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vnmartinez/jev-local-alternatives
Notes on TypeSafe AI's Jev decision model and a local open-source zero-shot alternative (GLiClass, CPU)
View on GitHub ↗Link checked 2026-11-12 · HTTP 200 · opens the vendor's own site
Install & config
Install command from the repository
pip install torch
Parsed from the README we fetched on 2026-10-26; we have not executed it.
This is the install method the repository states, not an MCP client config, so no JSON is generated.
Key names differ by client: Clients & CLI
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- 0 ★ · vnmartinez/jev-local-alternatives · read from the GitHub search API · (checked 2026-11-13 20:00:50)
- Source
- read directly from its GitHub page · type: open-source project
- Harvest history
- first seen 2026-10-26 · last seen 2026-11-14 · seen 20× · listed by 1 sources
- Install
pip install torch
FAQ (answers come from our records)
How do I install it?
The repository provides this install command:
pip install torch. It comes from the repository itself; we cannot guarantee it runs as-is in your client.Who is it for?
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repository
vnmartinez/jev-local-alternatives；first catalogued 2026-10-26；last seen 2026-11-14。There is no official certification or quality rating here — we record sources and dates only.# jev-local-alternatives
以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →
Notes on Jev, TypeSafe AI's "System One" decision model, plus a runnable example of an open-source model you can run locally for the same kind of task.
> Research snapshot from October 2026. Jev performance and adoption figures are self-reported by TypeSafe. No independent benchmark or architecture paper existed at the time of writing.
## What Jev is
A discriminative model. It does not generate text. You give it unstructured context and typed questions (boolean, choice, ordered scale), and it returns a probability distribution with a confidence score for each.
Labels are supplied at inference time (zero-shot), so it needs no task-specific training.
Reportedly trained only on synthetic data with an unpublished method (RLCD) that targets calibrated probabilities. TypeSafe says it is transformer-based. Weights are closed.
Released 15 Sep 2026 as a hosted API only. There is no self-hosting.
Listed pricing: US$0.042 per 1M input tokens, output free. Claimed latency is 70–500 ms. Input is limited to 64k tokens.
Questions in a single request are answered in parallel, so dependent questions need sequential calls.
Competitor: OpenAI Decisions API (Oct 2026).
Beware of look-alike sites. A "Jev AI" site exists that is not affiliated with TypeSafe.
The name refers to the Jevons paradox: cheaper decisions should lead to more of them being automated.
## Can it run locally?
No. Jev is closed and hosted. The closest local option is an open-source zero-shot classifier:
| Model | License | Notes |
| GLiClass v3 (Knowledgator) | Apache-2.0 | All labels in one forward pass; scales to many labels |
| ModernBERT-large-zeroshot-v2.0 | see model card | NLI-style, 8k context |
| DeBERTa-v3-large-zeroshot-v2.0 | MIT | NLI-style; one pass per label, so it slows down with many labels |
| Small open LLM (Qwen, Llama) with constrained output | varies | Use per-option log-probs as scores; flexible but slower |
Key gap: none of these promise calibrated probabilities, which is Jev's main selling point. If you need calibration, fit temperature scaling on a labeled set of your own.
## Run the example (CPU is fine)
bash
python -m venv .venv
# Windows: .venv\Scripts\activate | Linux/macOS: source .venv/bin/activate
pip install torch --index-url https://download.pytorch.org/whl/cpu
pip install -r requirements.txt
python classify.py
classify.py answers a typed question (support-ticket routing) for a few sample texts, prints a score for each label, and reports latency.
> Research snapshot from October 2026. Jev performance and adoption figures are self-reported by TypeSafe. No independent benchmark or architecture paper existed at the time of writing.
## What Jev is
A discriminative model. It does not generate text. You give it unstructured context and typed questions (boolean, choice, ordered scale), and it returns a probability distribution with a confidence score for each.
Labels are supplied at inference time (zero-shot), so it needs no task-specific training.
Reportedly trained only on synthetic data with an unpublished method (RLCD) that targets calibrated probabilities. TypeSafe says it is transformer-based. Weights are closed.
Released 15 Sep 2026 as a hosted API only. There is no self-hosting.
Listed pricing: US$0.042 per 1M input tokens, output free. Claimed latency is 70–500 ms. Input is limited to 64k tokens.
Questions in a single request are answered in parallel, so dependent questions need sequential calls.
Competitor: OpenAI Decisions API (Oct 2026).
Beware of look-alike sites. A "Jev AI" site exists that is not affiliated with TypeSafe.
The name refers to the Jevons paradox: cheaper decisions should lead to more of them being automated.
## Can it run locally?
No. Jev is closed and hosted. The closest local option is an open-source zero-shot classifier:
| Model | License | Notes |
| GLiClass v3 (Knowledgator) | Apache-2.0 | All labels in one forward pass; scales to many labels |
| ModernBERT-large-zeroshot-v2.0 | see model card | NLI-style, 8k context |
| DeBERTa-v3-large-zeroshot-v2.0 | MIT | NLI-style; one pass per label, so it slows down with many labels |
| Small open LLM (Qwen, Llama) with constrained output | varies | Use per-option log-probs as scores; flexible but slower |
Key gap: none of these promise calibrated probabilities, which is Jev's main selling point. If you need calibration, fit temperature scaling on a labeled set of your own.
## Run the example (CPU is fine)
bash
python -m venv .venv
# Windows: .venv\Scripts\activate | Linux/macOS: source .venv/bin/activate
pip install torch --index-url https://download.pytorch.org/whl/cpu
pip install -r requirements.txt
python classify.py
classify.py answers a typed question (support-ticket routing) for a few sample texts, prints a score for each label, and reports latency.
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- Source
- github · https://github.com/vnmartinez/jev-local-alternatives
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