTIFACTS) or None
+        if artifacts is None and (backend or os.environ.get(backends.ENV_BACKEND) or "").strip().lower() in ("openvino", "llamacpp") and backends.read_build(path):
+            artifacts = path  # a build that ships in the model folder (the GGUF), for a backend asked for by name
+        options = {**backends.env_options(), **(options or {})}
+        name = backends.choose(backend, device, artifacts, options)
+        log.info("the %s backend reads the prompts", name)
+        if name == "torch":
+            backbone, autocast = load_backbone(path, device, dtype)
+            return cls(backbone, tokenizer, table.float().to(backbone.device), config, calibration, path, autocast)
+        runner = backends.load(name, path, config, artifacts, options, device)
+        # the readout and calibration stay here, in float32 on the CPU
+        return cls(None, tokenizer, table.float(), config, calibration, path, False, runner=runner, torch_options={"device": device, "dtype": dtype})
+
+    @property
+    def device(self) -> torch.device:
+        return self.readout.device
+
+    @property
+    def backend(self) -> str:
+        """The backend that reads the prompts ("torch" also reads, on any backend, the pictures and the prompts longer than
+        the backend takes)."""
+        return self.runner.name if self.runner is not None else "torch"
+
+    # -- public -----------------------------------------------------------
+
+    def decide(self, body: dict) -> dict:
+        """Answer a `/v1/decide` request body. Returns {"model", "answers", "usage"} as the hosted API does."""
+        parsed = requests.parse(body)
+        probs, input_tokens = self._predict(parsed)
+        out = {}
+        for q in parsed.questions:
+            p = probs[q.id]
+            if q.type == "choice":
+                out[q.id] = answers.choice_answer(q.keys, p)
+            elif q.type == "score":
+                out[q.id] = answers.score_answer(q.levels, p)
+            elif q.type == "truth":
+                out[q.id] = answers.truth_answer(p)
+            else:
+                out[q.id] = answers.number_answer(q.grid, p)
+        return {"model": self.config["name"], "answers": out, "usage": {"input_tokens": input_tokens, "output_tokens": 0}}
+
+    def predict(self, body: dict) -> dict[str, list[float]]:
+        """The unrounded calibrated probabilities of every question: per option or level in request order, [yes, no]
+        for truth, per grid value for number."""
+        return self._predict(requests.parse(body))[0]
+
+    # -- internals --------------------------------------------------------
+
+    def _predict(self, parsed: requests.Request) -> tuple[dict[str, list[float]], int]:
+        images = self._load_images(parsed.images) if parsed.images else None
+        image_ids = [image["id"] for image in parsed.images]
+        out: dict[str, list[float]] = {}
+        state_tokens, total = None, 0
+        for q in parsed.questions:
+            answered = []
+            for row in q.rows:
+                encoded = self.template.encode(row, parsed.state, image_ids, images[2] if images else None)
+                if len(encoded.input_ids) > self.limits["max_input_tokens"]:
+                    raise requests.RequestError(f"question {q.id!r} needs {len(encoded.input_ids):,} tokens; the maximum context length is {self.limits['max_input_tokens']:,} tokens",
+                                                error_type="max_tokens_exceeded")  # fmt: skip
+                logits = self._logits(encoded, images)
+                answered.append(answers.calibrated(logits, row["type"], self.calibration, bool(image_ids)))
+                # the state and its pictures count once, every question's own tokens on top (as the hosted usage counts)
+                total += len(encoded.input_ids) - (encoded.state_tokens if state_tokens is not None else 0)
+                state_tokens = encoded.state_tokens
+            if q.type == "number":
+                out[q.id] = [1.0] if not q.rows else requests.compose(q.grid, answered)
+            else:
+                out[q.id] = answered[0] if answered else [1.0]
+        return out, total
+
+    @torch.inference_mode()
+    def _logits(self, encoded: Any, images: tuple | None) -> list[float]:
+        if self.runner is not None and images is None and self.runner.accepts(len(encoded.input_ids)):
+            h = self.runner.hidden(encoded.input_ids).to(self.device, torch.float32)
+        else:
+            h = self._torch_hidden(encoded, images).to(self.device)
+        scores = h @ self.readout.t()  # one score per added token, never the vocabulary
+        rows = torch.tensor(encoded.class_ids, device=self.device) - self.template.first_id
+        return scores[rows].tolist()
+
+    def _torch_hidden(self, encoded: Any, images: tuple | None) -> torch.Tensor:
+        """The hidden state at the <|answer|> position (the last token) from the transformers backbone, float32."""
+        if self.backbone is None:
+            log.warning("loading torch for the prompts the %s backend does not take (pictures, longer inputs)", self.backend)
+            self.backbone, self.autocast = load_backbone(self.path, self.torch_options.get("device"), self.torch_options.get("dtype"))
+        device = self.backbone.device
+        input_ids = torch.tensor([encoded.input_ids], device=device)
+        extra = {}
+        if images is not None:
+            extra = {
+                "pixel_values": images[0].to(device),
+                "image_grid_thw": images[1].to(device),
+                "mm_token_type_ids": (input_ids == self.template.image_pad).long(),
+            }
+        with torch.autocast(device_type=device.type, dtype=torch.bfloat16, enabled=self.autocast):
+            hidden = self.backbone.model(input_ids=input_ids, attention_mask=torch.ones_like(input_ids), use_cache=False, **extra).last_hidden_state
+        return hidden[0, -1].float()
+
+    def _load_images(self, images: list[dict]) -> tuple[torch.Tensor, torch.Tensor, dict[str, int]]:
+        """(pixel values, grid, placeholder tokens per image id) of a request's pictures: EXIF orientation applied,
+        RGB, the longest side at most max_image_side, then the backbone's own image processor."""
