Metadata-Version: 2.4
Name: oev
Version: 0.2.0
Summary: System One decision model: typed questions in, calibrated probability distributions out, one forward pass.
Author-email: Divyanshu Dhruv <71079602+divyanshudhruv@users.noreply.github.com>
License: 
                                         Apache License
                                   Version 2.0, January 2004
                                http://www.apache.org/licenses/
        
           TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
        
           1. Definitions.
        
              "License" shall mean the terms and conditions for use, reproduction,
              and distribution as defined by Sections 1 through 9 of this document.
        
              "Licensor" shall mean the copyright owner or entity authorized by
              the copyright owner that is granting the License.
        
              "Legal Entity" shall mean the union of the acting entity and all
              other entities that control, are controlled by, or are under common
              control with that entity. For the purposes of this definition,
              "control" means (i) the power, direct or indirect, to cause the
              direction or management of such entity, whether by contract or
              otherwise, or (ii) ownership of fifty percent (50%) or more of the
              outstanding shares, or (iii) beneficial ownership of such entity.
        
              "You" (or "Your") shall mean an individual or Legal Entity
              exercising permissions granted by this License.
        
              "Source" form shall mean the preferred form for making modifications,
              including but not limited to software source code, documentation
              source, and configuration files.
        
              "Object" form shall mean any form resulting from mechanical
              transformation or translation of a Source form, including but
              not limited to compiled object code, generated documentation,
              and conversions to other media types.
        
              "Work" shall mean the work of authorship, whether in Source or
              Object form, made available under the License, as indicated by a
              copyright notice that is included in or attached to the work
              (an example is provided in the Appendix below).
        
              "Derivative Works" shall mean any work, whether in Source or Object
              form, that is based on (or derived from) the Work and for which the
              editorial revisions, annotations, elaborations, or other modifications
              represent, as a whole, an original work of authorship. For the purposes
              of this License, Derivative Works shall not include works that remain
              separable from, or merely link (or bind by name) to the interfaces of,
              the Work and Derivative Works thereof.
        
              "Contribution" shall mean any work of authorship, including
              the original version of the Work and any modifications or additions
              to that Work or Derivative Works thereof, that is intentionally
              submitted to Licensor for inclusion in the Work by the copyright owner
              or by an individual or Legal Entity authorized to submit on behalf of
              the copyright owner. For the purposes of this definition, "submitted"
              means any form of electronic, verbal, or written communication sent
              to the Licensor or its representatives, including but not limited to
              communication on electronic mailing lists, source code control systems,
              and issue tracking systems that are managed by, or on behalf of, the
              Licensor for the purpose of discussing and improving the Work, but
              excluding communication that is conspicuously marked or otherwise
              designated in writing by the copyright owner as "Not a Contribution."
        
              "Contributor" shall mean Licensor and any individual or Legal Entity
              on behalf of whom a Contribution has been received by Licensor and
              subsequently incorporated within the Work.
        
           2. Grant of Copyright License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              copyright license to reproduce, prepare Derivative Works of,
              publicly display, publicly perform, sublicense, and distribute the
              Work and such Derivative Works in Source or Object form.
        
           3. Grant of Patent License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              (except as stated in this section) patent license to make, have made,
              use, offer to sell, sell, import, and otherwise transfer the Work,
              where such license applies only to those patent claims licensable
              by such Contributor that are necessarily infringed by their
              Contribution(s) alone or by combination of their Contribution(s)
              with the Work to which such Contribution(s) was submitted. If You
              institute patent litigation against any entity (including a
              cross-claim or counterclaim in a lawsuit) alleging that the Work
              or a Contribution incorporated within the Work constitutes direct
              or contributory patent infringement, then any patent licenses
              granted to You under this License for that Work shall terminate
              as of the date such litigation is filed.
        
