---
license: apache-2.0
language:
  - en
  - zh
  - es
library_name: kev
base_model:
  - Qwen/Qwen3.5-9B-Base
  - jaredpalmer/kev-9b
pipeline_tag: text-classification
tags:
  - decision-model
  - qwen3.5
  - kev
  - choice
  - calibration
  - local-serving
---

# OpenJev 9B

OpenJev 9B is an open, locally deployable typed decision model for `choice`,
`noul` (yes/no/unknown-style decisions), and ordinal `score` questions over a
shared state. It uses Qwen3.5-9B-Base with Kev's pointer-style decision head and
a local Go API boundary that isolates sibling questions.

This release does **not** claim parity with hosted Jev. No Jev output was used
as a training label, soft target, synthetic teacher, calibration target,
selection signal, or fallback response. No fresh same-item Jev evaluation was
run for this checkpoint.

## Identity

| Field | Value |
|---|---|
| Model | `openjev-9b-hard-final-w25` |
| Base | `Qwen/Qwen3.5-9B-Base@68c46c4b3498877f3ef123c856ecfde50c39f404` |
| Kev code | `jaredpalmer/kev@0fe8fc97c2bcc247fa3efb6e5c32af4e99770e91` |
| Weights SHA256 | `67b87afe12689bf6a95f6669d64250eae70e8744d0700019220942396355f8b8` |
| Head SHA256 | `56910f6a89bfb0cc5642764d7f983f5bad3d025dabcbad38590765455e7622ad` |
| Temperature | `1.9543199368684914` |
| Precision | BF16 |
| Maximum state | 16,384 tokens in the validated serving contract |
| Question semantics | strict isolated execution |

The four `model-*.safetensors` files contain merged full weights. `head.pt`
contains the typed Kev head. `SHA256SUMS` binds every checkpoint file.

## Run locally

Use the exact Kev revision for the reference backend:

```bash
git clone https://github.com/jaredpalmer/kev.git
cd kev
git checkout 0fe8fc97c2bcc247fa3efb6e5c32af4e99770e91
uv sync --extra serve
KEV_TEMPERATURE=1.9543199368684914 \
  uv run --extra serve python -m kev.serve \
  --run iFurySt/openjev --port 8008
```

The Kev endpoint implements the System One-shaped decision API. The OpenJev
source tree additionally contains a Go boundary with input validation,
authentication, overload handling, question isolation, readiness, benchmarks,
and an offline deployment layout. It has no Jev key or cloud fallback path.

## Training method

The selected checkpoint has three relevant stages:

1. A rank-16 LoRA continuation mixed 8,000 audited local records with 8,000
   deterministic Kev replay records for one pass.
2. A hard-label full-weight BF16 continuation ran one pass over 11,320 records
   / 16,279 questions using six A100-SXM4-80GB GPUs, FSDP2, seed `92323`, and
   LR/head LR `1e-6`.
3. R2 froze six WiSE-FT candidates and selected a 75% local parent / 25%
   hard-final interpolation using DEVELOPMENT and CALIBRATION only. This stage
   adds no optimizer update or new label.

A matched Qwen3.5-27B teacher research arm was trained during R1, but the
released checkpoint uses the hard-label arm, not that teacher arm. Historical
K0 data did contain some local-27B soft targets; those response rows are not
redistributed and are disclosed as a byte-reproduction gap.

See `TRAINING.md` and `DATA.md` in this repository for the full round ledger,
data counts, source revisions, licenses, negative results, and hardware.

## Quality evidence

The independently opened capability-v1 confirmation contains 1,200 records /
1,920 questions. On the 1,800-question clean paired population:

| Model | Accuracy | Multiclass Brier | Coverage at <=5% empirical error |
|---|---:|---:|---:|
| OpenJev 9B | 0.93611 | 0.08726 | 0.97611 |
| K0 parent | 0.94556 | 0.07848 | 0.98944 |
| Raw Qwen3.5-9B scoring adapter | 0.81333 | 0.28569 | 0.30889 |

These are synthetic confirmation results, not proof of general intelligence or
Jev parity. A later corrected DEVELOPMENT-only comparison on true released
Kev-9B found OpenJev / Kev accuracy `0.757624 / 0.738898` and Brier
`0.328546 / 0.355000`; it is not a locked test.

## Serving measurements

The same multilingual three-question Go request was measured after two warmups
and 20 requests at each concurrency. Every one of 160 measured CPU/GPU requests
returned HTTP 200.

| Device | c1 throughput / p50 | c8 throughput / p50 | Peak model memory |
|---|---:|---:|---:|
| 16-core Xeon CPU | 0.5699 req/s / 1,692.7 ms | 0.5037 req/s / 15,678.8 ms | 13.7 GiB RSS |
| A100-SXM4-80GB | 10.7123 req/s / 85.7 ms | 20.0055 req/s / 342.7 ms | 23.6 GiB device |

CPU is appropriate for offline, fallback, or very low-QPS use. A GPU is
recommended for interactive concurrent serving. Discrete CPU/GPU decisions
matched; probability bits are not promised identical across devices or dynamic
batches.

## Data and reproducibility

OpenJev publishes source builders, pinned revisions, hashes, roles, and
attribution. Original deterministic fixtures can be released under their
declared terms. Third-party rows that require per-origin review, contain a
benchmark canary, or have share-alike/attribution constraints remain
download/build-recipe-only. Jev responses and private teacher-response ledgers
are excluded.

That means the model is open and independently verifiable, while exact
from-scratch byte reproduction still has a documented gap around historical K0
rows/targets. See `DATA.md`; do not infer that all upstream dataset rows are
owned by OpenJev.

## Limitations

- No fresh Jev baseline and no parity claim.
- The model inherits biases and factual limitations from Qwen, Kev, and the
  listed training sources.
- Numeric/date/business-day and long-context joins remain known weak areas in
  consumed reporting-only diagnostics; those rows were not mined for training.
- Concurrent GPU batching can change rounded probabilities by up to 0.0018 in
  the measured fixture without changing the discrete decision.
- Active GPU compute is not preempted on client cancellation; queued work can
  be cancelled cooperatively.
- The API is close to, but not certified identical to, every hosted Jev error,
  usage, refusal, and rate-limit behavior.

## License and attribution

OpenJev code and released weights are provided under Apache-2.0. This model is
derived from Apache-2.0 Qwen and Kev assets. See `LICENSE` and `NOTICE`.
“Jev” and “TypeSafe” are used only to describe compatibility and the research
target; this project is not affiliated with or endorsed by TypeSafe.