+        from PIL import Image, ImageOps
+        from transformers import AutoImageProcessor
+
+        if self._images is None:
+            self._images = AutoImageProcessor.from_pretrained(self.path)
+        max_side = int(self.limits["max_image_side"])
+        fitted = []
+        for image in images:
+            source = image["path"] if image.get("path") is not None else io.BytesIO(base64.b64decode(image["data"]))
+            try:
+                with Image.open(source) as im:
+                    im = ImageOps.exif_transpose(im).convert("RGB")
+                    if max(im.size) > max_side:
+                        scale = max_side / max(im.size)
+                        im = im.resize((max(1, round(im.width * scale)), max(1, round(im.height * scale))), Image.BICUBIC)
+                    fitted.append(im.copy())
+            except (OSError, ValueError) as error:
+                raise requests.RequestError(f"Image {image['id']!r} could not be read: {error}") from None
+        out = self._images(images=fitted, return_tensors="pt")
+        merge = int(getattr(self._images, "merge_size", 2))
+        counts = (out["image_grid_thw"].prod(dim=-1) // merge**2).tolist()
+        return out["pixel_values"], out["image_grid_thw"], {image["id"]: int(n) for image, n in zip(images, counts)}

runtime/src/decisionnode_edge/request.py

@@ -0,0 +1,296 @@
+"""The request of the hosted `/v1/decide` endpoint, read into the questions the model answers.
+
+    {"state": ..., "questions": {"<id>": {"type": "choice" | "score" | "truth" | "number", ...}}, "images": [...]}
+
+What a request becomes (the same rules as the hosted API):
+
+    state         a string is used as it is; an object or array becomes compact JSON with its key order
+                  kept; a missing state, null and "" are the empty state.
+    instructions  a string, or the compact JSON of an object or array; missing or null is the empty text.
+    choice        criteria {option: description}, 1 to 255 options. A description is a string, the compact
+                  JSON of an object or array, or "" for null.
+    score         criteria [level, ...], 1 to 10 levels. One level is answered without the model.
+    truth         optional criteria {"true": ..., "false": ...}; needs instructions or criteria.
+    number        min, max and step (default 0, 255, 1): the answer is a value of that grid. Up to 256 values
+                  are one internal choice over the values; up to 4,096 are answered as pages of 256 (one choice
+                  over the pages, one per page) and composed. A grid of one value is answered without the model.
+    images        [{"id", "data" (base64) and "media_type"}] or, on Edge, [{"id", "path"}] for a local file.
+                  Every question sees every picture, in request order.
+
+Edge has no videos, boosters, uploads, image URLs, points or boxes: a request that names them is refused.
+"""
+
+from __future__ import annotations
+
+import json
+import math
+from dataclasses import dataclass, field
+from decimal import Decimal, localcontext
+from typing import Any
+
+MAX_OPTIONS = 255
+MAX_LEVELS = 10
+MAX_IMAGES = 16
+MAX_IMAGE_ID = 64
+IMAGE_MEDIA_TYPES = ("image/jpeg", "image/png", "image/webp")
+ONE_ROW_VALUES = 256  # the answer codes: a grid up to this size is one internal choice
+MAX_GRID_VALUES = 4096
+MAX_DECIMALS = 6
+NUMBER_DEFAULTS = {"min": 0, "max": 255, "step": 1}
+NOT_ON_EDGE = ("videos", "boosters")
+
+
+class RequestError(ValueError):
+    """A request the model cannot answer. `status` is the HTTP status the hosted API gives the same fault, `error_type`
+    the kind of fault its error body names (the local server's, server.py)."""
+
+    def __init__(self, message: str, status: int = 400, error_type: str = "api_usage_error"):
+        super().__init__(message)
+        self.status = status
+        self.error_type = error_type
+
+
+def text_of(value: Any) -> str:
+    """A state, instruction, description or level as the prompt carries it."""
+    if value is None:
+        return ""
+    if isinstance(value, str):
+        return value
+    return json.dumps(value, ensure_ascii=False, separators=(",", ":"))
+
+
+def _content(value: Any, where: str, nullable: bool = True) -> None:
+    if not (isinstance(value, (str, dict, list)) or (value is None and nullable)):
+        raise RequestError(f"{where} must be a string, an object or an array", 422)
+
+
+# ---------------------------------------------------------------------------
+# Number grids
+# ---------------------------------------------------------------------------
+
+
+def _decimal(value: Any) -> Decimal:
+    return Decimal(repr(value)) if isinstance(value, float) else Decimal(int(value))
+
+
+def _places(value: Decimal) -> int:
+    """Digits after the point once trailing zeros are dropped: 0.5 has 1, 7 has 0."""
+    text = format(value.normalize(), "f")
+    return len(text.split(".")[1]) if "." in text else 0
+
+
+def _value_text(scaled: int, d: int) -> str:
+    """The grid value scaled / 10^d in its shortest plain decimal form."""
+    digits = str(abs(scaled)).rjust(d + 1, "0")
+    whole, frac = (digits[:-d], digits[-d:].rstrip("0")) if d else (digits, "")
+    text = whole + ("." + frac if frac else "")
+    return "-" + text if scaled < 0 else text
+
+
+@dataclass(frozen=True)
+class Grid:
+    """Value i is (m + i * s) / 10^d, for i in 0 .. n - 1."""
+
+    d: int
+    m: int
+    s: int
+    n: int
+
+    def text(self, i: int) -> str:
+        return _value_text(self.m + i * self.s, self.d)
+
+    def texts(self, start: int = 0, end: int | None = None) -> list[str]:
+        return [self.text(i) for i in range(start, self.n if end is None else end)]
+
+    @property
+    def pages(self) -> int:
+        return 1 if self.n <= ONE_ROW_VALUES else -(-self.n // ONE_ROW_VALUES)
+
+    def page_bounds(self) -> list[tuple[int, int]]:
+        """(first, end) of each page; the first n mod pages pages hold one value more."""