           4. Redistribution. You may reproduce and distribute copies of the
              Work or Derivative Works thereof in any medium, with or without
              modifications, and in Source or Object form, provided that You
              meet the following conditions:
        
              (a) You must give any other recipients of the Work or
                  Derivative Works a copy of this License; and
        
              (b) You must cause any modified files to carry prominent notices
                  stating that You changed the files; and
        
              (c) You must retain, in the Source form of any Derivative Works
                  that You distribute, all copyright, patent, trademark, and
                  attribution notices from the Source form of the Work,
                  excluding those notices that do not pertain to any part of
                  the Derivative Works; and
        
              (d) If the Work includes a "NOTICE" text file as part of its
                  distribution, then any Derivative Works that You distribute must
                  include a readable copy of the attribution notices contained
                  within such NOTICE file, excluding those notices that do not
                  pertain to any part of the Derivative Works, in at least one
                  of the following places: within a NOTICE text file distributed
                  as part of the Derivative Works; within the Source form or
                  documentation, if provided along with the Derivative Works; or,
                  within a display generated by the Derivative Works, if and
                  wherever such third-party notices normally appear. The contents
                  of the NOTICE file are for informational purposes only and
                  do not modify the License. You may add Your own attribution
                  notices within Derivative Works that You distribute, alongside
                  or as an addendum to the NOTICE text from the Work, provided
                  that such additional attribution notices cannot be construed
                  as modifying the License.
        
              You may add Your own copyright statement to Your modifications and
              may provide additional or different license terms and conditions
              for use, reproduction, or distribution of Your modifications, or
              for any such Derivative Works as a whole, provided Your use,
              reproduction, and distribution of the Work otherwise complies with
              the conditions stated in this License.
        
           5. Submission of Contributions. Unless You explicitly state otherwise,
              any Contribution intentionally submitted for inclusion in the Work
              by You to the Licensor shall be under the terms and conditions of
              this License, without any additional terms or conditions.
              Notwithstanding the above, nothing herein shall supersede or modify
              the terms of any separate license agreement you may have executed
              with Licensor regarding such Contributions.
        
           6. Trademarks. This License does not grant permission to use the trade
              names, trademarks, service marks, or product names of the Licensor,
              except as required for reasonable and customary use in describing the
              origin of the Work and reproducing the content of the NOTICE file.
        
           7. Disclaimer of Warranty. Unless required by applicable law or
              agreed to in writing, Licensor provides the Work (and each
              Contributor provides its Contributions) on an "AS IS" BASIS,
              WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
              implied, including, without limitation, any warranties or conditions
              of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
              PARTICULAR PURPOSE. You are solely responsible for determining the
              appropriateness of using or redistributing the Work and assume any
              risks associated with Your exercise of permissions under this License.
        
           8. Limitation of Liability. In no event and under no legal theory,
              whether in tort (including negligence), contract, or otherwise,
              unless required by applicable law (such as deliberate and grossly
              negligent acts) or agreed to in writing, shall any Contributor be
              liable to You for damages, including any direct, indirect, special,
              incidental, or consequential damages of any character arising as a
              result of this License or out of the use or inability to use the
              Work (including but not limited to damages for loss of goodwill,
              work stoppage, computer failure or malfunction, or any and all
              other commercial damages or losses), even if such Contributor
              has been advised of the possibility of such damages.
        
           9. Accepting Warranty or Additional Liability. While redistributing
              the Work or Derivative Works thereof, You may choose to offer,
              and charge a fee for, acceptance of support, warranty, indemnity,
              or other liability obligations and/or rights consistent with this
              License. However, in accepting such obligations, You may act only
              on Your own behalf and on Your sole responsibility, not on behalf
              of any other Contributor, and only if You agree to indemnify,
              defend, and hold each Contributor harmless for any liability
              incurred by, or claims asserted against, such Contributor by reason
              of your accepting any such warranty or additional liability.
        
           END OF TERMS AND CONDITIONS
        
           APPENDIX: How to apply the Apache License to your work.
        
              To apply the Apache License to your work, attach the following
              boilerplate notice, with the fields enclosed by brackets "[]"
              replaced with your own identifying information. (Don't include
              the brackets!)  The text should be enclosed in the appropriate
              comment syntax for the file format. We also recommend that a
              file or class name and description of purpose be included on the
              same "printed page" as the copyright notice for easier
              identification within third-party archives.
        
