Open-source alternatives to Evalscope

This project is a standardized framework for benchmarking large language models across a wide range of academic and reasoning datasets. It provides a platform for executing automated evaluation tasks to measure model accuracy and performance, ensuring consistent assessment through a structured configuration schema. The framework distinguishes itself by incorporating a dedicated utility for data decontamination, which identifies and removes overlapping training samples from evaluation sets to prevent data leakage. It also features a flexible task builder that allows users to define custom benc

VLMEvalKit is a vision-language model evaluation framework and inference engine designed to run standardized benchmarks and measure model accuracy across diverse visual datasets. It serves as a multimodal model benchmark and performance toolkit for calculating metrics and comparing model responses. The toolkit includes a specialized visual reasoning evaluator that uses adversarial samples to distinguish actual image understanding from reliance on language patterns. It also provides capabilities for image generation evaluation, testing a model's ability to create or modify visuals based on tex

GAOKAO-Bench is an evaluation framework that utilizes GAOKAO questions as a dataset to evaluate large language models.

🤗 Evaluate: A library for easily evaluating machine learning models and datasets.

MMSegmentation is an open-source semantic segmentation toolbox built on PyTorch that provides a modular, configurable framework for building, training, evaluating, and deploying segmentation models. At its core, it offers a config-driven pipeline that assembles training, evaluation, and inference workflows by parsing hierarchical configuration files, with a modular component registry that enables plug-and-play composition of neural network modules, optimizers, datasets, and metrics. The framework supports the full model lifecycle through a unified runner interface that controls training, testi

ParlAI is a conversational AI research framework designed for training, evaluating, and sharing dialogue models using a unified interface for datasets and agents. It functions as a PyTorch-based training platform and a dialogue data collection system, providing a centralized model zoo for the distribution of versioned pretrained agents. The project distinguishes itself through a knowledge-grounded retrieval system that combines dense and sparse indexing to ground responses in external information. It also provides a comprehensive infrastructure for gathering human-AI interaction data via inte

GoLearn is a machine learning library for the Go programming language. It provides a supervised learning framework and a toolkit for building, training, and evaluating predictive models through a standardized interface. The project implements a data frame system that loads CSV files into structured grids for matrix operations. It includes a preprocessing library for discretizing continuous variables and a model evaluation toolkit that utilizes confusion matrices and cross-validation to measure precision and recall. The library covers data engineering and management, including the ability to

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 educational resource and engineering guide for building, deploying, and optimizing large language model applications and production pipelines. It serves as a blueprint for cloud AI infrastructure, providing a framework for orchestrating inference endpoints, data warehouses, and scalable production environments. The repository provides specific implementation patterns for retrieval augmented generation to ground model responses in external data. It includes a training workflow for crawling, structuring, and processing datasets to facilitate model fine-tuning, alongside an ev

This project provides a structured methodology and framework for engineering, testing, and maintaining system instructions for large language models. It serves as a guide for designing clear, explicit prompts that ensure models follow complex tasks with consistent accuracy. The framework distinguishes itself by treating prompt development as a rigorous engineering process, emphasizing the use of version control to manage prompt templates and style guides. By organizing instructions into modular, reusable components, it facilitates long-term maintainability and reduces technical debt within de

Tinker Cookbook is an open-source framework for fine-tuning large language models, supporting supervised learning, reinforcement learning, and parameter-efficient techniques like LoRA adapters. It provides a complete pipeline for aligning models with human preferences through multi-stage RLHF workflows, from supervised fine-tuning through preference optimization to reinforcement learning. The framework distinguishes itself through recipe-based training orchestration, where fine-tuning workflows are defined as composable recipe files that chain data loading, model configuration, and training l

jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

SlowFast is a PyTorch video understanding framework and spatiotemporal neural network library. It serves as a toolset for video action recognition, enabling the training and evaluation of models designed to classify complex activities and objects within video sequences. The framework is distinguished by its use of dual-pathway spatiotemporal sampling to capture both slow and fast motions. It supports self-supervised video learning for pre-training models on unlabeled data and employs multigrid spatiotemporal training to optimize learning across multiple spatial and temporal resolutions. The