+        q, r = divmod(self.n, self.pages)
+        out, start = [], 0
+        for j in range(self.pages):
+            size = q + 1 if j < r else q
+            out.append((start, start + size))
+            start += size
+        return out
+
+
+def number_grid(question: dict, qid: str) -> Grid:
+    values = {}
+    for name in ("min", "max", "step"):
+        value = question.get(name)
+        if value is None:
+            value = NUMBER_DEFAULTS[name]
+        if isinstance(value, bool) or not isinstance(value, (int, float)) or (isinstance(value, float) and not math.isfinite(value)):
+            raise RequestError(f"question {qid!r}: {name} must be a finite number", 422)
+        values[name] = _decimal(value)
+        if abs(values[name]) > 2**53:
+            raise RequestError(f"question {qid!r}: {name} must be within plus or minus 2^53", 422)
+        if name != "max" and _places(values[name]) > MAX_DECIMALS:
+            raise RequestError(f"question {qid!r}: {name} has more than {MAX_DECIMALS} decimals", 422)
+    if values["step"] <= 0:
+        raise RequestError(f"question {qid!r}: step must be above 0", 422)
+    if values["min"] > values["max"]:
+        raise RequestError(f"question {qid!r}: min is above max", 422)
+    d = max(_places(values["min"]), _places(values["step"]))
+    with localcontext() as ctx:
+        ctx.prec = 200
+        if d:
+            bound = Decimal(10) ** (15 - d) - Decimal(10) ** -d
+            if abs(values["min"]) > bound or abs(values["max"]) > bound:
+                raise RequestError(f"question {qid!r}: on a grid with {d} decimals, min and max must stay within plus or minus {bound}", 422)
+        scale = Decimal(10) ** d
+        m, s = int(values["min"] * scale), int(values["step"] * scale)
+        top = int((values["max"] * scale).to_integral_value(rounding="ROUND_FLOOR"))
+    n = (top - m) // s + 1
+    if n > MAX_GRID_VALUES:
+        raise RequestError(f"question {qid!r}: the grid holds {n} values, the limit is {MAX_GRID_VALUES}", 422)
+    return Grid(d=d, m=m, s=s, n=n)
+
+
+def number_rows(instructions: str, grid: Grid) -> list[dict]:
+    """The internal choice questions of a number question: one over the values, or one over the pages and one per page."""
+    if grid.pages == 1:
+        keys = grid.texts()
+        return [{"type": "choice", "instructions": instructions, "keys": keys, "descs": [""] * len(keys)}]
+    bounds = grid.page_bounds()
+    rows = [{"type": "choice", "instructions": instructions, "keys": [f"{grid.text(a)} to {grid.text(b - 1)}" for a, b in bounds], "descs": [""] * len(bounds)}]
+    for a, b in bounds:
+        keys = grid.texts(a, b)
+        rows.append({"type": "choice", "instructions": instructions, "keys": keys, "descs": [""] * len(keys)})
+    return rows
+
+
+def compose(grid: Grid, answered: list[list[float]]) -> list[float]:
+    """The probability of every grid value: p(page) * p(value | page) on a paged grid."""
+    if grid.pages == 1:
+        return list(answered[0])
+    out: list[float] = []
+    for j, page in enumerate(answered[1:]):
+        out.extend(answered[0][j] * p for p in page)
+    return out
+
+
+# ---------------------------------------------------------------------------
+# The request
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class Question:
+    id: str
+    type: str  # choice, score, truth or number (as answered)
+    rows: list[dict]  # what the model runs: one prompt per row (none for a question answered without the model)
+    levels: list | None = None  # score: the levels as sent, for `legend`
+    keys: list[str] | None = None  # choice: the options in request order
+    grid: Grid | None = None  # number
+
+
+@dataclass
+class Request:
+    state: str
+    questions: list[Question]
+    images: list[dict] = field(default_factory=list)
+
+
+def parse(body: Any) -> Request:
+    """Validate a `/v1/decide` request body (a dict) and read it into questions. Raises RequestError."""
+    if not isinstance(body, dict):
+        raise RequestError("the request must be a JSON object", 422)
+    for name in NOT_ON_EDGE:
+        if body.get(name):
+            raise RequestError(f"`{name}` is not available on DecisionNode Edge")
+    state = body.get("state")
+    _content(state, "state")
+    questions = body.get("questions")
+    if not isinstance(questions, dict) or not questions:
+        raise RequestError("questions must be an object with at least one question", 422)
+    state_text = text_of(state)
+    out = [_question(qid, q) for qid, q in questions.items()]
+    return Request(state=state_text, questions=out, images=_images(body.get("images")))
+
+
+def _question(qid: str, q: Any) -> Question:
+    if not isinstance(q, dict) or "type" not in q:
+        raise RequestError(f"question {qid!r} must be an object with a type", 422)
+    kind = q["type"]
+    if kind == "noul":  # the older name of the yes-or-no type, answered as truth
+        kind = "truth"
+    if kind not in ("choice", "score", "truth", "number"):
+        raise RequestError(f"question {qid!r}: unknown question type {kind!r}")
+    instructions, criteria = q.get("instructions"), q.get("criteria")
+    _content(instructions, f"question {qid!r}: instructions")
+    text = text_of(instructions)
+
+    if kind == "number":
+        if instructions is None or instructions == "":
+            raise RequestError(f"Number question must have instructions: {qid}")
+        grid = number_grid(q, qid)
+        rows = [] if grid.n == 1 else number_rows(text, grid)
+        return Question(qid, "number", rows, grid=grid)
+
+    if kind == "choice":
+        if not isinstance(criteria, dict):
+            raise RequestError(f"question {qid!r}: a choice needs criteria, an object of options", 422)
+        if not criteria:
+            raise RequestError(f"Choice question must have at least one choice: {qid}")
+        if len(criteria) > MAX_OPTIONS:
+            raise RequestError(f"Too many choices: question {qid!r} has {len(criteria)} options, the limit is {MAX_OPTIONS} options per choice.")
