---
license: mit
language:
- en
library_name: gguf
pipeline_tag: text-classification
tags:
- gguf
- openvino
- llama.cpp
- decision-model
- yes-no
- zero-shot-classification
- cpu
- local
- on-device
- jev
---

<p align="center">
  <img src="https://raw.githubusercontent.com/feder-cr/jev/main/assets/jevos-logo.png" alt="jevos logo" width="140">
</p>

<h1 align="center">jevos-v4</h1>

**Decisions on a laptop CPU in 28–130 ms.** Send a text and a question (yes/no, multiple choice or a score)
and get back the probability of each answer, in TypeSafe Jev's API, on your own machine. Free and open source:
an alternative to Jev that runs locally.

<video controls autoplay loop muted playsinline width="100%"
  src="https://huggingface.co/feder-cr/jev/resolve/main/assets/jevos_vs_jev_snake.mp4"></video>

*Same Snake game, same questions for both: jevos-v4 on a laptop answers in about 20 ms, Jev's cloud API in
about 350 ms (mostly the network round trip), so the local snake makes many more moves in the same time.*

## Files

| File | What it is |
|---|---|
| `model/` | the OpenVINO INT8 model that the `jev` binary runs (CPU) |
| `jevos-v4-q4_k_m.gguf` | the same model as 4-bit GGUF, for llama.cpp and the tools built on it |
| `jevos-v4-q8_0.gguf` | the same model as 8-bit GGUF |

These are the same files as the [jevos-v4 release on GitHub](https://github.com/feder-cr/jev/releases/tag/jevos-v4);
the SHA-256 of each is in that release's `SHA256SUMS.txt`.

## Quickstart

Get the `jev` binary for your system from the [GitHub release](https://github.com/feder-cr/jev/releases/tag/jevos-v4)
(Windows, Linux, macOS on Apple silicon), then put the model next to it:

```bash
tar -xzf jev-linux-x64.tar.gz                 # Windows: unzip jev-windows-x64.zip
hf download feder-cr/jev --include "model/*" --local-dir jev
cd jev
./jev serve                                   # Windows: jev.exe serve
```

```bash
curl http://127.0.0.1:8017/v1/systemone -H 'Content-Type: application/json' -d '{
  "model": "jev-latest",
  "state": "I was charged twice for the same order.",
  "questions": {"billing": {"type": "noul", "instructions": "Is this a billing problem?"}}}'
```

```json
{"model": "jevos-v4", "answers": {"billing": {"type": "noul", "noul": 0.94}}, "usage": {"input_tokens": 27, "output_tokens": 0}}
```

One binary, CPU only, no Python.

## Benchmarks

<img src="https://raw.githubusercontent.com/feder-cr/jev/main/assets/jevos_tasks.png" alt="Latency on a short / long request: jevos-v4 28 / 130 ms; Jev 311 / 314 ms; Qwen3.5-4B 3,060 / 4,761 ms; Laya 129 / 480 ms. Accuracy on 6 tasks (Admission policy, Rental policy, Rules and scenarios, Authority rules, Fraud points, Patent phrases): jevos-v4 0.95, 0.76, 0.76, 0.81, 0.50, 0.37; Jev 1.00, 0.91, 0.88, 0.98, 0.69, 0.59; Qwen3.5-4B 0.82, 0.60, 0.72, 0.78, 0.45, 0.18; Laya 0.54, 0.31, 0.55, 0.64, 0.25, 0.32" width="100%" />

Same questions for every system, through the same HTTP client. Latency is the median of 10 requests on an
Intel Core Ultra 7 255H laptop, 16 threads, each text read from scratch. No task text was used to train
jevos; five of the six sets helped choose the released checkpoint.

| | **jevos-v4** | Jev | Laya |
|---|:---:|:---:|:---:|
| Yes/no questions | ✓ | ✓ | ✓ |
| Multiple choice | ✓ | ✓ | ✓ |
| Scores | ✓ (early) | ✓ | ✓ |
| Runs on | your machine | cloud | your machine |
| Cost | free | per token | free |
| Context | 8,192 tokens | not stated | 512 tokens |

jevos-v4 is much faster than the cloud API and free, but less accurate than Jev on the harder tasks above
(fraud points, patent phrases): check it on your own questions before relying on it.

## API

`POST /v1/systemone`, in TypeSafe Jev's wire format: code written for Jev's SDK works unchanged for yes/no
questions. One request can ask several questions about the same text, which is read once:

```json
{
  "model": "jev-latest",
  "state": {"item": "wireless mouse", "customer_message": "The box arrived empty. This is the second time!"},
  "questions": {
    "refund": {"type": "noul", "instructions": "Our policy refunds items reported missing within 30 days of delivery. Should this customer get a refund?"},
    "team": {"type": "choice", "instructions": "Which team should handle this message?",
             "criteria": {"billing": "payments, refunds", "shipping": "deliveries, missing parcels", "tech": "a product that does not work"}},
    "anger": {"type": "score", "instructions": "How angry is the customer?", "criteria": ["calm", "annoyed", "angry", "furious"]}
  }
}
```

A `noul` returns P(yes); a `choice` the most probable option; a `score` the expected level. Choices and scores
also return a probability per answer and a `confidence`: when it is low, send the case to a person. Put the
rule in the question, and do sums in code.

Options, memory use and building from source are in the [README on GitHub](https://github.com/feder-cr/jev#readme);
guides and measurements are in the [wiki](https://github.com/feder-cr/jev/wiki).

## License and authors

MIT. Built by [Federico Elia (@feder-cr)](https://github.com/feder-cr) together with
[Loris Salsi (@LosaLosSantos)](https://github.com/LosaLosSantos).