           Copyright [yyyy] [name of copyright owner]
        
           Licensed under the Apache License, Version 2.0 (the "License");
           you may not use this file except in compliance with the License.
           You may obtain a copy of the License at
        
               http://www.apache.org/licenses/LICENSE-2.0
        
           Unless required by applicable law or agreed to in writing, software
           distributed under the License is distributed on an "AS IS" BASIS,
           WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
           See the License for the specific language governing permissions and
           limitations under the License.
        
        Copyright 2026 Divyanshu Dhruv
        
        Licensed under the Apache License, Version 2.0 (the "License");
        you may not use this file except in compliance with the License.
        You may obtain a copy of the License at
        
            http://www.apache.org/licenses/LICENSE-2.0
        
        Unless required by applicable law or agreed to in writing, software
        distributed under the License is distributed on an "AS IS" BASIS,
        WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
        See the License for the specific language governing permissions and
        limitations under the License.
        
Project-URL: Homepage, https://github.com/divyanshudhruv/oev
Project-URL: Model, https://huggingface.co/divyanshudhruv/oev-typed
Keywords: decision-model,calibration,classification,system-one,deberta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch>=2.2
Requires-Dist: numpy>=1.26
Provides-Extra: dev
Requires-Dist: pytest>=8; extra == "dev"
Provides-Extra: data
Requires-Dist: datasets>=2.14; extra == "data"
Provides-Extra: backbone
Requires-Dist: transformers>=4.40; extra == "backbone"
Requires-Dist: sentencepiece; extra == "backbone"
Requires-Dist: protobuf; extra == "backbone"
Provides-Extra: serve
Requires-Dist: fastapi>=0.110; extra == "serve"
Requires-Dist: uvicorn>=0.29; extra == "serve"
Provides-Extra: app
Requires-Dist: gradio<7,>=6.0; extra == "app"
Requires-Dist: huggingface_hub>=0.23; extra == "app"
Requires-Dist: transformers>=4.40; extra == "app"
Requires-Dist: sentencepiece; extra == "app"
Requires-Dist: protobuf; extra == "app"
Dynamic: license-file

<p align="center" style="margin: 24px 0;">
<picture>
  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/divyanshudhruv/oev/main/assets/logo_transp.png" />
  <img src="https://raw.githubusercontent.com/divyanshudhruv/oev/main/assets/logo_transp.png" alt="OEV" width="150" />
</picture>
</p>

<div align="center">

OEV is a small (`184M params`) neural decision engine. Instead of generating text, it scores answer options directly. The state, the question, and every option are packed into one sequence. One forward pass returns a calibrated probability distribution.

The single `184M` model scores `0.7705` on typed-decisions, slightly above laya's published `0.766` from a `421M` checkpoint. An ensemble of four `184M` checkpoints reaches `0.7760`, the `highest` reported result, and a single Banking77 soup checkpoint reaches `0.8584` (best ECE `0.0595` from the 3-checkpoint ensemble). Jev leads only on Banking77 (0.870).

[![Hugging Face Model](https://img.shields.io/badge/%F0%9F%A4%97%20Model-divyanshudhruv%2Foev--typed-blue)](https://huggingface.co/divyanshudhruv/oev-typed)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
[![Tests](https://img.shields.io/badge/tests-passing-brightgreen)](https://github.com/divyanshudhruv/oev/actions/workflows/tests.yml)
[![Python](https://img.shields.io/badge/python-3.10%2B-blue)](https://www.python.org/downloads/)
[![PyTorch](https://img.shields.io/badge/PyTorch-2.x-ee4c2c)](https://pytorch.org/get-started/locally/)
[![HF Space](https://img.shields.io/badge/%F0%9F%A4%97%20Space-oev--demo-yellow)](https://huggingface.co/spaces/divyanshudhruv/oev-demo)
[![PyPI](https://img.shields.io/pypi/v/oev)](https://pypi.org/project/oev/)

</div>

<p align="center" style="margin: 24px 0;">
<picture>
  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/divyanshudhruv/oev/main/assets/benchmarks_dark.png" />
  <img src="https://raw.githubusercontent.com/divyanshudhruv/oev/main/assets/benchmarks.png" alt="OEV vs Jev and laya on shared public benchmarks" width="92%" />
</picture>
</p>