+        for key, desc in criteria.items():
+            _content(desc, f"question {qid!r}: the description of {key!r}")
+        keys = list(criteria)
+        row = {"type": "choice", "instructions": text, "keys": keys, "descs": [text_of(d) for d in criteria.values()]}
+        return Question(qid, "choice", [] if len(keys) == 1 else [row], keys=keys)
+
+    if kind == "score":
+        if not isinstance(criteria, list) or not criteria:
+            raise RequestError(f"question {qid!r}: a score needs criteria, a list of 1 to {MAX_LEVELS} levels", 422)
+        if len(criteria) > MAX_LEVELS:
+            raise RequestError(f"Too many score levels. Must have at most {MAX_LEVELS} levels.")
+        for i, level in enumerate(criteria):
+            _content(level, f"question {qid!r}: level {i}", nullable=False)
+        row = {"type": "score", "instructions": text, "levels": [text_of(level) for level in criteria]}
+        return Question(qid, "score", [] if len(criteria) == 1 else [row], levels=list(criteria))
+
+    # truth
+    if criteria is not None and not isinstance(criteria, dict):
+        raise RequestError(f"question {qid!r}: truth criteria must be an object with `true` and `false`", 422)
+    if instructions is None and criteria is None:
+        raise RequestError(f"Truth question must have criteria or instructions: {qid}")
+    row: dict = {"type": "truth", "instructions": text}
+    if criteria:
+        for side in ("true", "false"):
+            _content(criteria.get(side), f"question {qid!r}: criteria.{side}")
+        sides = {side: text_of(criteria.get(side)) for side in ("true", "false") if text_of(criteria.get(side))}
+        if sides:
+            row["criteria"] = sides
+    return Question(qid, "truth", [row])
+
+
+def _images(images: Any) -> list[dict]:
+    if images is None:
+        return []
+    if not isinstance(images, list):
+        raise RequestError("images must be a list", 422)
+    if len(images) > MAX_IMAGES:
+        raise RequestError(f"Too many images: {len(images)}, the limit is {MAX_IMAGES}.")
+    seen = set()
+    for i, image in enumerate(images):
+        if not isinstance(image, dict) or not isinstance(image.get("id"), str) or not image["id"]:
+            raise RequestError(f"Image {i} needs an id.")
+        image_id = image["id"]
+        if len(image_id) > MAX_IMAGE_ID:
+            raise RequestError(f"Image {i}: its id is longer than {MAX_IMAGE_ID} characters.")
+        if image_id in seen:
+            raise RequestError(f"Image id {image_id!r} is used twice.")
+        seen.add(image_id)
+        if image.get("url") is not None or image.get("upload_id") is not None:
+            raise RequestError(f"Image {image_id!r}: DecisionNode Edge reads pictures from `data` or a local `path`, not from a URL or an upload.")
+        forms = [name for name in ("data", "path") if image.get(name) is not None]
+        if len(forms) != 1:
+            raise RequestError(f"Image {image_id!r} needs exactly one of data and path.")
+        if forms == ["data"] and image.get("media_type") not in IMAGE_MEDIA_TYPES:
+            raise RequestError(f"Image {image_id!r}: media_type must be one of {', '.join(IMAGE_MEDIA_TYPES)}.")
+    return images

runtime/src/decisionnode_edge/server.py

@@ -0,0 +1,260 @@
+"""A local HTTP server for a DecisionNode Edge model, on the standard library only.
+
+    python -m decisionnode_edge serve path/to/model [--host 127.0.0.1] [--port 8000] [--api-key FILE] [--backend ...]
+
+    POST /v1/systemone   the /v1/systemone format used by public decision-model leaderboards:
+                         {"model", "state", "questions", "assets"}; noul, choice and score questions; a noul is
+                         answered {"type": "noul", "noul": p}, a choice and a score as on /v1/decide
+    POST /v1/decide      the DecisionNode API's call: the runtime's own request and answers (EdgeModel.decide)
+    GET  /v1/models      {"models": [{"name", "description", "release_date"}]}
+    GET  /healthz        {"status": "ok", "model", "backend"} (no key needed)
+
+Pictures: `images` as the runtime takes them, base64 `data` only (a local `path` is refused over HTTP: a caller must
+never make the server read its files), and on /v1/systemone the `assets` map of that format, {"<ref>": {"type": "image",
+"format": "png" | "jpg" | "jpeg", "data_base64": ...}}, each asset a picture whose id is its reference (the state names
+it), in the map's order.
+
+Errors carry a `detail` body: {"detail": {"error_type", "message"}} (a 422 as a list of field errors). A
+request over the model's length limit says "maximum context length", a choice over 255 options "options per choice",
+the words leaderboard harnesses look for to count a request as beyond the model rather than as a failure.
+
+The model answers one request at a time (a lock); the server listens on the loopback address unless told otherwise.
+`model` is required on /v1/systemone, as on the hosted API, and any name is accepted: the answer names the model that
+answered.
+"""
+
+from __future__ import annotations
+
+import argparse
+import hmac
+import json
+import logging
+import sys
+import threading
+import time
+import traceback
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from typing import Any
+
+from .request import RequestError
+
+MAX_BODY_BYTES = 64 << 20  # 16 pictures as base64 with room to spare
+SYSTEMONE_TYPES = ("noul", "choice", "score")
+ASSET_MEDIA_TYPES = {"png": "image/png", "jpg": "image/jpeg", "jpeg": "image/jpeg"}
+AUTH_MESSAGE = "Cannot authenticate with the server. Please check your API key and try again."
+ROUTES = {"/v1/systemone": "POST", "/v1/decide": "POST", "/v1/models": "GET", "/healthz": "GET"}
+
+log = logging.getLogger("decisionnode_edge.server")
+
+
+class HTTPError(Exception):
+    def __init__(self, status: int, error_type: str, message: str, headers: dict | None = None):
+        super().__init__(message)
+        self.status, self.error_type, self.message, self.headers = status, error_type, message, headers or {}
+
+
+def error_body(status: int, error_type: str, message: str) -> dict:
+    if status == 422:
+        return {"detail": [{"type": "value_error", "loc": ["body"], "msg": message, "input": None}]}
+    return {"detail": {"error_type": error_type, "message": message}}
+
+
+def _images_over_http(images: Any) -> list:
+    if images is None:
+        return []
+    if not isinstance(images, list):
+        raise RequestError("images must be a list", 422)
+    for image in images:
+        if isinstance(image, dict) and image.get("path") is not None:
+            raise RequestError("Over HTTP a picture is sent as base64 `data` (or an asset), never as a local `path`.")
