Awesome GitHub RepositoriesConfidence Scoring

Fingerprint is a visitor identification and fraud detection platform that generates persistent, unique identifiers by analyzing browser and device attributes. By extracting technical signals from the client environment, it enables reliable user tracking across sessions without relying on traditional cookies. The platform distinguishes itself through its focus on high-accuracy identification and security-first architecture. It employs edge-side proxying to bypass ad-blockers and privacy restrictions, ensuring consistent data collection. To maintain data integrity, it uses cryptographic payload

NSFW detection on the client-side via TensorFlow.js

CTranslate2 is a C++ inference engine and runtime for Transformer models, designed to execute models on both CPU and GPU with optimizations for speed and memory efficiency. It functions as a model format converter, quantization tool, and REST API server, enabling deployment of neural machine translation, automatic speech recognition, and text generation models. The engine distinguishes itself through a suite of runtime optimizations including layer fusion, weight-matrix quantization, batch-by-length grouping, and a caching allocator that reuses GPU memory. It supports tensor-parallel model di

evo2 ist ein genomisches Large Language Model und Foundation Model, das darauf ausgelegt ist, genetische Informationen über verschiedene Spezies hinweg vorherzusagen, zu generieren und zu analysieren. Es fungiert als Nukleotid-Sequenz-Modellierer und DNA-Sequenz-Generator und nutzt Transformer-basiertes Sequenz-Modeling zur Verarbeitung genomischer Daten. Das System bietet Funktionen zur synthetischen DNA-Generierung, um neue genetische Sequenzen basierend auf biologischen Prompts oder spezies-spezifischen Tags zu erstellen. Es führt zudem Nukleotid-Wahrscheinlichkeitsvorhersagen durch, um genomische Varianten zu bewerten und biologische Eigenschaften innerhalb von DNA-Sequenzen zu analysieren. Das Modell unterstützt die Analyse genomischer Sequenzen durch die Extraktion hochdimensionaler Repräsentationen aus Zwischenschichten. Diese Embeddings ermöglichen spezialisierte Klassifizierungen und weiterführende Analysen genetischer Daten.

LLaDA is a masked diffusion language model and conditional text generator. It generates text by iteratively refining masked tokens through a diffusion process rather than predicting the next token in a sequence. The project functions as a vision-language diffusion model, converting visual inputs into text responses. It also serves as a preference optimization framework that uses log-likelihood estimation and evidence lower bounds to tune model responses. The system supports multi-round conversational AI and text sequence evaluation. It integrates vision-language embedding for cross-modal con