Model · Text classification
Kitani
Experimental open-weights judgment model by Kitani OpenJudgement is unfinished. We're releasing this checkpoint for people to experiment with, inspect, and build on. It still needs work on judgment quality, calibration, and inference efficiency. It is not as good as Jev overall in our internal task comparisons. It does show a meaningful improvement over untouched Qwen on our recorded validation comparison: 75.4% versus 64.2% annotation agreement. That is a result on a particular evaluation set, not a claim that we beat the base model on every task. There are questions it handles well and questions it confidently gets wrong. Please judge the preview by your own examples rather than assuming…
Open weights
apache-2.0
4.5B parameters
262,144 tokens
transformers
A calibrated typed-decision model: give it a state (text, ticket, policy, JSON) and a typed question — choice, boolean, or rubric score — and it returns a probability for every option in a single forward pass (~24 ms). No generation, no parsing, nothing to hallucinate. 12 of 13 subsets exceed Bespoke Nimble-9B — a model 2.2× its size — same prompt format, same scoring protocol. The primary suite: BoolQ, MultiNLI, PAWS, PubMedQA, SQuAD-2, VitaminC, Civil Comments, Aegis 2.0, MASSIVE (en/de), HelpSteer-2, SummEval (consistency / relevance). Every item human-labeled; byte-reproducible (manifest-locked ids + sha256); same protocol as the Bespoke Nimble evaluation. Wins: verification-style noul…
Open weights
apache-2.0
4.5B parameters
262,144 tokens
transformers
100x faster than generative LLMs • Runs on laptops & cloud CPUs • Global #1 on JevBench When you ask ChatGPT or Claude a question, it generates words one token at a time, like a person typing out an essay. That takes 2 to 5 seconds and burns expensive GPU compute. That is great for writing a story, but it is painfully slow and expensive for simple decisions: - "Did the AI make up this answer, or is it actually in the PDF?" - "Should this customer's message go to billing, shipping, or technical support?" - "Does the revenue bar chart support this financial claim?" - "Did the student get the math problem right according to the answer key?" Psychologist Daniel Kahneman described human thinking…
Open weights
apache-2.0
5.1B parameters
131,072 tokens
transformers
100x faster than generative LLMs • Runs on laptops & cloud CPUs • Global #1 on JevBench When you ask ChatGPT or Claude a question, it generates words one token at a time, like a person typing out an essay. That takes 2 to 5 seconds and burns expensive GPU compute. That is great for writing a story, but it is painfully slow and expensive for simple decisions: - "Did the AI make up this answer, or is it actually in the PDF?" - "Should this customer's message go to billing, shipping, or technical support?" - "Does the revenue bar chart support this financial claim?" - "Did the student get the math problem right according to the answer key?" Psychologist Daniel Kahneman described human thinking…
Open weights
apache-2.0
5.1B parameters
131,072 tokens
transformers
T
Model · Text classification
Tokimoa
uratori-ja-4b is a Japanese decision model. Given a text state (for example a source document and a claim) and one or more typed questions, it returns a probability distribution over the allowed answers in a single forward pass, without generating text. Three question types are supported: noul (yes/no), choice (pick one of 2 to 8 labelled options) and score (an ordinal scale with 2 to 10 levels). It is trained for three jobs: checking whether a claim or an answer is supported by supplied evidence (grounding), comparing two documents (contradictions, meaning-changing edits, added claims) and judging retrieval results and answers in a RAG pipeline (relevance, sufficiency, faithfulness). The…
Open weights
apache-2.0
4.2B parameters
transformers
Small, fast decisions for routing and checks at volume. Send text or JSON state with choice, noul (yes/no), or score questions. Get a probability for every offered answer, not generated text. Each batch takes one forward pass; large requests can use several batches. Use choice to route a request, noul for a yes/no check, or score for an ordered rating. The same call can ask several questions about a single state. Try it: POST /v1/systemone, GET /v1/models, and GET /healthz. Point TypeSafe's server-side Python or JavaScript SDKs at it with TYPESAFEBASEURL; text decisions use the same request and response fields as hosted Jev. Requests run one at a time by default; --batch-window-ms 5 enables…
Open weights
other
4.2B parameters
262,144 tokens
transformers
