Awesome GitHub RepositoriesSampling Parameter Tuning

MiniCPM is a collection of small language models designed for local, on-device deployment in resource-constrained environments. The project focuses on running dense Transformer models on consumer hardware, including GPUs, CPUs, and Apple Silicon, without requiring custom code forks. The project distinguishes itself through heavy optimization for edge hardware, utilizing quantized weight compression in GGUF and MLX formats to reduce memory overhead. It implements advanced inference techniques such as speculative sampling and radix-tree prefix caching to accelerate generation speed and throughp

This project is a terminal-based command line interface client and agent orchestrator for interacting with multiple large language model providers. It functions as an OpenAI API client and a local API gateway that exposes chat completions and embeddings through an HTTP server. The system distinguishes itself by providing a retrieval-augmented generation tool for indexing local files and URLs into a vector database to provide custom document context. It allows for the creation of specialized AI agents that combine custom system prompts with tool calling and external function execution. The to

BetterChatGPT is a cross-platform user interface and OpenAI API client designed for interacting with large language models. It functions as a prompt engineering workspace and a self-hosted AI frontend that allows users to connect to models via API keys or custom proxy endpoints. The project distinguishes itself through conversation management tools, including the ability to organize chats into color-coded folders and maintain a library of reusable prompt templates. It also includes a real-time cost monitoring system that tracks token consumption and calculates estimated pricing for interactio

G0DM0D3 is a static web client and multi-model chat gateway designed for AI research, prompt optimization, and red teaming. It provides a unified interface to query numerous AI models in parallel, allowing for the simultaneous evaluation of different prompt variations and sampling parameters to identify the most successful outputs. The project features specialized tooling for probing safety filters and bypassing model constraints through an input perturbation engine that applies text obfuscation and character substitution. It includes a composite scoring system to rank model performance and a

Chinese-Vicuna es un modelo de lenguaje grande chino e IA que sigue instrucciones basado en la arquitectura LLaMA. Está diseñado específicamente para la comprensión y generación de lenguaje natural en el idioma chino, utilizando un modelo ajustado por instrucciones para seguir prompts complejos de usuario a través de conversaciones. El proyecto proporciona un framework de ajuste fino LoRA y sistemas de cuantización para permitir la adaptación del modelo y la inferencia en hardware de consumo. Implementa inferencia cuantizada para reducir el uso de memoria tanto en CPUs como en GPUs, soportado por una implementación de bajo nivel en C++ para minimizar los requisitos de recursos del sistema. El sistema cubre una amplia gama de capacidades de procesamiento de lenguaje natural, incluyendo gestión de conversaciones multivuelta, traducción multilingüe y generación de código de programación. También incluye herramientas para entrenamiento específico de dominio, conversión de formato de modelo y una interfaz de chat interactiva con salida de texto en streaming.

ComfyUI-Easy-Use is a custom node suite and workflow optimizer designed to simplify Stable Diffusion generation pipelines. It provides a set of integrated tools to reduce visual clutter and streamline the process of creating images from text and existing image references. The project distinguishes itself through a pipeline manager that consolidates models, conditioning, and latents into unified data pipes, eliminating complex wiring in the node graph. It also introduces a logical operator set that enables conditional if-else branching and for-loop structures directly within the visual program