<p align="center" style="margin: 24px 0;">
<picture>
  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/divyanshudhruv/oev/main/assets/oev_vs_jev_full_dark.png" />
  <img src="https://raw.githubusercontent.com/divyanshudhruv/oev/main/assets/oev_vs_jev_full.png" alt="OEV versus TypeSafe Jev: accuracy on shared public datasets, every application workflow, speed, calibration, size, and soft-accuracy sharpening" width="95%" />
</picture>
</p>

<p align="center" style="margin: 24px 0;">
<picture>
  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/divyanshudhruv/oev/main/assets/transfer_speed_dark.png" />
  <img src="https://raw.githubusercontent.com/divyanshudhruv/oev/main/assets/transfer_speed.png" alt="Left: zero-shot and out-of-domain transfer, OEV distilled student beats laya zero-shot on emotion with the NLI floor documented; right: latency, OEV 22.2ms on T4 vs laya, Kev-4B and Jev" width="100%" />
</picture>
</p>

## At a glance

| claim                 | result                                                                                                  |
| --------------------- | ------------------------------------------------------------------------------------------------------- |
| best accuracy         | **0.7705** single model, **0.7760** ensemble - typed-decisions (laya 0.766 from 421M)                   |
| high-cardinality      | **0.8584** Banking77, one soup checkpoint (`ECE 0.0595` best ensemble; laya 0.425)                      |
| speed                 | **22.2 ms** single question (laya 32.8-39.5 ms published range)                                         |
| size                  | **184M** params, 0.44x laya                                                                             |
| weights & checkpoints | Apache 2.0 - [huggingface.co/divyanshudhruv/oev-typed](https://huggingface.co/divyanshudhruv/oev-typed) |

- `22.2 ms` per question on a `T4` (GPU); `447 ms` p50 on CPU (8 threads, 184M soup checkpoint)
- `0.8584` on 77-label `Banking77` from a single soup checkpoint (the 3-checkpoint ensemble still holds best ECE `0.0595`): each option is embedded as its own anchor with full tokens, so accuracy scales with label count (gap to Jev 1.16 pts)
- `184M` params, `Apache 2.0` weights
- Kev (0.8B / 4B) publishes no in-domain numbers on these datasets, so it is not in the tables; see [BENCHMARKS.md](BENCHMARKS.md) for the like-for-like comparison plan

> [!WARNING]
> Chart latency comparisons use different hardware and include published ranges; the current `runs/` manifest and raw timing samples are also unavailable here. The sharpening panel in the comparison figure shows historical gamma `2.5` values from an evaluation sweep; they are exploratory and not release claims. Reproduce claims from recorded run logs before treating them as release evidence.

## Architecture

```mermaid
flowchart LR
    subgraph input["Input"]
        direction TB
        S["state<br/>text or JSON"]
        Q["questions<br/>choice, noul, score"]
    end

    P["packer<br/>state + questions + anchors<br/>one packed sequence"]

    subgraph pass["One forward pass, 22 ms on T4"]
        direction TB
        E["encoder<br/>DeBERTa-v3-base, 184M<br/>or the from-scratch char model"]
        H["shared linear head<br/>scores every anchor"]
    end

    D["softmax per question<br/>calibrated distribution"]
    O["outputs<br/>choice / noul / score"]

    S --> P
    Q --> P
    P --> E
    E --> H
    H --> D
    D --> O
```

<details>
<summary>Vertical layout</summary>

```mermaid
flowchart TB
    subgraph input["Input"]
        direction TB
        S["state<br/>text or JSON"]
        Q["questions<br/>choice, noul, score"]
    end

    P["packer<br/>state + questions + anchors<br/>one packed sequence"]

    subgraph pass["One forward pass, 22 ms on T4"]
        direction TB
        E["encoder<br/>DeBERTa-v3-base, 184M<br/>or the from-scratch char model"]
        H["shared linear head<br/>scores every anchor"]
    end

    D["softmax per question<br/>calibrated distribution"]
    O["outputs<br/>choice / noul / score"]

    S --> P
    Q --> P
    P --> E
    E --> H
    H --> D
    D --> O
```

</details>

- **One anchor mechanism** covers all three primitives - options, yes/no pairs, and score levels are each embedded as anchors in one packed sequence
- **No text generation** - nothing to parse, nothing to hallucinate
- **New question types need no new heads** - new options are just new anchors

## Benchmarks: OEV vs the published field

Fine-tuned on each benchmark's train split, following the same protocol as Laya's published runs. Selected results and evaluation notes are in [BENCHMARKS.md](BENCHMARKS.md).