+    return list(images)
+
+
+def _asset_images(assets: Any) -> list[dict]:
+    """The /v1/systemone `assets` map as the runtime's images: the reference is the picture's id."""
+    if assets is None:
+        return []
+    if not isinstance(assets, dict):
+        raise RequestError("assets must be an object of references", 422)
+    out = []
+    for ref, asset in assets.items():
+        if not isinstance(asset, dict) or asset.get("type", "image") not in ("image", None):
+            raise RequestError(f"Asset {ref!r}: only image assets are taken.")
+        media_type = ASSET_MEDIA_TYPES.get(str(asset.get("format", "")).lower())
+        if media_type is None:
+            raise RequestError(f"Asset {ref!r}: format must be one of png, jpg, jpeg.")
+        if not isinstance(asset.get("data_base64"), str) or not asset["data_base64"]:
+            raise RequestError(f"Asset {ref!r} needs data_base64.", 422)
+        out.append({"id": ref, "data": asset["data_base64"], "media_type": media_type})
+    return out
+
+
+class Service:
+    """The routes, without the socket: handle(method, path, headers, body) gives (status, JSON body, extra headers)."""
+
+    def __init__(self, model: Any, api_key: str | None = None, description: str | None = None, release_date: str | None = None):
+        self.model = model
+        self.api_key = api_key
+        self.name = model.config["name"]
+        self.description = description or model.config.get("description") or f"{self.name}: typed, calibrated decisions, run locally"
+        self.release_date = release_date or model.config.get("release_date")
+        self.lock = threading.Lock()
+
+    def handle(self, method: str, path: str, headers: dict[str, str], body: bytes) -> tuple[int, dict, dict]:
+        try:
+            return 200, self._route(method, path.split("?", 1)[0], headers, body), {}
+        except HTTPError as error:
+            return error.status, error_body(error.status, error.error_type, error.message), error.headers
+        except RequestError as error:
+            return error.status, error_body(error.status, error.error_type, str(error)), {}
+        except Exception:  # a fault of the model or the backend, never the caller's
+            log.error("the model could not answer:\n%s", traceback.format_exc())
+            return 500, error_body(500, "internal_error", "The model could not answer this request."), {}
+
+    def _route(self, method: str, path: str, headers: dict[str, str], body: bytes) -> dict:
+        if path not in ROUTES:
+            raise HTTPError(404, "not_found_error", "Not found.")
+        if method != ROUTES[path]:
+            raise HTTPError(405, "api_usage_error", "Method not allowed.", {"Allow": ROUTES[path]})
+        if path == "/healthz":
+            return {"status": "ok", "model": self.name, "backend": self.model.backend}
+        self._authenticate(headers)
+        if path == "/v1/models":
+            return {"models": [{"name": self.name, "description": self.description, "release_date": self.release_date}]}
+        try:
+            request = json.loads(body)
+        except (UnicodeDecodeError, json.JSONDecodeError) as error:
+            raise RequestError(f"the body is not JSON: {error}", 422) from None
+        if path == "/v1/decide":
+            if isinstance(request, dict) and request.get("images") is not None:
+                request = {**request, "images": _images_over_http(request["images"])}
+            with self.lock:
+                return self.model.decide(request)
+        return self._systemone(request)
+
+    def _authenticate(self, headers: dict[str, str]) -> None:
+        if self.api_key is None:
+            return
+        given = headers.get("authorization", "")
+        scheme, _, token = given.partition(" ")
+        if scheme.lower() != "bearer" or not hmac.compare_digest(token.strip().encode(), self.api_key.encode()):
+            raise HTTPError(401, "authentication_error", AUTH_MESSAGE, {"WWW-Authenticate": "Bearer"})
+
+    def _systemone(self, request: Any) -> dict:
+        if not isinstance(request, dict):
+            raise RequestError("the request must be a JSON object", 422)
+        if not isinstance(request.get("model"), str):
+            raise RequestError("model: Field required, a string", 422)
+        questions = request.get("questions")
+        if isinstance(questions, dict):
+            for qid, question in questions.items():
+                kind = question.get("type") if isinstance(question, dict) else None
+                if kind == "number":
+                    raise RequestError(f"question {qid!r}: the number type is not available on /v1/systemone; send it to /v1/decide")
+                if isinstance(question, dict) and "type" in question and kind not in SYSTEMONE_TYPES:
+                    raise RequestError(f"question {qid!r}: unknown question type {kind!r}")
+        images = _images_over_http(request.get("images")) + _asset_images(request.get("assets"))
+        body = {"state": request.get("state"), "questions": questions}
+        if images:
+            body["images"] = images
+        with self.lock:
+            response = self.model.decide(body)
+        answers = {}
+        for qid, answer in response["answers"].items():  # a noul is answered under its own name, as the format has it
+            answers[qid] = {"type": "noul", "noul": answer["truth"]} if answer["type"] == "truth" else answer
+        return {"model": response["model"], "answers": answers, "usage": response["usage"]}
+
+
+class Handler(BaseHTTPRequestHandler):
+    protocol_version = "HTTP/1.1"  # keep-alive: every response carries Content-Length
+    disable_nagle_algorithm = True  # headers and body are two writes: without it a kept-alive client waits for a delayed ACK
+    server_version = "decisionnode-edge"
+    service: Service = None  # set by make_server
+
+    def _answer(self, method: str) -> None:
+        started = time.perf_counter()
+        body = b""
+        if method == "POST":
+            length = self.headers.get("Content-Length")
+            if length is None or not length.isdigit():
+                return self._send(411, error_body(411, "api_usage_error", "Content-Length is required."), {"Connection": "close"}, started)