This project is a technical curriculum and development guide focused on large language model prompt engineering, fine-tuning, and the creation of retrieval augmented generation applications. It serves as a comprehensive resource for developers to master crafting precise instructions and textual patterns to improve the quality and predictability of model outputs. The material covers the end-to-end workflow of adapting open-source models to specific datasets and integrating language models with vector databases to generate responses based on private information. It also provides a systematic ap

rllm is an asynchronous reinforcement learning framework for training language agents. It provides a unified pipeline that runs the same agent code for both evaluation and training, automatically capturing traces for gradient computation. The framework supports distributed reinforcement learning across multiple GPUs and nodes using pluggable backends, and executes agents in isolated sandboxes—either locally or in the cloud—for safe and scalable rollout collection. It trains agents built with LangGraph, SmolAgents, OpenAI Agents SDK, or custom frameworks without requiring core logic changes. T

Osim-rl is a research environment designed for the development and evaluation of reinforcement learning agents within physics-based musculoskeletal simulations. It provides a standardized interface that maps physiological state observations to muscle excitation control signals, enabling the study of human movement and biomechanics through iterative policy optimization. The framework distinguishes itself by integrating high-fidelity musculoskeletal modeling with tools for scientific benchmarking and reproducible experimentation. It allows researchers to define custom reward functions and adjus

MatchZoo is a deep learning framework designed for building, training, and evaluating neural networks that determine the relevance and similarity between pairs of textual inputs. It serves as a research platform for neural information retrieval, specifically supporting the development of models for document retrieval, question answering, and ranking tasks. The framework utilizes declarative architecture composition to define complex neural network structures. It includes automated hyper-parameter resolution to populate missing configuration parameters before model compilation and uses callbac

SmolLM is a project dedicated to the development of small language models. It focuses on training and fine-tuning compact models that maintain high performance while utilizing fewer parameters. The project emphasizes efficient AI inference and on-device text generation, aiming to enable the deployment of lightweight models on edge devices with limited memory and processing power. It utilizes synthetic data generation to produce artificial datasets that improve the reasoning and training of these AI systems. The system supports a variety of optimization and training capabilities, including we

This is a machine learning educational repository consisting of a collection of notebooks and code examples. It provides practical implementations of diverse machine learning algorithms and workflows, ranging from traditional scientific computing to deep learning. The project features specific implementations of Scikit-Learn models, such as decision trees, random forests, and support vector machines, as well as TensorFlow examples for building neural networks, convolutional layers, and recurrent architectures. It also includes tutorials on reinforcement learning development and the creation o

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

TCN is a deep learning sequence framework and library for building temporal convolutional networks. It provides a toolkit for implementing purely convolutional architectures to model sequential data as an alternative to recurrent neural networks. The project includes a sequence modeling benchmark suite designed to evaluate the accuracy and processing speed of architectures. This suite utilizes standardized tasks, including memory problems, digit classification, music, and language tasks, to quantify performance. The framework covers a range of structural components for sequence processing, s

Giskard is an evaluation framework, testing library, and quality monitoring system for large language models and AI agents. It serves as a toolkit for quantifying model performance and reliability, providing specialized capabilities for validating retrieval-augmented generation pipelines. The project distinguishes itself through an automated red teaming tool and security scanner designed to identify vulnerabilities, prompt injections, and safety risks. It utilizes adversarial probing and synthetic edge case generation to quantify model robustness and detect information disclosure. The platfo

Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. Tracing · Evals · Simulations · Datasets · Gateway · Guardrails. Self-hostable. Apache 2.0.

FlagEval, launched by BAAI in 2023, is a comprehensive large model evaluation system that encompasses over 800 open-source and closed-source models from around the globe. It features more than 40 capability dimensions, including reasoning, mathematical skills, and task-solving abilities, along…