> [!WARNING]
> Banking77 uses 77 OEV labels, while the published Jev figure is from a 72-label configuration. The `0.8584` and `0.870` values are not a controlled head-to-head comparison.

| benchmark       |                     OEV |  laya |   Jev | note                                                                  |
| --------------- | ----------------------: | ----: | ----: | --------------------------------------------------------------------- |
| typed-decisions |              **0.7760** | 0.766 | 0.727 | highest reported (ensemble); single model 0.7705                      |
| Banking77       |              **0.8584** | 0.425 | 0.870 | 2x laya; gap to Jev 1.16 pts                                          |
| AG News         |              **0.9489** | 0.950 | 0.910 | label-noise ceiling (~0.95)                                           |
| DAIR Emotion    | **0.9300** (fine-tuned) | 0.595 | 0.480 | zero-shot: OEV student **`0.6505`** beats laya's `0.595` head-to-head |

<p align="center" style="margin: 24px 0;">
<picture>
  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/divyanshudhruv/oev/main/assets/headline_scorecard_dark.png" />
  <img src="https://raw.githubusercontent.com/divyanshudhruv/oev/main/assets/headline_scorecard.png" alt="OEV headline results: typed-decisions accuracy, Banking77 accuracy, and hardware-separated latency" width="100%" />
</picture>
</p>

<p align="center" style="margin: 24px 0;">
<picture>
  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/divyanshudhruv/oev/main/assets/zeroshot_dark.png" />
  <img src="https://raw.githubusercontent.com/divyanshudhruv/oev/main/assets/zeroshot.png" alt="Zero-shot and out-of-domain transfer as dot pairs: OEV 0.650 versus laya 0.595 on emotion, with the WANLI and ANLI floors marked" width="49%" />
</picture>
<picture>
  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/divyanshudhruv/oev/main/assets/latency_profile_dark.png" />
  <img src="https://raw.githubusercontent.com/divyanshudhruv/oev/main/assets/latency_profile.png" alt="OEV latency on Tesla T4 and CPU, hardware separated; batch value is per-question throughput" width="49%" />
</picture>
</p>

<p align="center" style="margin: 24px 0;">

<picture>
  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/divyanshudhruv/oev/main/assets/workflows_dark.png" />
  <img src="https://raw.githubusercontent.com/divyanshudhruv/oev/main/assets/workflows.png" alt="Typed-decisions accuracy per workflow: OEV wins invoice processing, customer service and agent-trace observability; laya wins security incidents" width="49%" />
</picture><picture>
  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/divyanshudhruv/oev/main/assets/decision_primitives_dark.png" />
  <img src="https://raw.githubusercontent.com/divyanshudhruv/oev/main/assets/decision_primitives.png" alt="Illustrative normalized distributions for OEV choice, noul, and score decision primitives" width="50%" />
</picture>
</p>

## Quickstart

```bash
pip install oev          # from PyPI
# or from source:
pip install -e .
```

Optional extras:

- `pip install -e ".[dev]"` - pytest
- `pip install -e ".[data]"` - dataset converters
- `pip install -e ".[backbone]"` - DeBERTa fine-tuning
- `pip install -e ".[serve]"` - FastAPI server (`oev-serve`)
- `pip install -e ".[app]"` - Gradio Space dependencies

```python
from oev.infer import OEV

agent = OEV("checkpoints_td5/oev-tiny.pt", device="cpu")

result = agent.decide("We were charged twice for the same order.", {
    "department": {"type": "choice", "options": ["billing", "technical", "sales", "other"],
                   "instructions": "Which department should handle this?"},
    "refund_requested": {"type": "noul"},
    "severity": {"type": "score", "levels": [1, 2, 3, 4, 5]},
})
```

```json
{
  "department": {
    "choice": "billing",
    "probabilities": {
      "billing": 0.94,
      "technical": 0.04,
      "sales": 0.01,
      "other": 0.01
    },
    "confidence": 0.94
  },
  "refund_requested": 0.91,
  "severity": {
    "value": 3,
    "probabilities": { "1": 0.02, "2": 0.08, "3": 0.61, "4": 0.22, "5": 0.07 }
  }
}
```