+            if int(length) > MAX_BODY_BYTES:
+                return self._send(413, error_body(413, "api_usage_error", "Request body too large."), {"Connection": "close"}, started)
+            body = self.rfile.read(int(length))
+        headers = {k.lower(): v for k, v in self.headers.items()}
+        status, payload, extra = self.service.handle(method, self.path, headers, body)
+        self._send(status, payload, extra, started)
+
+    def _send(self, status: int, payload: dict, extra: dict, started: float) -> None:
+        data = json.dumps(payload, ensure_ascii=False, separators=(",", ":")).encode("utf-8")
+        self.send_response(status)
+        self.send_header("Content-Type", "application/json")
+        self.send_header("Content-Length", str(len(data)))
+        self.send_header("X-Latency-Ms", f"{(time.perf_counter() - started) * 1000:.2f}")
+        for name, value in extra.items():
+            self.send_header(name, value)
+        self.end_headers()
+        self.wfile.write(data)
+        if extra.get("Connection") == "close":
+            self.close_connection = True
+
+    def do_GET(self) -> None:  # noqa: N802 (the standard library's names)
+        self._answer("GET")
+
+    def do_POST(self) -> None:  # noqa: N802
+        self._answer("POST")
+
+    def do_PUT(self) -> None:  # noqa: N802
+        self._answer("PUT")
+
+    def do_DELETE(self) -> None:  # noqa: N802
+        self._answer("DELETE")
+
+    def log_message(self, format: str, *args: Any) -> None:  # noqa: A002
+        log.info("%s %s", self.address_string(), format % args)
+
+
+def make_server(model: Any, host: str = "127.0.0.1", port: int = 8000, api_key: str | None = None, **service: Any) -> ThreadingHTTPServer:
+    handler = type("EdgeHandler", (Handler,), {"service": Service(model, api_key=api_key, **service)})
+    server = ThreadingHTTPServer((host, port), handler)
+    server.daemon_threads = True
+    return server
+
+
+def main(argv: list[str] | None = None) -> int:
+    from . import EdgeModel
+
+    parser = argparse.ArgumentParser(prog="python -m decisionnode_edge serve", description="Serve a DecisionNode Edge model over HTTP: /v1/systemone, /v1/decide, /v1/models.")
+    parser.add_argument("model", help="the model directory")
+    parser.add_argument("--host", default="127.0.0.1", help="the address to listen on (default 127.0.0.1, this machine only)")
+    parser.add_argument("--port", type=int, default=8000)
+    parser.add_argument("--api-key", default=None, help="a file holding the key callers must send as `Authorization: Bearer <key>`")
+    parser.add_argument("--device", default=None)
+    parser.add_argument("--dtype", default=None)
+    parser.add_argument("--backend", default=None, help="auto, torch, vllm, openvino, mlx or llamacpp (default: auto)")
+    parser.add_argument("--artifacts", default=None, help="the directory python -m decisionnode_edge.build wrote")
+    parser.add_argument("--description", default=None, help="the description GET /v1/models gives")
+    parser.add_argument("--release-date", default=None, help="the release date GET /v1/models gives (YYYY-MM-DD)")
+    args = parser.parse_args(argv)
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(name)s %(message)s")
+    api_key = None
+    if args.api_key:
+        with open(args.api_key, encoding="utf-8") as f:
+            api_key = f.read().strip()
+        if not api_key:
+            raise SystemExit(f"{args.api_key} is empty")
+    model = EdgeModel.load(args.model, device=args.device, dtype=args.dtype, backend=args.backend, artifacts=args.artifacts)
+    model.decide({"state": "The color is red.", "questions": {"warmup": {"type": "choice", "instructions": "Which color is named?", "criteria": {"red": "red", "blue": "blue"}}}})
+    server = make_server(model, args.host, args.port, api_key, description=args.description, release_date=args.release_date)
+    log.info("serving %s (%s backend) on http://%s:%d%s", model.config["name"], model.backend, args.host, server.server_address[1], ", key required" if api_key else "")
+    try:
+        server.serve_forever()
+    except KeyboardInterrupt:
+        pass
+    finally:
+        server.server_close()
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

runtime/src/decisionnode_edge/template.py

@@ -0,0 +1,264 @@
+"""The prompt format the model reads: the added tokens, the answer codes and the encoder.
+
+One prompt per question. The state block comes first, then the question block, then the
+word "Answer" and the <|answer|> token, whose position the answer is read from:
+
+    <|state|>\\n{Picture n (id): <|vision_start|>pads<|vision_end|>\\n ...}{state}\\n
+    <|question|>\\ntype: {choice|score|noul}\\n{instructions}\\n{option lines}Answer<|answer|>
+
+A choice option line is `<|opt_NNN|> key: description\\n` (`<|opt_NNN|> key\\n` when the
+description is empty), a score level line `<|opt_NNN|> index: level\\n`. A truth question
+has no option lines (the prompt calls it `noul`) and carries
+its criteria as `yes: ...\\n` and `no: ...\\n` lines. It is answered by <|yes|> and <|no|>.
+
+Each option gets one of 256 code tokens. The codes are drawn from a stream seeded by a
+SHA-256 of the question alone (never the state), so the same question always gets the
+same codes. The seed's first part is a fixed string from the model directory
+(`code_seed_hex` in decisionnode_edge.json): the model gives its published answers only
+with that string.
+
+Request text can never become a control token: plain text is tokenised with
+`split_special_tokens=True`, and the few added tokens that flag does not split (such as
+`<think>`) are cut inside the string before tokenising.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import re
+from dataclasses import dataclass, field
+from typing import Any, Sequence
+
+STATE = "<|state|>"
+QUESTION = "<|question|>"
+ANSWER = "<|answer|>"
+YES = "<|yes|>"
+NO = "<|no|>"
+N_CODES = 256
+CODES = tuple(f"<|opt_{i:03d}|>" for i in range(N_CODES))
+ADDED_TOKENS = (STATE, QUESTION, ANSWER, YES, NO) + CODES  # 261 tokens, in id order
+ANSWER_CUE = "Answer"
+# The type word each question type carries in the prompt.
