Best AI tools for< Evaluate Models >
20 - AI tool Sites
BenchLLM
BenchLLM is an AI tool designed for AI engineers to evaluate LLM-powered apps by running and evaluating models with a powerful CLI. It allows users to build test suites, choose evaluation strategies, and generate quality reports. The tool supports OpenAI, Langchain, and other APIs out of the box, offering automation, visualization of reports, and monitoring of model performance.
SuperAnnotate
SuperAnnotate is an AI data platform that simplifies and accelerates model-building by unifying the AI pipeline. It enables users to create, curate, and evaluate datasets efficiently, leading to the development of better models faster. The platform offers features like connecting any data source, building customizable UIs, creating high-quality datasets, evaluating models, and deploying models seamlessly. SuperAnnotate ensures global security and privacy measures for data protection.
Flow AI
Flow AI is an advanced AI tool designed for evaluating and improving Large Language Model (LLM) applications. It offers a unique system for creating custom evaluators, deploying them with an API, and developing specialized LMs tailored to specific use cases. The tool aims to revolutionize AI evaluation and model development by providing transparent, cost-effective, and controllable solutions for AI teams across various domains.
Maxim
Maxim is an end-to-end AI evaluation and observability platform that empowers modern AI teams to ship products with quality, reliability, and speed. It offers a comprehensive suite of tools for experimentation, evaluation, observability, and data management. Maxim aims to bring the best practices of traditional software development into non-deterministic AI workflows, enabling rapid iteration and deployment of AI models. The platform caters to the needs of AI developers, data scientists, and machine learning engineers by providing a unified framework for evaluation, visual flows for workflow testing, and observability features for monitoring and optimizing AI systems in real-time.
Labelbox
Labelbox is a data factory platform that empowers AI teams to manage data labeling, train models, and create better data with internet scale RLHF platform. It offers an all-in-one solution comprising tooling and services powered by a global community of domain experts. Labelbox operates a global data labeling infrastructure and operations for AI workloads, providing expert human network for data labeling in various domains. The platform also includes AI-assisted alignment for maximum efficiency, data curation, model training, and labeling services. Customers achieve breakthroughs with high-quality data through Labelbox.
Athina AI
Athina AI is a platform that provides research and guides for building safe and reliable AI products. It helps thousands of AI engineers in building safer products by offering tutorials, research papers, and evaluation techniques related to large language models. The platform focuses on safety, prompt engineering, hallucinations, and evaluation of AI models.
Sacred
Sacred is a tool to configure, organize, log and reproduce computational experiments. It is designed to introduce only minimal overhead, while encouraging modularity and configurability of experiments. The ability to conveniently make experiments configurable is at the heart of Sacred. If the parameters of an experiment are exposed in this way, it will help you to: keep track of all the parameters of your experiment easily run your experiment for different settings save configurations for individual runs in files or a database reproduce your results In Sacred we achieve this through the following main mechanisms: Config Scopes are functions with a @ex.config decorator, that turn all local variables into configuration entries. This helps to set up your configuration really easily. Those entries can then be used in captured functions via dependency injection. That way the system takes care of passing parameters around for you, which makes using your config values really easy. The command-line interface can be used to change the parameters, which makes it really easy to run your experiment with modified parameters. Observers log every information about your experiment and the configuration you used, and saves them for example to a Database. This helps to keep track of all your experiments. Automatic seeding helps controlling the randomness in your experiments, such that they stay reproducible.
Scale AI
Scale AI is an AI tool that accelerates the development of AI applications for enterprise, government, and automotive sectors. It offers Scale Data Engine for generative AI, Scale GenAI Platform, and evaluation services for model developers. The platform leverages enterprise data to build sustainable AI programs and partners with leading AI models. Scale's focus on generative AI applications, data labeling, and model evaluation sets it apart in the AI industry.
