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MyTools.AI
MyTools.AI is an AI application that offers a suite of tools powered by advanced AI models to assist users in various tasks such as content creation, image generation, code generation, music generation, and more. The application aims to simplify and enhance user workflows by leveraging AI technology to provide quick and accurate solutions. Users can generate content, images, code, and music effortlessly, making it an ideal tool for individuals across different professions and industries.
OpenFuture
OpenFuture is the world's largest AI Tools Directory in 2024, offering a comprehensive collection of AI applications across various categories such as 3D generator, aggregators, AI detection, art generator, audio editing, and more. Users can explore a wide range of AI-powered tools to enhance productivity, streamline tasks, and improve efficiency in different domains. The platform serves as a valuable resource for individuals and businesses looking to leverage artificial intelligence technology for various purposes.
LowTech AI
LowTech AI is a platform that combines simple fill-in-the-blank inputs with powerful AI to create various tools for users. It offers features like summarizing text, generating emails, finding synonyms, coding functions, and more. Users can benefit from effortless intelligence, flexible tool creation, and zero friction sharing. The platform aims to enhance creativity, speed, and skills in tasks such as brainstorming, planning, writing, and analyzing, among others.
Theee.ai
Theee.ai is a one-stop platform offering access to over 50,000 AI tools for free. Users can find a variety of AI-powered tools for tasks such as research, video creation, diagram generation, math solutions, image editing, logo design, and more. The platform aims to simplify the process of discovering and utilizing AI tools by providing a centralized hub for users to explore and leverage cutting-edge technologies. With a focus on convenience and efficiency, Theee.ai caters to individuals and businesses seeking innovative solutions powered by artificial intelligence.
Diagramix
Diagramix is an AI-powered tool that allows users to generate various types of diagrams, such as sequence diagrams, flowcharts, state diagrams, ER diagrams, user journey diagrams, class diagrams, mind maps, and use case diagrams. Users can create these diagrams easily and efficiently with the help of Diagramix and ChatGPT. The tool provides a user-friendly interface for creating visual representations of complex systems and processes, making it ideal for professionals, students, and anyone who needs to visualize information.
AI Findr
AI Findr is an AI tool directory that showcases the best AI tools of 2024. Users can discover various AI categories and submit their startup for visibility. The platform offers a range of AI tools for different purposes, such as AI-generated models for fashion ecommerce, ad generation, art creation, customer service, task management, education assistance, image generation, and more. AI Findr Tools Directory is updated daily with new tools and features, providing users with innovative solutions powered by AI technology.
ToolPilot
ToolPilot is an AI tools directory and review platform that helps users discover, compare, and excel with AI tools. It offers unbiased reviews, powerful filters, and a vibrant community. Users can browse through featured AI tools, explore categories, and read reviews from other users to find the best AI tool for their needs.
AIGO.tools
AIGO.tools is an AI directory website that serves as a comprehensive platform for discovering and accessing a wide range of AI tools. Users can explore and find various AI writing tools, AI marketing tools, AI paraphrasing tools, AI SEO tools, AI study tools, AI generator tools, AI research tools, AI art tools, AI music tools, AI video tools, AI coding tools, AI photo tools, and more. The website features a free AI Tools Directory, a Startup List for AI tool developers, and supports the submission of AI tools to enhance visibility and promote innovation in the AI community.
Explosion
Explosion is a software company specializing in developer tools and tailored solutions for AI, Machine Learning, and Natural Language Processing (NLP). They are the makers of spaCy, one of the leading open-source libraries for advanced NLP. The company offers consulting services and builds developer tools for various AI-related tasks, such as coreference resolution, dependency parsing, image classification, named entity recognition, and more.
Slashprompt.ai
Slashprompt.ai is a platform that helps users discover the best AI tools tailored to their specific needs. With advanced search capabilities, users can quickly find solutions for any challenge they face. The website offers a wide range of AI tools across various categories such as analysis, art generation, audio, blockchain, business, chat, coding, content detection, cooking, copywriting, customer support, design, developer tools, education, fitness, health, hiring, image generation, interior design, legal, logo design, marketing, meetings, music, chatbots, productivity, SEO, social media, spirituality, summarization, translation, video editing, voice generation, website building, and writing.