+TYPE_WORDS = {"choice": "choice", "score": "score", "truth": "noul"}
+
+IMAGE_PAD = "<|image_pad|>"
+VISION_START = "<|vision_start|>"
+VISION_END = "<|vision_end|>"
+
+# Segment kinds
+SPECIAL, TEXT, STATE_TEXT, IMAGE = "special", "text", "state", "image"
+
+
+class TemplateError(ValueError):
+    pass
+
+
+# ---------------------------------------------------------------------------
+# Codes
+# ---------------------------------------------------------------------------
+
+
+def question_hash(seed: str, question: dict, pass_index: int = 0) -> bytes:
+    """SHA-256 of the question alone: the seed, the pass, the type word, the instructions and the options
+    (or levels, or truth criteria). Every part is length-prefixed, so two questions cannot collide by concatenation."""
+    kind = question["type"]
+    parts = [seed, str(int(pass_index)), TYPE_WORDS[kind], question["instructions"]]
+    if kind == "choice":
+        for key, desc in zip(question["keys"], question["descs"]):
+            parts += [key, desc]
+    elif kind == "score":
+        parts += list(question["levels"])
+    else:
+        for name, text in criteria_lines(question):
+            parts += [name, text]
+    h = hashlib.sha256()
+    for part in parts:
+        raw = part.encode("utf-8")
+        h.update(len(raw).to_bytes(8, "big"))
+        h.update(raw)
+    return h.digest()
+
+
+class HashStream:
+    """Deterministic integers from a seed: SHA-256 in counter mode, 4 bytes at a time."""
+
+    def __init__(self, seed: bytes):
+        self._seed, self._counter, self._buf = seed, 0, b""
+
+    def _u32(self) -> int:
+        if len(self._buf) < 4:
+            self._buf += hashlib.sha256(self._seed + self._counter.to_bytes(8, "big")).digest()
+            self._counter += 1
+        value = int.from_bytes(self._buf[:4], "big")
+        self._buf = self._buf[4:]
+        return value
+
+    def below(self, n: int) -> int:
+        """A uniform integer in [0, n), by rejection (no modulo bias)."""
+        limit = (1 << 32) - ((1 << 32) % n)
+        while True:
+            value = self._u32()
+            if value < limit:
+                return value % n
+
+    def sample(self, population: int, k: int) -> list[int]:
+        """k distinct integers from range(population): a partial Fisher-Yates shuffle."""
+        pool = list(range(population))
+        for i in range(k):
+            j = i + self.below(population - i)
+            pool[i], pool[j] = pool[j], pool[i]
+        return pool[:k]
+
+
+def n_classes(question: dict) -> int:
+    if question["type"] == "choice":
+        return len(question["keys"])
+    if question["type"] == "score":
+        return len(question["levels"])
+    return 2
+
+
+def assign_codes(seed: str, question: dict) -> list[int]:
+    """The code index of every option (or level) of a choice or score question, in the order given."""
+    k = n_classes(question)
+    if k > N_CODES:
+        raise TemplateError(f"{k} options, the model has {N_CODES} codes")
+    return HashStream(question_hash(seed, question)).sample(N_CODES, k)
+
+
+# ---------------------------------------------------------------------------
+# Segments
+# ---------------------------------------------------------------------------
+
+
+def criteria_lines(question: dict) -> list[tuple[str, str]]:
+    """A truth question's criteria as ("yes", text) and ("no", text), each only if given."""
+    criteria = question.get("criteria") or {}
+    return [(label, criteria[name]) for name, label in (("true", "yes"), ("false", "no")) if criteria.get(name)]
+
+
+def image_label(number: int, image_id: str) -> str:
+    """The words in front of the request's number-th picture (counted from 1)."""
+    return f"Picture {int(number)} ({image_id}): "
+
+
+def build_segments(question: dict, state: str, image_ids: Sequence[str], codes: Sequence[int]) -> list[tuple[str, str]]:
+    """The prompt of one question as (kind, value) segments, state first."""
+    segments: list[tuple[str, str]] = [(SPECIAL, STATE), (TEXT, "\n")]
+    for number, image_id in enumerate(image_ids, 1):
+        segments += [(TEXT, image_label(number, image_id)), (IMAGE, image_id), (TEXT, "\n")]
+    segments += [(STATE_TEXT, state), (TEXT, "\n")]
+
+    kind = question["type"]
+    text = f"\ntype: {TYPE_WORDS[kind]}\n{question['instructions']}\n"
+    if kind == "truth":
+        text += "".join(f"{label}: {value}\n" for label, value in criteria_lines(question))
+    segments += [(SPECIAL, QUESTION), (TEXT, text)]
+    if kind == "choice":
+        for key, desc, code in zip(question["keys"], question["descs"], codes):
+            segments += [(SPECIAL, CODES[code]), (TEXT, f" {key}: {desc}\n" if desc else f" {key}\n")]
+    elif kind == "score":
+        for index, (level, code) in enumerate(zip(question["levels"], codes)):
+            segments += [(SPECIAL, CODES[code]), (TEXT, f" {index}: {level}\n")]
+    segments += [(TEXT, ANSWER_CUE), (SPECIAL, ANSWER)]
+    return segments
+
+
+# ---------------------------------------------------------------------------
+# Encoder
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class Encoded:
+    input_ids: list[int]
+    class_ids: list[int]  # the token whose readout row scores each class, in class order
+    state_tokens: int  # tokens of the state block (with its pictures), shared by every question of a request
+    image_slots: list[tuple[str, int, int]] = field(default_factory=list)  # (image id, first pad position, count)
+
+
+class Template:
+    """The template bound to the model's tokenizer."""
