Awesome GitHub RepositoriesModel Benchmarks

JARVIS is a system for large language model task orchestration, deployment management, and automation benchmarking. It utilizes a task orchestrator to decompose complex requests into actionable steps and coordinates various expert models to synthesize final responses. The project includes an AI model deployment manager to handle the local deployment of expert models across different hardware scales. It further provides an AI workflow API consisting of web endpoints used to trigger automated task workflows and retrieve results from model selection stages. The framework incorporates an automat

This project is a comprehensive repository and curated index of resources, research papers, and development frameworks designed to support the construction and deployment of intelligent systems. It serves as a centralized knowledge base for developers seeking to navigate the technical landscape of artificial intelligence, ranging from foundational educational materials to specialized implementation guides. The repository distinguishes itself by providing structured directories for comparing generative artificial intelligence providers, including aggregated performance metrics, pricing data, a

llm-numbers es un conjunto de herramientas de cálculo y benchmarks utilizados para predecir requisitos de hardware, uso de tokens y costos operativos en varios niveles de modelos. Proporciona una calculadora de costos y recursos basada en fórmulas y benchmarks para estimar tokens, memoria de GPU y gastos operativos para modelos de lenguaje grandes. El proyecto incluye un planificador de requisitos de hardware para calcular la VRAM y la memoria de GPU necesarias para alojar modelos basados en recuentos de parámetros. También cuenta con un estimador de tokens que convierte recuentos de palabras en estimaciones de tokens para predecir la facturación de la API y el uso de la ventana de contexto, junto con benchmarks de precios que comparan costos y compensaciones de rendimiento entre diferentes métodos de alojamiento. El conjunto de herramientas cubre el benchmarking de modelos de IA y la previsión de costos, planificación de recursos de GPU y análisis de rendimiento para medir las ganancias de rendimiento del procesamiento por lotes. Utiliza fórmulas deterministas y datasets de benchmark estáticos para mapear parámetros a memoria y calcular las relaciones costo-beneficio entre modelos base y ajuste fino (fine-tuning).

This project is a language model evaluation framework and benchmarking tool designed to measure the accuracy and performance of models across diverse datasets. It provides a system for implementing model-based graders, running standardized tests for mathematical reasoning, coding, and factuality, and calculating quantified performance metrics such as precision, recall, F1 scores, and pass-at-k. The framework utilizes model-based grading and rubrics to validate response quality against expert-defined criteria. It includes a multi-model benchmarking loop and a model-agnostic API interface to co

This project is an LLM research orchestrator and autonomous AI agent framework designed to automate the scientific lifecycle. It functions as an end-to-end research pipeline and model training toolkit, managing everything from initial literature reviews and hypothesis testing to the final drafting of academic papers. The system is distinguished by its ability to convert unstructured academic PDFs into machine-executable knowledge layers, allowing agents to reproduce and extend research findings. It employs a two-loop orchestration architecture and a specialized research engineering skill libr

Awesome Coding Plan is a community-driven knowledge repository that provides a comparative analysis of subscription-based coding environments and artificial intelligence development tools. It functions as a tracker for developer tool costs, aggregating data on pricing structures, usage quotas, and token limits to assist in the selection of cloud-based coding services. The project utilizes a standardized framework to evaluate the performance and economic efficiency of various language models. By organizing technical metrics into a unified format, it allows for the objective assessment of proce