Add Dohnuts 0.1.0 decision model

LICENSE

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NOTICE

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+Dohnuts
+Copyright 2026 PsiACE and Dohnuts contributors.
+
+The source code is licensed under Apache-2.0. Model weights and source datasets
+are separate artifacts with their own terms.
+
+Qwen3.5-0.8B is developed by the Qwen team at Alibaba Cloud and is licensed under
+Apache-2.0. Dohnuts trains language LoRA adapters and a decision head over this
+base. Base weights are downloaded from the original publisher.
+https://huggingface.co/Qwen/Qwen3.5-0.8B
+
+The RLCD objective and evaluation protocols follow Laya by Nandha Kishor M and
+Laya Vision by Reed Richards. Both projects are licensed under Apache-2.0.
+Pinned references and implementation details are recorded in https://github.com/PsiACE/dohnuts/blob/main/docs/rlcd.md and
+https://github.com/PsiACE/dohnuts/blob/main/docs/upstream-alignment.md.
+https://github.com/NandhaKishorM/laya
+https://github.com/r33drichards/laya-vision
+
+JevBench supplies the public benchmark tasks and scoring protocol. Published Jev
+outcomes are reference measurements, not locally executed Jev predictions.
+https://github.com/fstandhartinger/jevbench
+
+Bub supplies the agent SDK. Flash Linear Attention supplies fused operators.
+They are installed as dependencies, not bundled copies of their source code.
+https://github.com/bubbuild/bub
+https://github.com/fla-org/flash-linear-attention
+
+Dataset sources, provenance, and terms are listed in https://github.com/PsiACE/dohnuts/blob/main/docs/data-and-evaluation.md.
+Raw datasets, source images, and third-party model weights are not distributed
+with this source repository or Python package. Reference names do not imply
+endorsement by their authors.