_Confidence gating_ - automate when confident, escalate when not:

```python
from oev.presets import triage_questions, gate

for name, payload, confident in gate(result, threshold=0.85):
    automate(name, payload) if confident else escalate_to_human(name)
```

HTTP server (native + Jev-compatible `/v1/systemone` endpoint - TypeSafe clients work by changing baseUrl):

```bash
pip install -e ".[serve]"
oev-serve --checkpoint checkpoints_td5/oev-tiny.pt --port 8000
curl -X POST localhost:8000/decide -H "Content-Type: application/json" \
  -d '{"state": "My payment failed twice", "questions": {"urgency": {"type": "score", "levels": [1, 2, 3]}}}'
```

Docker:

```bash
docker compose up   # checkpoint at ./checkpoints/oev-tiny.pt
```

## Training

```bash
python -m oev.convert_typed

python -m oev.train --backbone microsoft/deberta-v3-base --epochs 4 --batch-size 8 --max-len 768 --data-dir data/typed --out checkpoints_td5

python -m oev.rlcd --checkpoint checkpoints_td5/oev-tiny.pt --data-dir data/typed --epochs 2 --batch-size 8 --out checkpoints_rlcd

python -m oev.ensemble --ckpts checkpoints_td5/oev-tiny.pt,checkpoints_rlcd/oev-tiny.pt,checkpoints_rlcd_soup/oev-tiny.pt,checkpoints_rlcd_seed1/oev-tiny.pt --data-dir data/typed

python -m pytest -q
```

`train_colab.ipynb` runs the entire pipeline end to end. Evaluation rules and selected limitations are in [BENCHMARKS.md](BENCHMARKS.md).

## Limitations

- every benchmark number is from a checkpoint fine-tuned on that benchmark's train split (the `0.9300` emotion figure included). Zero-shot emotion is a separate **win**: the shipped distilled student (`0.6505`, both models zero-shot) against laya's `0.595`, up from the round-1 starting point of `0.4265`
- ANLI remains near chance (`0.3380` for the round-1 student), while the WANLI specialist reaches `0.5645`; the NLI result is split-dependent, not uniformly at chance
- pure-mimicry distillation transfers breadth, not depth. The round-1 student hit emotion `0.6875` (+26 pts zero-shot) but lost typed skill (`0.5385`). Adding gold-CE loss (round 2) collapsed to uniform, a documented negative result. Round 2b (pure-KL, balanced domains) rescued it: typed `0.6480`, emotion `0.6505`, b77 `0.7964`, probes all PASS
- the headline ensemble result is an average of four checkpoints; the best single model is `0.7705`
- CPU inference is roughly `20x` slower than the T4 (`447 ms` p50, 8 threads) - all headline timings are GPU
- the b77 headline includes a 3-checkpoint probability ensemble and a single-file soup checkpoint at `0.8584`; `0.8403` is a historical warm-start re-tune, not the current best single artifact
- English only

## Roadmap

- [ ] round 3 distillation: 6 teachers, 5 domains including NLI
- [ ] 4-member Banking77 ensemble: the live shot past `0.8584`
- [ ] round 4: one file near specialist numbers everywhere
- [ ] INT8 / ONNX CPU deployment (export + quantization scripts in `scripts/`, bench pending)
- [ ] multi-question shared-state encoding (one pass, many questions)
- [ ] robustness: reduce mild overconfidence on out-of-distribution inputs
- [ ] non-English checkpoints (the interface is language-agnostic; the weights are not yet)

Full list: [ROADMAP.md](ROADMAP.md).

## Credits

The interface and benchmark protocol follow [Laya](https://github.com/NandhaKishorM/laya), [Kev](https://github.com/jaredpalmer/kev) and the System One model category introduced by TypeSafe's [Jev](https://typesafe.com). Their published numbers are quoted here for comparison and remain their measurements.
