# AI use cases for Awesome Jev Gallery (OmniJev/awesome-jev-gallery) Repo: [OmniJev/awesome-jev-gallery](https://github.com/OmniJev/awesome-jev-gallery) | Use cases: 4 | Page: https://dedupedai.com/use-cases/omnijev-awesome-jev-gallery/ A curated list of papers, open models, benchmarks, commentary and projects tied to TypeSafe's System One models and Jev. This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL. - Pick a local open model for typed decisions: https://dedupedai.com/use-cases/omnijev-awesome-jev-gallery/pick-local-jev-model/ - Compare typed-decision models on shared benchmarks: https://dedupedai.com/use-cases/omnijev-awesome-jev-gallery/compare-typed-decision-models/ - Route Thai or Japanese intents with a small model: https://dedupedai.com/use-cases/omnijev-awesome-jev-gallery/route-thai-japanese-intents/ - Have an AI agent design a System One workflow: https://dedupedai.com/use-cases/omnijev-awesome-jev-gallery/design-system-one-workflow-with-agent/ ## How to pick a local open model for typed decisions with Awesome Jev Gallery Repo: [OmniJev/awesome-jev-gallery](https://github.com/OmniJev/awesome-jev-gallery) | Difficulty: Intermediate | Time: 60 minutes | Basis: From the README Match your hardware to a listed open decision model and check its README before running it. **Who it is for:** A developer who wants typed decisions without a cloud API and has a specific GPU or Mac. **The problem:** The Open Source section lists many models with different hardware targets, sizes and licenses, and it is hard to tell which one fits. **The outcome:** A shortlist of one or two models that match your hardware, with the license and setup notes read from each repo. ### Steps 1. Open the Open Source section of the Awesome JEV README. 2. Match your hardware to an entry: Rizzo Flow for local runs through llama.cpp, or Laya-MLX or Laya Ultrafast for a local MLX port on Apple Silicon. 3. Open that repo and read its README for setup steps the list does not cover. 4. Follow the Hugging Face link on the entry to find the model weights. 5. Send one typed question with a fixed option set and check that you get one probability per option. ### Prompt to paste into your agent ```text Using the Awesome JEV list at https://github.com/OmniJev/awesome-jev-gallery, find the Open Source entry that matches , read its README, and report the setup steps and the output format for one typed decision. Verify that the README states a license before you recommend the entry. ``` ### Caveat Many entries are unofficial or independent projects, and the speed and accuracy figures are reported by their authors, not verified here. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/omnijev-awesome-jev-gallery/pick-local-jev-model/ ## How to compare typed-decision models on shared benchmarks with Awesome Jev Gallery Repo: [OmniJev/awesome-jev-gallery](https://github.com/OmniJev/awesome-jev-gallery) | Difficulty: Advanced | Time: 180 minutes | Basis: From the README Use the Benchmark and Leaderboard section to run the same public tasks on two candidate models. **Who it is for:** An ML engineer choosing between open decision models for a production classifier or router. **The problem:** Each project reports its own scores on different test sets, so the numbers cannot be compared directly. **The outcome:** A side-by-side table of accuracy on the same public task set for your two shortlisted models. ### Steps 1. Open the Benchmark and Leaderboard section and list the benchmark entries. 2. Note that Open-Jev reports 179 of 231 public JevBench tasks answered correctly by its 9B model, according to its authors. 3. Find the 13-subset public evaluation suite linked from the Bespoke Nimble entry. 4. Run the same tasks on each candidate model and record the probability each option receives. 5. Compare accuracy on the shared tasks and note which model fails on which task type. ### Prompt to paste into your agent ```text Using the Benchmark and Leaderboard section of https://github.com/OmniJev/awesome-jev-gallery, collect the public evaluation suites and their task counts, then produce a table of and accuracy on one shared suite. Verify that both models were scored on identical tasks before you report a difference. ``` ### Caveat The list does not publish one verified leaderboard, so every score here is author-reported on that author's own test set. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/omnijev-awesome-jev-gallery/compare-typed-decision-models/ ## How to route Thai or Japanese intents with a small model with Awesome Jev Gallery Repo: [OmniJev/awesome-jev-gallery](https://github.com/OmniJev/awesome-jev-gallery) | Difficulty: Intermediate | Time: 120 minutes | Basis: From the README Use the Thai and Japanese decision models to score fixed intent labels for local-language messages. **Who it is for:** A product team that receives customer messages in Thai or Japanese and needs to route them to fixed categories. **The problem:** Routing messages with a general chat model is slow to tune and gives free-form answers instead of fixed label probabilities. **The outcome:** A small model that returns one probability per intent label for each message, tested on your own sample. ### Steps 1. Open the OpenThai-SystemOne entry for a Thai and English 0.8B model with a 256-way slot head. 2. Open the sokudan entry for a Japanese 314.6M ModernBERT-ja model that returns choice, score and bool probabilities. 3. Map your intent labels to a fixed option set for each language. 4. Test each model on a held-out sample of your own messages. 5. Check that the top label matches the intent you expect for most messages before routing live traffic. ### Prompt to paste into your agent ```text Using the OpenThai-SystemOne and sokudan entries in https://github.com/OmniJev/awesome-jev-gallery, map to a fixed option set and score a held-out sample of messages. Report the label probabilities for each message. Verify that the labels cover every intent you plan to route. ``` ### Caveat The reported accuracies are on the authors' own held-out sets, so test on messages from your own product before relying on them. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/omnijev-awesome-jev-gallery/route-thai-japanese-intents/ ## How to have an AI agent design a System One workflow with Awesome Jev Gallery Repo: [OmniJev/awesome-jev-gallery](https://github.com/OmniJev/awesome-jev-gallery) | Difficulty: Intermediate | Time: 60 minutes | Basis: From the README Install TypeSafe's agent skill files so Claude Code or Codex can draft a typed System One workflow. **Who it is for:** A developer who already uses Claude Code or Codex and wants to start a System One workflow for a decision task. **The problem:** System One uses typed question shapes and probability outputs that are unfamiliar, so a first workflow takes time to design. **The outcome:** A draft workflow with a typed question, a fixed option set and the primitive to use, reviewed by you. ### Steps 1. Open the TypeSafe Agent Skills repo linked from the System One section. 2. Install the skill files for your agent by following the install steps in that repo's README. 3. Read the Primitives page to see the Choice, Score and Noul question shapes. 4. Ask your agent to draft a workflow for your decision task using one of those primitives. 5. Check that the draft has a fixed option set and that each option gets its own probability. ### Prompt to paste into your agent ```text Using the skill files in https://github.com/typesafe-ai/skills, design a System One workflow for using the Choice, Score or Noul primitive. Output the typed question and the fixed option set. Verify that every option is listed and that the output asks for a probability per option. ``` ### Caveat The skill files help an agent draft workflows, but the README does not explain API access, so check TypeSafe's docs for how to call System One. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/omnijev-awesome-jev-gallery/design-system-one-workflow-with-agent/