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Human ChatGPT Comparison Corpus (HC3), Detectors, and more! 🔥

An open source DevOps tool from the CNCF for packaging and versioning AI/ML models, datasets, code, and configuration into an OCI Artifact.

XAI - An eXplainability toolbox for machine learning

a recursive self-improving harness designed to help your agents (and future iterations of those agents) succeed on any task

Googles NotebookLM but local

ML powered analytics engine for outlier detection and root cause analysis.

Framework and Library for Distributed Online Machine Learning

Cookiecutter template for FastAPI projects using: Machine Learning, uv, Github Actions and Pytests

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

An LLM extension for Ghidra to enable AI assistance in RE.

Leverage hallucinations from Large Language Models (LLMs) for novelty-driven explorations.

Run LLaMA (and Stanford-Alpaca) inference on Apple Silicon GPUs.

🤖 AI browser extensions & userscripts to augment your web experience

Cleora AI is a general-purpose open-source model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data. Created by Synerise.com team.

ChatGPT-Pro is an advanced application that combines the power of ChatGPT and DALL.E.

A C++17 library to access the entire OpenAI API.

The open source implementation of Gemini, the model that will "eclipse ChatGPT" by Google

⚡ Edgen: Local, private GenAI server alternative to OpenAI. No GPU required. Run AI models locally: LLMs (Llama2, Mistral, Mixtral...), Speech-to-text (whisper) and many others.

↻ Keeps ChatGPT sessions fresh, eliminating chat time limits + network errors + Cloudflare checks

Curated List of AI, ML and Deep Learning books

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.

🕶️ Adds chat auto-clear functionality to ChatGPT for more privacy