MLflow
MLflow is an open source platform for managing the end-to-end machine learning (ML) lifecycle, including tracking experiments, packaging models, deploying models, and managing model registries. It provides a unified platform for both traditional ML and generative AI applications.
integrate.ai
integrate.ai is a platform that enables data and analytics providers to collaborate easily with enterprise data science teams without moving data. Powered by federated learning technology, the platform allows for efficient proof of concepts, data experimentation, infrastructure agnostic evaluations, collaborative data evaluations, and data governance controls. It supports various data science jobs such as match rate analysis, exploratory data analysis, correlation analysis, model performance analysis, feature importance & data influence, and model validation. The platform integrates with popular data science tools like Azure, Jupyter, Databricks, AWS, GCP, Snowflake, Pandas, PyTorch, MLflow, and scikit-learn.
thisorthis.ai
thisorthis.ai is an AI tool that allows users to compare generative AI models and AI model responses. It helps users analyze and evaluate different AI models to make informed decisions. The tool requires JavaScript to be enabled for optimal functionality.
Evidently AI
Evidently AI is an open-source machine learning (ML) monitoring and observability platform that helps data scientists and ML engineers evaluate, test, and monitor ML models from validation to production. It provides a centralized hub for ML in production, including data quality monitoring, data drift monitoring, ML model performance monitoring, and NLP and LLM monitoring. Evidently AI's features include customizable reports, structured checks for data and models, and a Python library for ML monitoring. It is designed to be easy to use, with a simple setup process and a user-friendly interface. Evidently AI is used by over 2,500 data scientists and ML engineers worldwide, and it has been featured in publications such as Forbes, VentureBeat, and TechCrunch.
Robust Intelligence
Robust Intelligence is an end-to-end solution for securing AI applications. It automates the evaluation of AI models, data, and files for security and safety vulnerabilities and provides guardrails for AI applications in production against integrity, privacy, abuse, and availability violations. Robust Intelligence helps enterprises remove AI security blockers, save time and resources, meet AI safety and security standards, align AI security across stakeholders, and protect against evolving threats.
FinetuneDB
FinetuneDB is an AI fine-tuning platform that allows users to easily create and manage datasets to fine-tune LLMs, evaluate outputs, and iterate on production data. It integrates with open-source and proprietary foundation models, and provides a collaborative editor for building datasets. FinetuneDB also offers a variety of features for evaluating model performance, including human and AI feedback, automated evaluations, and model metrics tracking.
Frontier Model Forum
The Frontier Model Forum (FMF) is a collaborative effort among leading AI companies to advance AI safety and responsibility. The FMF brings together technical and operational expertise to identify best practices, conduct research, and support the development of AI applications that meet society's most pressing needs. The FMF's core objectives include advancing AI safety research, identifying best practices, collaborating across sectors, and helping AI meet society's greatest challenges.
Robust Intelligence
Robust Intelligence is an end-to-end security solution for AI applications. It automates the evaluation of AI models, data, and files for security and safety vulnerabilities and provides guardrails for AI applications in production against integrity, privacy, abuse, and availability violations. Robust Intelligence helps enterprises remove AI security blockers, save time and resources, meet AI safety and security standards, align AI security across stakeholders, and protect against evolving threats.
MeDA School
MeDA School is an educational platform dedicated to promoting and nurturing talents in the field of Medical Artificial Intelligence (AI). The platform aims to establish a solid foundation for intelligent and precision medical talent pools in Taiwan and globally. MeDA School facilitates interaction and communication among members of the intelligent medical ecosystem, fostering deep understanding and trust in the operation and tasks of medical AI. The platform offers a blend of virtual and physical courses, inviting domain experts to share cutting-edge knowledge and integrating interdisciplinary knowledge to be practically applied in various fields.
Deepfake Detection Challenge Dataset
The Deepfake Detection Challenge Dataset is a project initiated by Facebook AI to accelerate the development of new ways to detect deepfake videos. The dataset consists of over 100,000 videos and was created in collaboration with industry leaders and academic experts. It includes two versions: a preview dataset with 5k videos and a full dataset with 124k videos, each featuring facial modification algorithms. The dataset was used in a Kaggle competition to create better models for detecting manipulated media. The top-performing models achieved high accuracy on the public dataset but faced challenges when tested against the black box dataset, highlighting the importance of generalization in deepfake detection. The project aims to encourage the research community to continue advancing in detecting harmful manipulated media.
Lumenova AI
Lumenova AI is an AI platform that focuses on making AI ethical, transparent, and compliant. It provides solutions for AI governance, assessment, risk management, and compliance. The platform offers comprehensive evaluation and assessment of AI models, proactive risk management solutions, and simplified compliance management. Lumenova AI aims to help enterprises navigate the future confidently by ensuring responsible AI practices and compliance with regulations.
