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You can run typed, probability-scored LLM decisions on your own hardware today, and several open projects do it. No open project is a verified drop-in for Jev. The projects use different methods, publish different benchmarks, and leave the hardest part to you: deciding whether the probabilities are trustworthy enough to act on. The practical choice is among four approaches, and each one trades setup effort against calibration, latency and hardware.
What a Jev-style decision interface does
A Jev-style or System One decision interface takes a context, often called the state, and a typed question with a bounded set of answers. The answer may be one option from a list, a yes or no, or a score over ordered levels such as low, medium and high. Often each option carries a probability.
That shape suits an agent or application that needs a small decision, such as approve, escalate or call a tool, rather than a paragraph it then has to parse. Implementations still differ. Some score next-token options from a general model, some use a model trained for the decision, some rotate the option order, and some add post-hoc calibration.
Compatibility is not equivalence
A published comparison of Jev alternatives sorts the field into open reproductions, zero-shot classifiers, and structured-output libraries. The same comparison states that the alternatives do not reproduce Jev’s training method. Some projects imitate Jev’s API; others share only the general idea of a typed decision.
#1 Best Overall
Matching request and response shapes tells you the calling code will run. It does not tell you that answers agree with Jev’s, or that the probabilities carry the same meaning.
Four approaches to a local typed decision
Read constrained answer probabilities from an open-weight LLM
Open Alternative to Jev reads probabilities for the typed choices directly from an open-weight model. Its repository documents two processing modes. In packed mode, several questions share one prompt, and the repository states that a packed answer can depend on neighboring questions in 6–9% of cases. Where batch neighbors must not influence an answer, the separate mode is the one to use.
Use a training-free adapter with bias correction
AnyJev’s Decider class reads a model’s next-token distribution without any training. Its L0 method averages scores over rotations of the option order and divides out the label prior, meaning it removes the model’s built-in preference for certain labels. The repository describes the method as requiring no labels. It also describes Tacit checkpoints, trained by self-distillation for one-pass decisions, and an optional escalation path that allows a capped reasoning step.
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Rank #2
Use a model fine-tuned for typed decisions
Decider’s repository describes Qwen3.5-based models fine-tuned for one-pass typed decisions. Von describes a compact local decision model with a Jev-shaped service interface. openJev-verdict-2.0 describes a 151-million-parameter non-autoregressive model, which produces its answer without generating text token by token. A model this small trades breadth for footprint, so test it on the decisions your application actually makes.
Repurpose a general model or classifier
Some projects read constrained output logits from a general model, or adapt a classifier or natural-language-inference model to choose among labels. This removes text generation and output parsing from the stack. The catch is the word “probability.” In these setups it often means a normalized preference over the options you supplied, not a measured chance of being right.
Side-by-side comparison
“Not stated” means the project’s description, as cited here, does not give that value.
| Approach | Example projects | Where the probability comes from | Runtime and compatibility | Calibration evidence | Latency and hardware evidence |
|---|---|---|---|---|---|
| Constrained answer probabilities from an open-weight LLM | Open Alternative to Jev | Probabilities over the allowed option tokens | Hugging Face Transformers and vLLM; chat template may need customizing | Repository states raw probabilities are overconfident; see calibration section | 582 ms per case on one H200 MIG slice (27B model, 8-bit); about 30 GB CUDA GPU memory for that benchmark run |
| Training-free adapter with bias correction | AnyJev (Decider, L0) | Next-token distribution, averaged over option rotations, with label prior divided out | Not stated | Reported in its own experiment; see benchmark section | Not stated |
| Fine-tuned decision model | Decider’s Qwen3.5-based models; Von; openJev-verdict-2.0 | Model trained to output the decision in one pass | Not stated; Von describes a Jev-shaped service interface | Not stated | openJev-verdict-2.0: 151 million parameters; others not stated |
| Repurposed general model or classifier | Projects reading output logits or adapting classifier or NLI models | Normalized preference over supplied options | Depends on the project | Not stated; probability may not be calibrated | Not stated |
Choosing by constraint
- No labeled examples yet. The training-free adapter is the only approach here whose method needs no labels. Use it to prototype, but you still need labels to learn whether its answers and probabilities hold on your task.
- A CUDA GPU and a 27B-class model are available. The constrained-probability approach has the most specific documentation in this set, including runtimes, template rules and processing modes.
- Footprint matters more than breadth. A compact fine-tuned model is the only approach with a documented parameter count under 200 million. Its benchmark figures are project-reported.
- You already run a general model and the option set is small and fixed. Reading its logits avoids a second model. Verify what the probability means before you set a threshold on it.
How to compare candidates on your own task
Run the same labeled examples through each candidate, on the same runtime where possible.
