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.
Global
#441 · +88 · 1d ·GitHub
scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.
#463 · +83 · 1d ·GitHub
8B to 27B: a calibrated probability for every option in one pass, and a short thought only when unsure. cpp and MLX.
Qwen3-8B on a real BANKING77 item. Every number is a model output.
Jev-like family of decision models built on top of Qwen3.5/3.8 you can train and run on your own
#1251 · +24 · 1d ·GitHub
We advise to run all examples through Google Colab for the easiest setup. Google Colab allows you to use a T4 GPU with 16GB of VRAM for free. All examples were mainly built and tested using Google Colab, so it should be the most stable platform.
#1719 · +16 · 1d ·GitHub
A curated list of public projects, integrations, and discussions built on Jev — TypeSafe AI's System One model for typed decisions.
#1875 · +14 · 1d ·GitHub
Register | Course Platform | Course Materials | Join Slack | Course Slack Channel | Telegram | YouTube Playlist | FAQ
#2015 · +12 · 1d ·GitHub
We are your essential toolkit for computer vision. From data loading to real-time zone counting, we provide the building blocks so you can focus on building applications around your models. 🤝
Typed decisions from a frozen language model and a small physics engine: calibrated probabilities, conformal sets, 73k context, images.
jevos: an open-source alternative to Jev and Microsoft-Decision-1. Yes/no decisions, choices and scores with probabilities, on your laptop's CPU.
#3182 · +6 · 1d ·GitHub
"). Everything else — dates, amounts, units, math — is deterministic code. Jev decides, code computes.
#3190 · +6 · 1d ·GitHub
Strands decider is one of a new class of decision models, or "system one" models. Unlike an LLM, which can generate arbitrary text, a decision model picks between sets of options and rates things on a scale.
#3426 · +5 · 1d ·GitHub
:zap:机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归
What it is, and what it isn't. These are very small models. They make extremely fast, well-calibrated judgement calls between options, and they slot easily into your local code.
jev lets you filter, rank and classify rows with plain-language conditions. Every row is judged by TypeSafe's Jev, a System One model that returns calibrated probabilities instead of generated text. No index, no embeddings, no vector column.
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
#4600 · +2 · 1d ·GitHub
deep learning for image processing including classification and object-detection etc.
NLTK -- the Natural Language Toolkit -- is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. 14.
#4911 · +2 · 1d ·GitHub
A Library for Advanced Deep Time Series Models for General Time Series Analysis.
#5478 · +1 · 1d ·GitHub
> 🇺🇦 UKRAINE IS BEING ATTACKED BY RUSSIAN ARMY. CIVILIANS ARE GETTING KILLED. RESIDENTIAL AREAS ARE GETTING BOMBED.
Magika is a novel AI-powered file type detection tool that relies on the recent advance of deep learning to provide accurate detection.
\ It is written in Python and uses Qt for its graphical interface.
Free and open source face recognition with deep neural networks.
#5952 · +1 · 1d ·GitHub
Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.
#5970 · +1 · 1d ·GitHub
What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning ... from theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book.
Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.
Open source annotation tool for machine learning practitioners.
We also provide a cheatsheet full of useful one-liners.
#6730 · +0 · 1d ·GitHub
Implementation of Vision Transformer , a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch. Significance is further explained in Yannic Kilcher's video.