OpenJV

Jevlike is an open-source research starter for training small models that choose among a variable list of text options. Given a context and an option list, it represents each option as a query, attends to the context tokens to produce an option-specific context vector, applies a shared dot product, and runs a softmax across the options to return probabilities in one pass rather than generating an answer token by token. It provides a trainable byte-level encoder and an optional frozen Hugging Face encoder, with command-line tools for generating synthetic data, training, evaluation, and prediction from JSONL records. The repository also includes a visual option scorer used by Doom and chess controller examples. Training supports CPU, Apple MPS, and CUDA; the default encoder truncates contexts to 192 bytes and options to 32 bytes, and the project requires the complete option list at prediction time. Jevlike is an independent implementation rather than a copy of TypeSafe's commercial Jev model, and its code is released under the MIT License.

NanoJev is an open-source 0.6B parallel-decision model and training pipeline based on Qwen3-0.6B. It accepts states, questions, and candidate sets in a single forward pass and returns complete probability distributions without output-token decoding. Shared decision heads support dynamic-choice distributions over supplied candidates, Boolean proposition probabilities, and ordered score levels with expected values. The repository includes data generation, distribution-loss training, evaluation, persistent serving, and browser replay tools. Its game demonstrations combine model judgments with controller code: maze controllers ask local safety questions and track collisions and explored paths, while Snake controllers filter immediate collisions, plan toward visible food, and use the model to break ties between remaining actions. Checkpoints and datasets are distributed through Hugging Face, and the service exposes repeated batched evaluations through a local HTTP endpoint.

jev-trader is an AI market-making bot for the Kuru MON-USDC order book on Monad. A TypeSafe Jev model observes the order book once per block and predicts whether to buy or sell over a configurable future horizon; each block, the bot cancels its resting orders and submits a post-only limit order one tick inside the touch, aiming to earn the spread when a taker fills it. Its hot loop reads the book with one RPC call and submits the transaction with one asynchronous raw-transaction call, leaving receipt processing, fee estimates, and vault checks off the critical path. It records block decisions, quotes, fills, positions, and profit-and-loss data, and exposes snapshots, history, and server-sent events through a Bun server. If the model misses the block, it holds and posts nothing; position or margin limits can redirect a quote to the opposite side. The repository supports a dry-run mode with simulated fills and a mock momentum heuristic, or live Jev decisions using a TypeSafe AI API key and private key. It includes a deployed dry-run dashboard endpoint and can be run with Bun.

kev is an open-source, local Jev-inspired decision model from Jared Palmer. It runs on a laptop and accepts a document or other state plus typed questions, returning calibrated probabilities rather than generated text. It supports yes/no (noul), multiple-choice, and ordered-score questions. The model uses a LoRA adapter and a readout head on a Qwen base model. It packs the state and all questions into one sequence, applies a block-causal mask so each question can see the state but not sibling questions, and performs one prefill pass without decoding. A pointer head scores each option against the question's decision token and applies softmax; the head is trained with cross-entropy on labelled outcomes. The local server exposes a TypeSafe System One-compatible API at POST /v1/systemone, allowing the official TypeSafe SDK to connect by changing its base URL. It also provides model information, option-order permutation, and separate-pass comparison endpoints, plus a local playground. The repository includes 0.5B, 0.6B, 4B, and 8B model checkpoints and recipes for local or Modal-based training and evaluation. Serving is tested on Apple Silicon, with larger checkpoints requiring bf16 on a 32 GB Mac. The server has no authentication and is intended for local use. The project is licensed under Apache-2.0; base models and datasets have separate licenses.

LocalJev is a TypeScript service that provides a local Jev-compatible decision API for applications using TypeSafe's typed decision interface. It exposes a POST /v1/systemone endpoint and translates typed Jev questions and application state into classification prompts for an OpenAI-compatible DiffusionGemma inference server. The service validates and retries malformed JSON, normalizes probability vectors, calculates Jev-compatible choices, expected scores, and entropy-based confidence, and returns the standard Jev response shape. Its probabilities are model-generated or self-reported rather than read directly from model logits, so the repository advises evaluating calibration for the intended workload. It runs with Bun and can use oMLX as the local inference server; the default LocalJev address is http://127.0.0.1:8080.