AI Search
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designtools.ai
This website provides a curated collection of AI tools specifically designed for designers, including tools for UI & UX, colors, images, typography, marketing, and productivity. These tools leverage artificial intelligence to enhance and streamline the design workflow, enabling designers to create stunning visuals, generate content, automate tasks, and improve their overall productivity.
AixyAI Directory
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Creati.ai
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ToolList.ai
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Galaxy.ai
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nlp-phd-global-equality
This repository aims to promote global equality for individuals pursuing a PhD in NLP by providing resources and information on various aspects of the academic journey. It covers topics such as applying for a PhD, getting research opportunities, preparing for the job market, and succeeding in academia. The repository is actively updated and includes contributions from experts in the field.
llms-interview-questions
This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.
AgentPoison
AgentPoison is a repository that provides the official PyTorch implementation of the paper 'AgentPoison: Red-teaming LLM Agents via Memory or Knowledge Base Backdoor Poisoning'. It offers tools for red-teaming LLM agents by poisoning memory or knowledge bases. The repository includes trigger optimization algorithms, agent experiments, and evaluation scripts for Agent-Driver, ReAct-StrategyQA, and EHRAgent. Users can fine-tune motion planners, inject queries with triggers, and evaluate red-teaming performance. The codebase supports multiple RAG embedders and provides a unified dataset access for all three agents.
1.5-Pints
1.5-Pints is a repository that provides a recipe to pre-train models in 9 days, aiming to create AI assistants comparable to Apple OpenELM and Microsoft Phi. It includes model architecture, training scripts, and utilities for 1.5-Pints and 0.12-Pint developed by Pints.AI. The initiative encourages replication, experimentation, and open-source development of Pint by sharing the model's codebase and architecture. The repository offers installation instructions, dataset preparation scripts, model training guidelines, and tools for model evaluation and usage. Users can also find information on finetuning models, converting lit models to HuggingFace models, and running Direct Preference Optimization (DPO) post-finetuning. Additionally, the repository includes tests to ensure code modifications do not disrupt the existing functionality.
rl
TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. It provides pytorch and **python-first** , low and high level abstractions for RL that are intended to be **efficient** , **modular** , **documented** and properly **tested**. The code is aimed at supporting research in RL. Most of it is written in python in a highly modular way, such that researchers can easily swap components, transform them or write new ones with little effort.
only_train_once
Only Train Once (OTO) is an automatic, architecture-agnostic DNN training and compression framework that allows users to train a general DNN from scratch or a pretrained checkpoint to achieve high performance and slimmer architecture simultaneously in a one-shot manner without fine-tuning. The framework includes features for automatic structured pruning and erasing operators, as well as hybrid structured sparse optimizers for efficient model compression. OTO provides tools for pruning zero-invariant group partitioning, constructing pruned models, and visualizing pruning and erasing dependency graphs. It supports the HESSO optimizer and offers a sanity check for compliance testing on various DNNs. The repository also includes publications, installation instructions, quick start guides, and a roadmap for future enhancements and collaborations.
inspectus
Inspectus is a versatile visualization tool for large language models. It provides multiple views, including Attention Matrix, Query Token Heatmap, Key Token Heatmap, and Dimension Heatmap, to offer insights into language model behaviors. Users can interact with the tool in Jupyter notebooks through an easy-to-use Python API. Inspectus allows users to visualize attention scores between tokens, analyze how tokens focus on each other during processing, and explore the relationships between query and key tokens. The tool supports the visualization of attention maps from Huggingface transformers and custom attention maps, making it a valuable resource for researchers and developers working with language models.
pytorch-lightning
PyTorch Lightning is a framework for training and deploying AI models. It provides a high-level API that abstracts away the low-level details of PyTorch, making it easier to write and maintain complex models. Lightning also includes a number of features that make it easy to train and deploy models on multiple GPUs or TPUs, and to track and visualize training progress. PyTorch Lightning is used by a wide range of organizations, including Google, Facebook, and Microsoft. It is also used by researchers at top universities around the world. Here are some of the benefits of using PyTorch Lightning: * **Increased productivity:** Lightning's high-level API makes it easy to write and maintain complex models. This can save you time and effort, and allow you to focus on the research or business problem you're trying to solve. * **Improved performance:** Lightning's optimized training loops and data loading pipelines can help you train models faster and with better performance. * **Easier deployment:** Lightning makes it easy to deploy models to a variety of platforms, including the cloud, on-premises servers, and mobile devices. * **Better reproducibility:** Lightning's logging and visualization tools make it easy to track and reproduce training results.
