Popular GitHub Repositories for Insecure Output Handling Remediation

A game theoretic approach to explain the output of any machine learning model.

Flexible and powerful framework for managing multiple AI agents and handling complex conversations

Flexible and powerful framework for managing multiple AI agents and handling complex conversations

The AI framework that adds the engineering to prompt engineering (Python/TS/Ruby/Java/C#/Rust/Go compatible)

Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research

Dockerized FastAPI wrapper for Kokoro-82M text-to-speech model w/CPU ONNX and NVIDIA GPU PyTorch support, handling, and auto-stitching

Data & AI Notebook templates catalog organized by tools, following the IMO (input, model, output) framework for easy usage and discovery..

Better Prometheus alerts for Kubernetes - smart grouping, AI enrichment, and automatic remediation

Enhance Tesseract OCR output for scanned PDFs by applying Large Language Model (LLM) corrections.

Run AI models locally on your machine with node.js bindings for llama.cpp. Enforce a JSON schema on the model output on the generation level

Developer friendly Natural Language Processing ?

Tools for handling speech data in machine learning projects.

TrustRAG：The RAG Framework within Reliable input,Trusted output

Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)

Emacs as your personal AI assistant. Use LLMs such as ChatGPT or LLaMA for text generation or DALL-E and Stable Diffusion for image generation. Also supports speech input / output.

LLMs can generate feedback on their work, use it to improve the output, and repeat this process iteratively.

High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). Allowing users to chat with LLM models, execute structured function calls and get structured output. Works also with models not fine-tuned to JSON output and function calls.

Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [CVPR 2021]

From anywhere you can type, query and stream the output of an LLM or any other script