仓库/Skill 库

非自回归的 System 1 决策引擎。单次前向传播即可对任意文本进行类型化选择、打分和 yes/no 决策，支持 100+ 语言，并通过路由按请求选择合适的 checkpoint。Non-autoregressive System 1 decision engine. Typed choice, score and yes/no decisions over any text in a single forward pass, in 100+ languages, with a router that picks the right checkpoint per request.

开放式前沿智能Open Frontier Intelligence

基于 Qwen3.5 构建的小型 Jev 风格决策模型家族，可自行训练与部署。tiny Jev-like family of decision models built on top of Qwen3.5 you can train and run on your own

一款围绕该模型构建的、面向 Apple silicon 的本地推理引擎。A local inference engine for Apple silicon, built around the model.

基于 2x DGX Spark 与 TensorFold 的 GLM-5.3-Flash EXL3。GLM-5.3-Flash EXL3 on 2x DGX Spark with TensorFold

DeepSeek v4 Flash EXL3 运行于单台 DGX SparkDeepSeek v4 Flash EXL3 on one DGX Spark

GLM-5.3 Flash EXL3，针对 2x DGX Sparks。GLM-5.3 Flash EXL3 for 2x DGX Sparks

用于解析日期和时间的小型神经模型A small neural model for parsing date & time

JevK5：TypeSafe Jev 的开源权重替代方案。单次前向即可输出带概率的类型化决策；权重与代码均采用 Apache-2.0 协议。JevK5: open-weight alternative to TypeSafe Jev. Typed decisions with probabilities in one forward pass; Apache-2.0 weights and code.

一个小型开放决策模型：state + 类型化问题 → 校准概率。在 Qwen3.5 上的 Jev / System One 复现。A small open decision model: state + typed questions -> calibrated probabilities. A Jev / System One re-creation on Qwen3.5.

基于 PyTorch 的韩语 LLM 实现与训练项目PyTorch 기반 한국어 LLM 구현 및 학습 프로젝트

基于 Jev API 的开放决策模型：通过前向传递给出带概率的类型化答案（yes/no、choice、score、multi），无需生成。基于 Qwen3.5-4B + LoRA，单卡 GPU。Open decisions model with Jev's API: typed answers (yes/no, choice, score, multi) with probabilities from forward passes, no generation. Qwen3.5-4B + LoRA, one GPU.