AdalFlow
AdalFlow is a library designed to help developers build and optimize Large Language Model (LLM) task pipelines. It follows a design pattern similar to PyTorch, offering a light, modular, and robust codebase. Named in honor of Ada Lovelace, AdalFlow aims to inspire more women to enter the AI field. The library is tailored for various GenAI applications like chatbots, translation, summarization, code generation, and autonomous agents, as well as classical NLP tasks such as text classification and named entity recognition. AdalFlow emphasizes modularity, robustness, and readability to support users in customizing and iterating code for their specific use cases.
LLM4Decompile
LLM4Decompile is an open-source large language model dedicated to decompilation of Linux x86_64 binaries, supporting GCC's O0 to O3 optimization levels. It focuses on assessing re-executability of decompiled code through HumanEval-Decompile benchmark. The tool includes models with sizes ranging from 1.3 billion to 33 billion parameters, available on Hugging Face. Users can preprocess C code into binary and assembly instructions, then decompile assembly instructions into C using LLM4Decompile. Ongoing efforts aim to expand capabilities to support more architectures and configurations, integrate with decompilation tools like Ghidra and Rizin, and enhance performance with larger training datasets.
local-talking-llm
The 'local-talking-llm' repository provides a tutorial on building a voice assistant similar to Jarvis or Friday from Iron Man movies, capable of offline operation on a computer. The tutorial covers setting up a Python environment, installing necessary libraries like rich, openai-whisper, suno-bark, langchain, sounddevice, pyaudio, and speechrecognition. It utilizes Ollama for Large Language Model (LLM) serving and includes components for speech recognition, conversational chain, and speech synthesis. The implementation involves creating a TextToSpeechService class for Bark, defining functions for audio recording, transcription, LLM response generation, and audio playback. The main application loop guides users through interactive voice-based conversations with the assistant.
ExplainableAI.jl
ExplainableAI.jl is a Julia package that implements interpretability methods for black-box classifiers, focusing on local explanations and attribution maps in input space. The package requires models to be differentiable with Zygote.jl. It is similar to Captum and Zennit for PyTorch and iNNvestigate for Keras models. Users can analyze and visualize explanations for model predictions, with support for different XAI methods and customization. The package aims to provide transparency and insights into model decision-making processes, making it a valuable tool for understanding and validating machine learning models.
fastfit
FastFit is a Python package designed for fast and accurate few-shot classification, especially for scenarios with many semantically similar classes. It utilizes a novel approach integrating batch contrastive learning and token-level similarity score, significantly improving multi-class classification performance in speed and accuracy across various datasets. FastFit provides a convenient command-line tool for training text classification models with customizable parameters. It offers a 3-20x improvement in training speed, completing training in just a few seconds. Users can also train models with Python scripts and perform inference using pretrained models for text classification tasks.
llm-analysis
llm-analysis is a tool designed for Latency and Memory Analysis of Transformer Models for Training and Inference. It automates the calculation of training or inference latency and memory usage for Large Language Models (LLMs) or Transformers based on specified model, GPU, data type, and parallelism configurations. The tool helps users to experiment with different setups theoretically, understand system performance, and optimize training/inference scenarios. It supports various parallelism schemes, communication methods, activation recomputation options, data types, and fine-tuning strategies. Users can integrate llm-analysis in their code using the `LLMAnalysis` class or use the provided entry point functions for command line interface. The tool provides lower-bound estimations of memory usage and latency, and aims to assist in achieving feasible and optimal setups for training or inference.
AI-Drug-Discovery-Design
AI-Drug-Discovery-Design is a repository focused on Artificial Intelligence-assisted Drug Discovery and Design. It explores the use of AI technology to accelerate and optimize the drug development process. The advantages of AI in drug design include speeding up research cycles, improving accuracy through data-driven models, reducing costs by minimizing experimental redundancies, and enabling personalized drug design for specific patients or disease characteristics.