Best AI tools for< Imitate Style >

AI Humanizer is a free online tool that utilizes advanced algorithms to imitate human writing. It helps users convert AI-generated text into content that appears to be written by a human. The tool offers features like natural language processing, contextual understanding, SEO optimization, and plagiarism detection avoidance. It is beneficial for content creators, marketers, students, and businesses looking to enhance their writing and SEO performance.

**stylellm** is a text style transfer project based on large language models (llms). The project utilizes large language models to learn the writing style of specific literary works (commonly used vocabulary, sentence structure, rhetoric, character dialogue, etc.), forming a series of specific style models. Using the **stylellm** model, the learned style can be transferred to other general texts, that is: input a piece of original text, the model can rewrite it, output text with the characteristics of that style, achieving the effect of text modification,润色or style imitation.

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Chat-Style-Bot is an intelligent chatbot designed to mimic the chatting style of a specified individual. By analyzing and learning from WeChat chat records, Chat-Style-Bot can imitate your unique chatting style and become your personal chat assistant. Whether it's communicating with friends or handling daily conversations, Chat-Style-Bot can provide a natural, personalized interactive experience.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

This repository contains custom Evilginx phishlets that are meticulously crafted and updated for real-world applications. It also offers an advanced course, EvilGoPhish Mastery, focusing on phishing and smishing techniques using EvilGoPhish 3.0. The course complements the repository by providing in-depth guidance on deploying these scripts for red team phishing and smishing campaigns.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

Paper-Reading-ConvAI is a repository that contains a list of papers, datasets, and resources related to Conversational AI, mainly encompassing dialogue systems and natural language generation. This repository is constantly updating.

The 'llms' repository is a comprehensive guide on Large Language Models (LLMs), covering topics such as language modeling, applications of LLMs, statistical language modeling, neural language models, conditional language models, evaluation methods, transformer-based language models, practical LLMs like GPT and BERT, prompt engineering, fine-tuning LLMs, retrieval augmented generation, AI agents, and LLMs for computer vision. The repository provides detailed explanations, examples, and tools for working with LLMs.

Infinity is a high-throughput, low-latency REST API for serving vector embeddings, supporting all sentence-transformer models and frameworks. It is developed under the MIT License and powers inference behind Gradient.ai. The API allows users to deploy models from SentenceTransformers, offers fast inference backends utilizing various accelerators, dynamic batching for efficient processing, correct and tested implementation, and easy-to-use API built on FastAPI with Swagger documentation. Users can embed text, rerank documents, and perform text classification tasks using the tool. Infinity supports various models from Huggingface and provides flexibility in deployment via CLI, Docker, Python API, and cloud services like dstack. The tool is suitable for tasks like embedding, reranking, and text classification.

AI4Animation is a comprehensive framework for data-driven character animation, including data processing, neural network training, and runtime control, developed in Unity3D/PyTorch. It explores deep learning opportunities for character animation, covering biped and quadruped locomotion, character-scene interactions, sports and fighting games, and embodied avatar motions in AR/VR. The research focuses on generative frameworks, codebook matching, periodic autoencoders, animation layering, local motion phases, and neural state machines for character control and animation.

Vim-airline is a lean and mean status/tabline plugin for Vim that provides a nice statusline at the bottom of each Vim window. It consists of several sections displaying information such as mode, environment status, filename, filetype, file encoding, and current position in the file. The plugin is highly customizable and integrates with various plugins, providing a tiny core with extensibility in mind. It is optimized for speed, supports multiple themes, and integrates seamlessly with other plugins. Vim-airline is written in 100% Vimscript, eliminating the need for Python. The plugin aims to be stable and includes a unit testing suite for reliability.

Aio-scrapy is an asyncio-based web crawling and web scraping framework inspired by Scrapy. It supports distributed crawling/scraping, implements compatibility with scrapyd, and provides options for using redis queue and rabbitmq queue. The framework is designed for fast extraction of structured data from websites. Aio-scrapy requires Python 3.9+ and is compatible with Linux, Windows, macOS, and BSD systems.

This repository contains the implementation of Self-Play of Adversarial Language Game (SPAG) as described in the paper 'Self-playing Adversarial Language Game Enhances LLM Reasoning'. The SPAG involves training Language Models (LLMs) in an adversarial language game called Adversarial Taboo. The repository provides tools for imitation learning, self-play episode collection, and reinforcement learning on game episodes to enhance LLM reasoning abilities. The process involves training models using GPUs, launching imitation learning, conducting self-play episodes, assigning rewards based on outcomes, and learning the SPAG model through reinforcement learning. Continuous improvements on reasoning benchmarks can be observed by repeating the episode-collection and SPAG-learning processes.

以特定格式呈现Pitchfork风格音乐评论。

模仿给定文本的语言风格和表述习惯

Imitates a specific individual's style and opinions accurately and ethically.

I imitate GPT-5 LLM, with advanced reasoning, personalization, and higher emotional intelligence