Competely
Competely is an AI-powered competitive analysis tool that provides comprehensive insights into competitors across various industries. It saves users hours of manual research by generating detailed side-by-side comparisons, including information on marketing, product features, pricing, audience, customer sentiment, company info, and SWOT analysis. The tool is suitable for founders, executives, marketers, product managers, agencies, and consultants, helping them make data-driven decisions and strategies to outsmart the competition.
20 - Open Source AI Tools
ollama-grid-search
A Rust based tool to evaluate LLM models, prompts and model params. It automates the process of selecting the best model parameters, given an LLM model and a prompt, iterating over the possible combinations and letting the user visually inspect the results. The tool assumes the user has Ollama installed and serving endpoints, either in `localhost` or in a remote server. Key features include: * Automatically fetches models from local or remote Ollama servers * Iterates over different models and params to generate inferences * A/B test prompts on different models simultaneously * Allows multiple iterations for each combination of parameters * Makes synchronous inference calls to avoid spamming servers * Optionally outputs inference parameters and response metadata (inference time, tokens and tokens/s) * Refetching of individual inference calls * Model selection can be filtered by name * List experiments which can be downloaded in JSON format * Configurable inference timeout * Custom default parameters and system prompts can be defined in settings
pyllms
PyLLMs is a minimal Python library designed to connect to various Language Model Models (LLMs) such as OpenAI, Anthropic, Google, AI21, Cohere, Aleph Alpha, and HuggingfaceHub. It provides a built-in model performance benchmark for fast prototyping and evaluating different models. Users can easily connect to top LLMs, get completions from multiple models simultaneously, and evaluate models on quality, speed, and cost. The library supports asynchronous completion, streaming from compatible models, and multi-model initialization for testing and comparison. Additionally, it offers features like passing chat history, system messages, counting tokens, and benchmarking models based on quality, speed, and cost.
babilong
BABILong is a generative benchmark designed to evaluate the performance of NLP models in processing long documents with distributed facts. It consists of 20 tasks that simulate interactions between characters and objects in various locations, requiring models to distinguish important information from irrelevant details. The tasks vary in complexity and reasoning aspects, with test samples potentially containing millions of tokens. The benchmark aims to challenge and assess the capabilities of Large Language Models (LLMs) in handling complex, long-context information.
arena-hard-auto
Arena-Hard-Auto-v0.1 is an automatic evaluation tool for instruction-tuned LLMs. It contains 500 challenging user queries. The tool prompts GPT-4-Turbo as a judge to compare models' responses against a baseline model (default: GPT-4-0314). Arena-Hard-Auto employs an automatic judge as a cheaper and faster approximator to human preference. It has the highest correlation and separability to Chatbot Arena among popular open-ended LLM benchmarks. Users can evaluate their models' performance on Chatbot Arena by using Arena-Hard-Auto.
TempCompass
TempCompass is a benchmark designed to evaluate the temporal perception ability of Video LLMs. It encompasses a diverse set of temporal aspects and task formats to comprehensively assess the capability of Video LLMs in understanding videos. The benchmark includes conflicting videos to prevent models from relying on single-frame bias and language priors. Users can clone the repository, install required packages, prepare data, run inference using examples like Video-LLaVA and Gemini, and evaluate the performance of their models across different tasks such as Multi-Choice QA, Yes/No QA, Caption Matching, and Caption Generation.
ai-hub
AI Hub Project aims to continuously test and evaluate mainstream large language models, while accumulating and managing various effective model invocation prompts. It has integrated all mainstream large language models in China, including OpenAI GPT-4 Turbo, Baidu ERNIE-Bot-4, Tencent ChatPro, MiniMax abab5.5-chat, and more. The project plans to continuously track, integrate, and evaluate new models. Users can access the models through REST services or Java code integration. The project also provides a testing suite for translation, coding, and benchmark testing.
