# K2-Type-0.9B ![Organization](https://img.shields.io/badge/Org-IFM-555) ![License](https://img.shields.io/badge/License-Apache%202.0-2da44e) ![Params](https://img.shields.io/badge/Params-0.9B-8957e5) ![Type](https://img.shields.io/badge/Type-Decision%20model-555) ![Released](https://img.shields.io/badge/Released-Sep%202026-blue) > **Quick answer:** K2-Type-0.9B is a 0.9B-parameter decision model from IFM (Institute of Foundation Models), released September 26, 2026 under Apache 2.0. Built on the K2-Horizon-0.9B backbone with a pointer head, it takes a state and typed questions (`noul`, `choice`, `score`) and returns a probability for every option in one forward pass, never generating text. It scores 76.2% (176/231) on the public JevBench set with 27 ms median latency on one H200. ## At a Glance **What it is:** A very small decision model in the style of TypeSafe AI's Jev that is wire-compatible with the `/v1/systemone` API. **Where it leads:** Calibration and speed for its size — Brier 0.328, ECE 0.065, 27 ms p50 and 60 ms p95 per decision, about 590 input tokens per decision. **Where it lags:** One-pass only, no reasoning: multi-step arithmetic, date differences and documents beyond 8,192 tokens are weak, and calibration is fitted on Kev's suite so other distributions can be over- or under-confident. ## What K2-Type-0.9B Is Questions share the state but cannot see each other (block-causal attention mask), so adding a question never changes another's answer. A pointer head scores each option's `` hidden state against the question's `` hidden state, and a softmax at temperature 1.478 produces probabilities. Training was a full fine-tune on about 354K decision records (public classification, NLI and QA sets, Kev's decision data, game positions and synthetic items) with soft labels from a 7B teacher decision model, then 50 iterations of PPO on Snake and a temperature refit. The same weights play Snake from a text board (66 food per game on average on a 12x12 board). Training code and data are not released. Weights and the minimal serving code (`jev/`) are public. It builds on [K2-Horizon-0.9B](/models/ifm/k2-horizon-0-9b). ## Specifications | Field | Value | |---|---| | Organization | IFM | | Hugging Face | [IFM/K2-Type-0.9B](https://huggingface.co/IFM/K2-Type-0.9B) | | Base model | IFM/K2-Horizon-0.9B | | Parameters | 0.9B (1.08B stored, including the unused LM head) | | Output | Typed probabilities (no text generation) | | License | Apache 2.0 | | Release date | 2026-09-26 | ## Public Benchmark Scores | Benchmark | Score | Source | Date | |---|---|---|---| | [JevBench](/benchmarks/fstandhartinger/jevbench) (public set, accuracy) | 76.2 | [Model card](https://huggingface.co/IFM/K2-Type-0.9B) | 2026-09 | *Self-reported by IFM on the 231-item JevBench public set (standard 66/72, easy 47/48, hard 63/111). The official JevBench score also uses sealed items, on which every listed system scores well below its public accuracy.* ## Frequently Asked Questions

What is K2-Type-0.9B?

IFM's 0.9B open decision model that returns calibrated probabilities for typed questions in one forward pass, compatible with the Jev/SystemOne API.

Can K2-Type-0.9B generate text?

No. It never generates text; the backbone's language-model head is left unused and the pointer head scores the options you provide.

How fast is it?

About 27 ms median and 60 ms p95 per decision on a single H200.

K2-Type-0.9B — Training jobs

Training jobs