Search Results for "binary options software"

KittenTTS is an open-source, ultra-lightweight, and high-quality text-to-speech model featuring just 15 million parameters and a binary size under 25 MB. It is designed for real-time CPU-based deployment across diverse platforms. Ultra-lightweight, model size less than 25MB. CPU-optimized, runs without GPU on any device. High-quality voices, several premium voice options available. Fast inference, optimized for real-time speech synthesis.

...The system is designed for scientific research applications in structural biology, biochemistry, and bioinformatics, enabling accurate modeling of proteins, ligands, and covalent modifications. Users can perform local predictions via Docker containers, integrating AlphaFold 3’s inference process with provided JSON input configurations. The software includes flexible options for running both data preprocessing and GPU-accelerated inference, allowing users to adapt to available computational resources.

...The repository introduces the model lineup, intended uses, and evaluation highlights, and it complements IBM’s broader Granite initiative spanning multiple modalities. IBM’s research blog details the motivation for opening these models and points developers to downloads, papers, and hosting options. Together, the materials position Granite Code as enterprise-friendly, permissively licensed models for practical software engineering assistance. They slot into the larger Granite ecosystem that includes language and time-series models, community cookbooks, and production guidance.

Jev-Omni is an open-weight multimodal decision classification model designed to evaluate text, images, audio, and video through structured questions and predefined answer options. Built on Google's Gemma 4 12B IT architecture and fine-tuned on 30,000 questions, it returns calibrated probabilities for each possible answer rather than generating natural-language explanations. The model supports three decision formats: Noul for binary yes/no questions, Choice for multiclass selection, and Score for probability-based evaluation. ...

...Unlike conventional generative models, Quyet evaluates a supplied state, such as text, JSON, or conversation history, and answers multiple typed questions using predefined options. It supports three decision formats: Choice for categorical classification, Score for ordered scales, and Noul for binary judgments. The model calculates probabilities from next-token option predictions and applies calibrated temperatures to improve confidence estimates. It primarily supports English and is additionally tuned for Vietnamese. ...