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- Measure accuracy by case type. Include the hard cases your application will actually meet, not only the easy majority.
- Check calibration. Group predictions into probability bins and compare each bin’s stated probability with its observed accuracy. Expected calibration error (ECE) is the weighted average of those gaps. If the bins show overconfidence, fit a temperature on the validation set and re-measure on the test set.
- Test option order. Score each case with options in their original order, reversed, and rotated. Count how often the chosen answer changes.
- Test batch effects. Score each question alone, then inside a batch of unrelated questions. Any change in the answer means neighbors are influencing the result.
- Measure latency on the target machine. Include model loading, batching, prompt length and serving overhead, not just the forward pass.
- Check deployment fit. Confirm the model license, the runtime, and the constraints in the deployment section below.
Calibration: a probability is not automatically a confidence
A normalized distribution can still be wrong about its own certainty. For example, if a model assigns 0.9 to answers that turn out right 70% of the time, it is overconfident, and a threshold set on those numbers will clear more often than the accuracy justifies. Open Alternative to Jev states that its raw probabilities are overconfident for this reason. Set thresholds from validation results rather than round numbers, and keep tracking live error rates after launch, because drift in the inputs changes calibration even when the model stays the same.
Published benchmark figures and what they show
Two projects publish figures against a baseline. Both are project-reported, and neither transfers automatically to another dataset, model size, language or prompt format.
Rank #4
Open Alternative to Jev against Jev 1.13.0
The Open Alternative to Jev repository reports results on its 400-case LocalLLaMA/typed-decisions benchmark. The stock Qwen3.6-27B ran through its library on one H200 MIG slice with 8-bit weights, in 2026. The Jev 1.13.0 column was measured by the repository’s authors through TypeSafe’s API on 2026-09-18.
| Metric | Stock Qwen3.6-27B via the library (repository, 2026) | Jev 1.13.0 via TypeSafe’s API (measured 2026-09-18) |
|---|---|---|
| Accuracy | 73.7% | 72.7% |
| KL divergence | 0.27 | 1.44 |
| Expected calibration error (ECE) | 0.020 | 0.144 |
| Time per case | 582 ms | 710 ms |
Read the gaps with their limits. On 400 cases, one percentage point is four answers, so the accuracy difference is small. The lower KL divergence and ECE are the larger differences in this table. The Jev figure came through an API, while the other ran on a single H200 MIG slice, so the latency gap compares serving paths as well as models. These numbers do not establish a general ranking.
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The AnyJev repository reports results for Qwen3-8B on the 20-way BANKING77 task, using 300 test items.
| Method | Accuracy | ECE |
|---|---|---|
| Raw logits | 0.747 | 0.240 |
| L0 | 0.803 | 0.184 |
The accuracy gap is about 17 items out of 300, and the calibration gain comes from one experiment on one dataset. The repository also reports that L0 reduces option-order flips relative to raw logits in that experiment. Those are project-specific results, and a different model or task can produce different results.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Hardware and runtime requirements
- The Open Alternative to Jev benchmark instructions say the Qwen3.6-27B run at 8-bit precision needs a CUDA GPU with about 30 GB of memory. That is the configuration for that benchmark, not a minimum for local typed decisions.
- The same repository separates absolute throughput from ratios between modes, and warns that its hardware and software setup affects absolute values. Compare ratios across modes on your own machine; absolute numbers from another machine may not carry over.
- Across the field, projects run on consumer GPUs, Apple Silicon, CUDA systems, and CPU-capable setups. No single minimum applies.
Choose the model and runtime first, then size memory for the exact checkpoint, quantization, context length and workload. This article does not cover current GPU models, prices or availability.
Quick Recap
Deployment constraints that change answers
- Option tokens. In the constrained-probability approach, each option letter must be a single token in the model’s tokenizer. Check this for every model you swap in.
- Chat template. The repository’s expected chat format may need customizing for other models’ templates. A mismatch may not raise an error, so it can go unnoticed.
- Pinned versions. Pin the Transformers version in production. Template changes can move the positions the model reads its answer from, so an upgrade can change decisions without any visible failure.
- Integration tests. Test the exact pinned model, runtime and prompt format together, against a fixed set of cases whose expected answers you have recorded.
What the evidence does and does not establish
- Established: four distinct approaches exist, and their projects document the constraints above.
- Not established: that any open project reproduces Jev’s training, matches its answers on your data, or produces probabilities calibrated for your threshold.
- Not established: market adoption. Repository stars are not evidence of adoption or quality.
- Volatile: repository contents, model versions, benchmark tables and hardware notes change. Check each project’s current documentation and the date of any figure before relying on it.
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