AI tools for artificialanalysis.ai
Related Tools:
no-cost-ai
No-cost-ai is a repository dedicated to providing a comprehensive list of free AI models and tools for developers, researchers, and curious builders. It serves as a living index for accessing state-of-the-art AI models without any cost. The repository includes information on various AI applications such as chat interfaces, media generation, voice and music tools, AI IDEs, and developer APIs and platforms. Users can find links to free models, their limits, and usage instructions. Contributions to the repository are welcome, and users are advised to use the listed services at their own risk due to potential changes in models, limitations, and reliability of free services.
Awesome-AITools
This repo collects AI-related utilities. ## All Categories * All Categories * ChatGPT and other closed-source LLMs * AI Search engine * Open Source LLMs * GPT/LLMs Applications * LLM training platform * Applications that integrate multiple LLMs * AI Agent * Writing * Programming Development * Translation * AI Conversation or AI Voice Conversation * Image Creation * Speech Recognition * Text To Speech * Voice Processing * AI generated music or sound effects * Speech translation * Video Creation * Video Content Summary * OCR(Optical Character Recognition)
Awesome-AI
Awesome AI is a repository that collects and shares resources in the fields of large language models (LLM), AI-assisted programming, AI drawing, and more. It explores the application and development of generative artificial intelligence. The repository provides information on various AI tools, models, and platforms, along with tutorials and web products related to AI technologies.
awesome-generative-ai
Awesome Generative AI is a curated list of modern Generative Artificial Intelligence projects and services. Generative AI technology creates original content like images, sounds, and texts using machine learning algorithms trained on large data sets. It can produce unique and realistic outputs such as photorealistic images, digital art, music, and writing. The repo covers a wide range of applications in art, entertainment, marketing, academia, and computer science.
Free-LLM-Collection
Free-LLM-Collection is a curated list of free resources for mastering the Legal Language Model (LLM) technology. It includes datasets, research papers, tutorials, and tools to help individuals learn and work with LLM models. The repository aims to provide a comprehensive collection of materials to support researchers, developers, and enthusiasts interested in exploring and leveraging LLM technology for various applications in the legal domain.
llms-tools
The 'llms-tools' repository is a comprehensive collection of AI tools, open-source projects, and research related to Large Language Models (LLMs) and Chatbots. It covers a wide range of topics such as AI in various domains, open-source models, chats & assistants, visual language models, evaluation tools, libraries, devices, income models, text-to-image, computer vision, audio & speech, code & math, games, robotics, typography, bio & med, military, climate, finance, and presentation. The repository provides valuable resources for researchers, developers, and enthusiasts interested in exploring the capabilities of LLMs and related technologies.
RAGHub
RAGHub is a community-driven project focused on cataloging new and emerging frameworks, projects, and resources in the Retrieval-Augmented Generation (RAG) ecosystem. It aims to help users stay ahead of changes in the field by providing a platform for the latest innovations in RAG. The repository includes information on RAG frameworks, evaluation frameworks, optimization frameworks, citation frameworks, engines, search reranker frameworks, projects, resources, and real-world use cases across industries and professions.
Awesome-local-LLM
Awesome-local-LLM is a curated list of platforms, tools, practices, and resources that help run Large Language Models (LLMs) locally. It includes sections on inference platforms, engines, user interfaces, specific models for general purpose, coding, vision, audio, and miscellaneous tasks. The repository also covers tools for coding agents, agent frameworks, retrieval-augmented generation, computer use, browser automation, memory management, testing, evaluation, research, training, and fine-tuning. Additionally, there are tutorials on models, prompt engineering, context engineering, inference, agents, retrieval-augmented generation, and miscellaneous topics, along with a section on communities for LLM enthusiasts.
llm-resources
llm-resources is a repository providing resources to get started with Large Language Models (LLMs). It includes videos on Neural Networks and LLMs, free courses, prompt engineering guides, explored frameworks, AI assistants, and tips on making RAG work properly. The repository also contains important links and updates related to LLMs, AWS, RAG, agents, model context protocol, and more. It aims to help individuals with a basic understanding of NLP and programming knowledge to explore and utilize LLMs effectively.
LLMEvaluation
The LLMEvaluation repository is a comprehensive compendium of evaluation methods for Large Language Models (LLMs) and LLM-based systems. It aims to assist academics and industry professionals in creating effective evaluation suites tailored to their specific needs by reviewing industry practices for assessing LLMs and their applications. The repository covers a wide range of evaluation techniques, benchmarks, and studies related to LLMs, including areas such as embeddings, question answering, multi-turn dialogues, reasoning, multi-lingual tasks, ethical AI, biases, safe AI, code generation, summarization, software performance, agent LLM architectures, long text generation, graph understanding, and various unclassified tasks. It also includes evaluations for LLM systems in conversational systems, copilots, search and recommendation engines, task utility, and verticals like healthcare, law, science, financial, and others. The repository provides a wealth of resources for evaluating and understanding the capabilities of LLMs in different domains.