+
+    def __init__(self, tokenizer: Any, seed: str):
+        self.tokenizer = tokenizer
+        self.seed = seed
+        ids = [tokenizer.convert_tokens_to_ids(token) for token in ADDED_TOKENS]
+        first = ids[0]
+        if first is None or ids != list(range(first, first + len(ADDED_TOKENS))):
+            raise TemplateError("the tokenizer does not hold the model's added tokens as one id range")
+        self.first_id = first
+        self.special_ids = dict(zip(ADDED_TOKENS, ids))
+        self.image_pad, self.vision_start, self.vision_end = (tokenizer.convert_tokens_to_ids(t) for t in (IMAGE_PAD, VISION_START, VISION_END))
+        unsplit = _unsplit_added_tokens(tokenizer)
+        self._guard = re.compile("|".join(re.escape(t) for t in sorted(unsplit, key=len, reverse=True))) if unsplit else None
+        self._guarded_ids = frozenset(unsplit.values())
+
+    def class_ids(self, question: dict, codes: Sequence[int]) -> list[int]:
+        if question["type"] == "truth":
+            return [self.special_ids[YES], self.special_ids[NO]]
+        return [self.first_id + 5 + c for c in codes]
+
+    def encode(self, question: dict, state: str, image_ids: Sequence[str] = (), image_tokens: dict[str, int] | None = None) -> Encoded:
+        codes = assign_codes(self.seed, question) if question["type"] in ("choice", "score") else []
+        segments = build_segments(question, state, image_ids, codes)
+        table = self._tokenize([value for kind, value in segments if kind in (TEXT, STATE_TEXT)])
+        ids: list[int] = []
+        slots: list[tuple[str, int, int]] = []
+        state_tokens = 0
+        for kind, value in segments:
+            if kind == SPECIAL:
+                ids.append(self.special_ids[value])
+            elif kind in (TEXT, STATE_TEXT):
+                ids.extend(table[value])
+                if kind == STATE_TEXT:
+                    state_tokens = len(ids) + len(table["\n"])  # the state block ends with the line break after the state
+            else:  # a picture: its placeholders, as many as the image processor gives it
+                count = int((image_tokens or {})[value])
+                ids.append(self.vision_start)
+                slots.append((value, len(ids), count))
+                ids.extend([self.image_pad] * count)
+                ids.append(self.vision_end)
+        return Encoded(ids, self.class_ids(question, codes), state_tokens, slots)
+
+    def _tokenize(self, texts: Sequence[str]) -> dict[str, list[int]]:
+        """Plain-text ids of every distinct text, with special-token strings kept as text."""
+        unique = [t for t in dict.fromkeys(texts) if t]
+        pieces_of = {t: _break_tokens(t, self._guard) for t in unique if self._guard is not None and self._guard.search(t)}
+        flat = list(dict.fromkeys(p for t in unique for p in pieces_of.get(t, (t,))))
+        encoded = {}
+        if flat:
+            out = self.tokenizer(flat, add_special_tokens=False, split_special_tokens=True, return_attention_mask=False)["input_ids"]
+            encoded = dict(zip(flat, (list(x) for x in out)))
+        table: dict[str, list[int]] = {"": []}
+        for text in unique:
+            if text in pieces_of:
+                ids = [i for piece in pieces_of[text] for i in encoded[piece]]
+                if not self._guarded_ids.isdisjoint(ids):
+                    raise TemplateError("request text produced a control token")
+                table[text] = ids
+            else:
+                table[text] = encoded[text]
+        return table
+
+
+def _unsplit_added_tokens(tokenizer: Any) -> dict[str, int]:
+    """Added tokens that `split_special_tokens=True` still turns into one id (found by trying each)."""
+    out = {}
+    for token, index in dict(tokenizer.get_added_vocab()).items():
+        if len(token) >= 2 and tokenizer.encode(token, add_special_tokens=False, split_special_tokens=True) == [index]:
+            out[token] = int(index)
+    return out
+
+
+def _break_tokens(text: str, pattern: "re.Pattern[str]") -> list[str]:
+    """Cut `text` after the first character of every added-token string, so none survives whole."""
+    pieces, start = [], 0
+    for match in pattern.finditer(text):
+        pieces.append(text[start : match.start() + 1])
+        start = match.start() + 1
+    pieces.append(text[start:])
+    out = []
+    for piece in pieces:
+        out.extend(_break_tokens(piece, pattern) if pattern.search(piece) else [piece])
+    return [piece for piece in out if piece]

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6e9f31870a35eb9372bbc8719603e5d02f2e07eff90af9f7b77fe3b17d3628ff
+size 20038396

tokenizer_config.json

@@ -0,0 +1,48 @@
+{
+  "add_prefix_space": false,
+  "audio_bos_token": "<|audio_start|>",
+  "audio_eos_token": "<|audio_end|>",
+  "audio_token": "<|audio_pad|>",
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "image_token": "<|image_pad|>",
+  "is_local": true,
+  "local_files_only": false,
+  "merges_file": null,
+  "model_max_length": 262144,
+  "model_specific_special_tokens": {
+    "audio_bos_token": "<|audio_start|>",
+    "audio_eos_token": "<|audio_end|>",
+    "audio_token": "<|audio_pad|>",
+    "image_token": "<|image_pad|>",
+    "video_token": "<|video_pad|>",
+    "vision_bos_token": "<|vision_start|>",
+    "vision_eos_token": "<|vision_end|>"
+  },
+  "pad_token": "<|endoftext|>",
+  "pretokenize_regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen3_5Tokenizer",
+  "unk_token": null,
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>"
+}

video_preprocessor_config.json

@@ -0,0 +1,21 @@
+{
+    "size": {
+        "longest_edge": 234881024,
+        "shortest_edge": 4096
+    },
+    "patch_size": 16,
+    "temporal_patch_size": 2,
+    "merge_size": 2,
+    "image_mean": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "image_std": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "processor_class": "Qwen3VLProcessor",
+    "video_processor_type": "Qwen3VLVideoProcessor"
+}