ML-Bench
ML-Bench is a tool designed to evaluate large language models and agents for machine learning tasks on repository-level code. It provides functionalities for data preparation, environment setup, usage, API calling, open source model fine-tuning, and inference. Users can clone the repository, load datasets, run ML-LLM-Bench, prepare data, fine-tune models, and perform inference tasks. The tool aims to facilitate the evaluation of language models and agents in the context of machine learning tasks on code repositories.
rageval
Rageval is an evaluation tool for Retrieval-augmented Generation (RAG) methods. It helps evaluate RAG systems by performing tasks such as query rewriting, document ranking, information compression, evidence verification, answer generation, and result validation. The tool provides metrics for answer correctness and answer groundedness, along with benchmark results for ASQA and ALCE datasets. Users can install and use Rageval to assess the performance of RAG models in question-answering tasks.
llm-colosseum
llm-colosseum is a tool designed to evaluate Language Model Models (LLMs) in real-time by making them fight each other in Street Fighter III. The tool assesses LLMs based on speed, strategic thinking, adaptability, out-of-the-box thinking, and resilience. It provides a benchmark for LLMs to understand their environment and take context-based actions. Users can analyze the performance of different LLMs through ELO rankings and win rate matrices. The tool allows users to run experiments, test different LLM models, and customize prompts for LLM interactions. It offers installation instructions, test mode options, logging configurations, and the ability to run the tool with local models. Users can also contribute their own LLM models for evaluation and ranking.
raga-llm-hub
Raga LLM Hub is a comprehensive evaluation toolkit for Language and Learning Models (LLMs) with over 100 meticulously designed metrics. It allows developers and organizations to evaluate and compare LLMs effectively, establishing guardrails for LLMs and Retrieval Augmented Generation (RAG) applications. The platform assesses aspects like Relevance & Understanding, Content Quality, Hallucination, Safety & Bias, Context Relevance, Guardrails, and Vulnerability scanning, along with Metric-Based Tests for quantitative analysis. It helps teams identify and fix issues throughout the LLM lifecycle, revolutionizing reliability and trustworthiness.
BALROG
BALROG is a benchmark tool designed to evaluate agentic Long-Longitudinal Memory (LLM) and Vision-Language Memory (VLM) capabilities using reinforcement learning environments. It provides a comprehensive assessment of agentic abilities, supports both language and vision-language models, integrates with popular AI APIs, and allows for easy integration of custom agents, new environments, and models.
lumigator
Lumigator is an open-source platform developed by Mozilla.ai to help users select the most suitable language model for their specific needs. It supports the evaluation of summarization tasks using sequence-to-sequence models such as BART and BERT, as well as causal models like GPT and Mistral. The platform aims to make model selection transparent, efficient, and empowering by providing a framework for comparing LLMs using task-specific metrics to evaluate how well a model fits a project's needs. Lumigator is in the early stages of development and plans to expand support to additional machine learning tasks and use cases in the future.
GPT4Point
GPT4Point is a unified framework for point-language understanding and generation. It aligns 3D point clouds with language, providing a comprehensive solution for tasks such as 3D captioning and controlled 3D generation. The project includes an automated point-language dataset annotation engine, a novel object-level point cloud benchmark, and a 3D multi-modality model. Users can train and evaluate models using the provided code and datasets, with a focus on improving models' understanding capabilities and facilitating the generation of 3D objects.
simpletransformers
Simple Transformers is a library based on the Transformers library by HuggingFace, allowing users to quickly train and evaluate Transformer models with only 3 lines of code. It supports various tasks such as Information Retrieval, Language Models, Encoder Model Training, Sequence Classification, Token Classification, Question Answering, Language Generation, T5 Model, Seq2Seq Tasks, Multi-Modal Classification, and Conversational AI.
mindnlp
MindNLP is an open-source NLP library based on MindSpore. It provides a platform for solving natural language processing tasks, containing many common approaches in NLP. It can help researchers and developers to construct and train models more conveniently and rapidly. Key features of MindNLP include: * Comprehensive data processing: Several classical NLP datasets are packaged into a friendly module for easy use, such as Multi30k, SQuAD, CoNLL, etc. * Friendly NLP model toolset: MindNLP provides various configurable components. It is friendly to customize models using MindNLP. * Easy-to-use engine: MindNLP simplified complicated training process in MindSpore. It supports Trainer and Evaluator interfaces to train and evaluate models easily. MindNLP supports a wide range of NLP tasks, including: * Language modeling * Machine translation * Question answering * Sentiment analysis * Sequence labeling * Summarization MindNLP also supports industry-leading Large Language Models (LLMs), including Llama, GLM, RWKV, etc. For support related to large language models, including pre-training, fine-tuning, and inference demo examples, you can find them in the "llm" directory. To install MindNLP, you can either install it from Pypi, download the daily build wheel, or install it from source. The installation instructions are provided in the documentation. MindNLP is released under the Apache 2.0 license. If you find this project useful in your research, please consider citing the following paper: @misc{mindnlp2022, title={{MindNLP}: a MindSpore NLP library}, author={MindNLP Contributors}, howpublished = {\url{https://github.com/mindlab-ai/mindnlp}}, year={2022} }
MMMU
MMMU is a benchmark designed to evaluate multimodal models on college-level subject knowledge tasks, covering 30 subjects and 183 subfields with 11.5K questions. It focuses on advanced perception and reasoning with domain-specific knowledge, challenging models to perform tasks akin to those faced by experts. The evaluation of various models highlights substantial challenges, with room for improvement to stimulate the community towards expert artificial general intelligence (AGI).