README.md

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+---
+library_name: dohnuts
+base_model: Qwen/Qwen3.5-0.8B
+base_model_relation: adapter
+license: cc-by-nc-sa-4.0
+tags:
+- dohnuts
+- decision-model
+- multimodal
+- rlcd
+- lora
+- research
+---
+
+# Dohnuts-0.1.0-0.8B
+
+A multimodal decision model built on Qwen3.5-0.8B. It scores supplied candidates
+from text and images, returning candidate probabilities, truth estimates, or
+ordered scores. Independent questions about one input share computation in a
+single forward pass. The interface returns decisions without generating reasoning
+or free-form answers.
+
+## Model details
+
+| Property | Value |
+| --- | --- |
+| Base model | Qwen/Qwen3.5-0.8B |
+| Training | Joint RLCD and auxiliary cross-entropy; language LoRA and a candidate scorer |
+| Selection | Seed 42, update 3,600; highest development macro accuracy |
+| Calibration | One temperature per decision type, fitted on an independent partition |
+| Runtime | Merged LoRA, BF16, fused operations, shared input prefixes |
+| Inputs | Text and one decoded image; 2–128 candidates per question |
+| Context | 4,096 tokens per question at inference; 2,048 during training |
+| Hardware used | One AMD Radeon RX 7900 XTX, 24 GB |
+
+## Use
+
+Dohnuts is intended for tasks with explicit candidate answers: routing requests,
+classifying content, estimating whether a condition holds, rating relevance, or
+answering visual questions. An application supplies the choices and decides how
+to act on the returned probabilities.
+
+After [setting up the runtime](https://github.com/PsiACE/dohnuts/blob/main/docs/inference.md), load the model from Hugging Face:
+
+```python
+from dohnuts.predictor import Predictor
+
+model = Predictor.from_checkpoint("PsiACE/Dohnuts-0.1.0-0.8B")
+```
+
+The compact checkpoint contains LoRA and scorer weights. The loader downloads
+and caches it with the pinned base model, merges LoRA, and applies calibration.
+Weights are not bundled with the Python package. See the [inference guide](https://github.com/PsiACE/dohnuts/blob/main/docs/inference.md) for question
+definitions, images, and response fields, or [Bub integration](https://github.com/PsiACE/dohnuts/blob/main/docs/bub-agent.md)
+for agent use.
+
+## Evaluation
+
+The model achieves **78.21% macro accuracy** over 26 held-out dataset groups
+containing 180,031 decisions. Each group contributes equally to this mean.
+Development data selects weights; calibration data fits temperatures; test data
+does neither. This is one training seed, with no estimate of variation across seeds.
+
+### JevBench
+
+Accuracy on the same 231 public tasks from JevBench v1.2.2:
+
+| Model | Correct | Accuracy |
+| --- | ---: | ---: |
+| Dohnuts-0.1.0-0.8B | 152 / 231 | 65.80% |
+| Jev 1.13.0 | 200 / 231 | 86.58% |
+| Laya multilingual | 110 / 231 | 47.62% |
+| Laya Vision | 111 / 231 | 48.05% |
+
+Jev results use published per-task outcomes; Dohnuts and the two Laya checkpoints
+were measured locally. Dohnuts uses a 4,096-token limit; the Laya runs use their
+native 1,024-token limit and truncation. The other 303 leaderboard tasks are
+unavailable, including the judge tier. These accuracies are not the official
+four-axis leaderboard score.
+
+### Laya task suites
+
+Dohnuts leads the published Laya multilingual reference on the 51-language
+MASSIVE intent suite, at **60.14%** versus **36.61%** language macro accuracy.
+Laya multilingual leads on the 15-language XNLI suite, at **73.84%** versus
+**70.91%**. The upstream question builders are preserved, but the historical
+reference's input hashes are unavailable, so exact input identity cannot be verified.
+
+Against Laya Vision on the same local image examples, Dohnuts scores higher on
+A-OKVQA and VQAv2 yes/no; Laya Vision scores higher on ScienceQA and has lower
+calibration error on ScienceQA and VQAv2. These runs use different precision and
+have reference data-exposure limitations, documented in the
+[comparison protocols](https://github.com/PsiACE/dohnuts/blob/main/docs/upstream-alignment.md).
+
+The [comparison gallery](https://github.com/PsiACE/dohnuts/blob/main/docs/figures/README.md) includes every application suite,
+language results, vision accuracy and calibration, and paired JevBench outcomes.
+
+### Inference speed
+
+Warm end-to-end median latency on one RX 7900 XTX, using BF16 and each model's
+native API:
+
+| Workload | Dohnuts | Laya multilingual | Laya Vision |
+| --- | ---: | ---: | ---: |
+| Text, 1 question | 15.07 ms | 9.77 ms | 11.00 ms |
+| Text, 50 questions | 112.51 ms | 47.19 ms | 125.63 ms |
+| Image, 1 question | 24.43 ms | — | 75.41 ms |
+| Image, 3 questions | 35.84 ms | — | 77.89 ms |
+
+Measurements use three warmups and 20 synchronized repetitions, including