Large-Language-Model-Notebooks-Course
This practical free hands-on course focuses on Large Language models and their applications, providing a hands-on experience using models from OpenAI and the Hugging Face library. The course is divided into three major sections: Techniques and Libraries, Projects, and Enterprise Solutions. It covers topics such as Chatbots, Code Generation, Vector databases, LangChain, Fine Tuning, PEFT Fine Tuning, Soft Prompt tuning, LoRA, QLoRA, Evaluate Models, Knowledge Distillation, and more. Each section contains chapters with lessons supported by notebooks and articles. The course aims to help users build projects and explore enterprise solutions using Large Language Models.
AgentBench
AgentBench is a benchmark designed to evaluate Large Language Models (LLMs) as autonomous agents in various environments. It includes 8 distinct environments such as Operating System, Database, Knowledge Graph, Digital Card Game, and Lateral Thinking Puzzles. The tool provides a comprehensive evaluation of LLMs' ability to operate as agents by offering Dev and Test sets for each environment. Users can quickly start using the tool by following the provided steps, configuring the agent, starting task servers, and assigning tasks. AgentBench aims to bridge the gap between LLMs' proficiency as agents and their practical usability.
OlympicArena
OlympicArena is a comprehensive benchmark designed to evaluate advanced AI capabilities across various disciplines. It aims to push AI towards superintelligence by tackling complex challenges in science and beyond. The repository provides detailed data for different disciplines, allows users to run inference and evaluation locally, and offers a submission platform for testing models on the test set. Additionally, it includes an annotation interface and encourages users to cite their paper if they find the code or dataset helpful.
StableToolBench
StableToolBench is a new benchmark developed to address the instability of Tool Learning benchmarks. It aims to balance stability and reality by introducing features such as a Virtual API System with caching and API simulators, a new set of solvable queries determined by LLMs, and a Stable Evaluation System using GPT-4. The Virtual API Server can be set up either by building from source or using a prebuilt Docker image. Users can test the server using provided scripts and evaluate models with Solvable Pass Rate and Solvable Win Rate metrics. The tool also includes model experiments results comparing different models' performance.
20 - OpenAI Gpts
Instructor GCP ML
Formador para la certificación de ML Engineer en GCP, con respuestas y explicaciones detalladas.
HuggingFace Helper
A witty yet succinct guide for HuggingFace, offering technical assistance on using the platform - based on their Learning Hub
GPT Architect
Expert in designing GPT models and translating user needs into technical specs.
GPT Designer
A creative aide for designing new GPT models, skilled in ideation and prompting.
Pytorch Trainer GPT
Your purpose is to create the pytorch code to train language models using pytorch
FinVIX | Finance Pro for College Courses
Expert in undergraduate financial math, using multiple in-depth trainings.
Business Model Canvas Strategist
Business Model Canvas Creator - Build and evaluate your business model
Business Model Advisor
Business model expert, create detailed reports based on business ideas.
Startup Critic
Apply gold-standard startup valuation and assessment methods to identify risks and gaps in your business model and product ideas.
Startup Advisor
Startup advisor guiding founders through detailed idea evaluation, product-market-fit, business model, GTM, and scaling.
FinWiz
FinWiz-GPT is designed for finance professionals. It assists in market analysis, financial modeling, and understanding complex financial instruments. It's a great tool for financial analysts, investment bankers, and accountants.
Face Rating GPT 😐
Evaluates faces and rates them out of 10 ⭐ Provides valuable feedback to improving your attractiveness!