+preprocessing and transfers. They exclude model loading, network, and queueing.
+Image timings use warm caches. They do not measure uncached image encoding or
+service throughput under load. See the [full charts](https://github.com/PsiACE/dohnuts/blob/main/docs/figures/README.md#same-hardware-inference-latency)
+and [benchmark protocol](https://github.com/PsiACE/dohnuts/blob/main/docs/local-benchmarks.md).
+
+## Training
+
+The mixture covers 26 text, language, and vision task groups, including public
+classification, question-answering, retrieval, policy, mail, and visual datasets.
+A fixed cap provides 143,238 eligible training rows; sampling is uniform over
+groups with replacement. The 3,600 updates process 115,200 sampled examples.
+Related documents and identical images are grouped to prevent cross-split leakage.
+Dataset sources, exclusions, and terms are listed in the
+[data reference](https://github.com/PsiACE/dohnuts/blob/main/docs/data-and-evaluation.md).
+
+The base model and vision encoder are frozen. Training updates rank-8 language
+LoRA adapters and a shared candidate scorer. The joint RLCD and cross-entropy
+objective follows the pinned Laya and Laya Vision implementations. Its four
+samples perturb decision logits; they are not generated trajectories. LoRA is
+merged before temperature fitting and final evaluation. The
+[RLCD specification](https://github.com/PsiACE/dohnuts/blob/main/docs/rlcd.md) gives the objective and fixed schedule.
+
+## Limitations
+
+- Quality depends on the task. Jev leads on the public JevBench tasks; Laya
+  multilingual leads on several application suites and small text-batch latency.
+- Global temperatures do not improve every dataset's calibration. The API's
+  `confidence` field summarizes a distribution; it is not a measured probability
+  of correctness. Check calibration on the intended workload.
+- Candidate wording, order, and input length can affect decisions. Over-budget
+  inputs are rejected. A long-document result covers only the eligible subset.
+- Laya Vision has possible VQAv2 training-pool exposure and A-OKVQA selection
+  exposure. Backbone pretraining exposure is unverified. These comparisons do
+  not establish performance on unseen data for every reference.
+- Bub acceptance exercises the decision tool. It does not measure autonomous
+  planning quality.
+
+## Artifact and provenance
+
+Base revision: `2fc06364715b967f1860aea9cf38778875588b17`.
+
+Selected weight SHA-256:
+`196be33a0282537bcd821e2115643b352d0ad2a0bbaf7b242a1b7fe5bd96cfdf`.
+
+The exported checkpoint records calibration, selection, and partition hashes in
+`dohnuts.json`. The [results](https://github.com/PsiACE/dohnuts/blob/main/results/README.md) include loss, development accuracy,
+held-out quality, calibration bins, latency samples, and resource measurements.
+Their manifest identifies the selected weights and checksums each published table.
+No training Git revision was recorded. [Chart values](https://github.com/PsiACE/dohnuts/blob/main/docs/figures/chart-data.csv)
+and a [figure manifest](https://github.com/PsiACE/dohnuts/blob/main/docs/figures/manifest.json) accompany the comparisons.
+
+## License
+
+The code is licensed under [Apache-2.0](https://github.com/PsiACE/dohnuts/blob/main/LICENSE). The decision weights are provided
+under [CC BY-NC-SA 4.0](https://huggingface.co/PsiACE/Dohnuts-0.1.0-0.8B/blob/main/LICENSE)
+for non-commercial research. This grant covers the Dohnuts LoRA and decision-head
+contributions; the base model and source data retain their own terms.
+
+The Qwen3.5-0.8B base is Apache-2.0. ScienceQA's dataset terms include non-commercial
+and share-alike restrictions; other sources have their own research-use terms.
+The checkpoint is not offered as a commercially cleared model. See the
+[data reference](https://github.com/PsiACE/dohnuts/blob/main/docs/data-and-evaluation.md#release-assets-and-terms) and
+[attributions](https://github.com/PsiACE/dohnuts/blob/main/NOTICE).

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dohnuts.json

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+{
+  "format_version": 1,
+  "project": "dohnuts",
+  "distribution": "dohnuts",
+  "base_model": "Qwen/Qwen3.5-0.8B",
+  "base_revision": "2fc06364715b967f1860aea9cf38778875588b17",
+  "adapter": "qwen3.5",
+  "lora_rank": 8,
+  "image_pixels": 262144,
+  "max_length": 2048,
+  "backend": {
+    "linear_patch": true,
+    "triton_convolution": true,
+    "experimental_rocm_sdpa": true,
+    "fused_norm_and_swiglu": true,
+    "shared_prefix": true,
+    "frozen_vision_cache_MiB": 128
+  },
+  "inference": "merged LoRA, BF16, fused operations, shared-prefix parallel candidate scoring",
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