Best AI tools for< Language Model Researcher >

ChatTTS is an open-source text-to-speech model designed for dialogue scenarios, supporting both English and Chinese speech generation. Trained on approximately 100,000 hours of Chinese and English data, it delivers speech quality comparable to human dialogue. The tool is particularly suitable for tasks involving large language model assistants and creating dialogue-based audio and video introductions. It provides developers with a powerful and easy-to-use tool based on open-source natural language processing and speech synthesis technologies.

Iflow is an AI assistant application designed to help users efficiently acquire knowledge in various areas, whether it's for daily entertainment, general life knowledge, or professional academic research. It provides real-time answers to questions, summarizes lengthy articles, and assists in structuring documents to enhance creativity and productivity. With Iflow, users can easily enter a state of flow where knowledge flows effortlessly. The application covers a wide range of topics and is equipped with advanced natural language processing capabilities to cater to diverse user needs.

MindpoolAI is a tool that allows users to access multiple leading AI models with a single query. This means that users can get the answers they are looking for, spark ideas, and fuel their work, creativity, and curiosity. MindpoolAI is easy to use and does not require any technical expertise. Users simply need to enter their prompt and select the AI models they want to compare. MindpoolAI will then send the query to the selected models and present the results in an easy-to-understand format.

FunBlocks is an AI reading and writing assistant application that aims to unleash users' full potential by providing a suite of AI-powered tools for brainstorming, writing, reading, and creating presentations. It offers features such as AI Flow for creativity enhancement, AI Extension for boosting efficiency, and AI Writer and AI Slides for content creation. Users can benefit from personalized AI solutions, seamless collaboration ecosystem, and innovative prompts tailored to their needs. FunBlocks caters to various user roles, including students, content creators, researchers, and professionals, by offering a comprehensive support system for knowledge work.

Langtrace AI is an open-source observability tool powered by Scale3 Labs that helps monitor, evaluate, and improve LLM (Large Language Model) applications. It collects and analyzes traces and metrics to provide insights into the ML pipeline, ensuring security through SOC 2 Type II certification. Langtrace supports popular LLMs, frameworks, and vector databases, offering end-to-end observability and the ability to build and deploy AI applications with confidence.

ChatGPT is a large language model developed by OpenAI. It is designed to understand and generate human-like text, and can be used for a variety of tasks such as answering questions, writing stories, and translating languages. ChatGPT is free to use, and can be accessed through a web interface or via an API.

GPT and Gemini are two of the most popular AI-powered chatbots available today. Both chatbots are capable of generating human-like text, answering questions, and providing information. However, there are some key differences between the two chatbots.

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.

NSFW Characters AI is a cutting-edge development in artificial intelligence that has taken the world of conversational AI by storm. This technology introduces a fresh perspective on communication, emphasizing freedom of expression, personalized experiences, and the removal of content filters. With NSFW Characters AI, users can engage in unfiltered conversations, interact with AI girlfriends, and explore a world of imaginative and creative dialogues. It represents a paradigm shift in the world of AI-driven conversations, where the focus is on genuine, unfiltered interactions.

MiniGPT-4 is a powerful AI tool that combines a vision encoder with a large language model (LLM) to enhance vision-language understanding. It can generate detailed image descriptions, create websites from handwritten drafts, write stories and poems inspired by images, provide solutions to problems shown in images, and teach users how to cook based on food photos. MiniGPT-4 is highly computationally efficient and easy to use, making it a valuable tool for a wide range of applications.

Free ChatGPT Omni (GPT4o) is a user-friendly website that allows users to effortlessly chat with ChatGPT for free. It is designed to be accessible to everyone, regardless of language proficiency or technical expertise. GPT4o is OpenAI's groundbreaking multimodal language model that integrates text, audio, and visual inputs and outputs, revolutionizing human-computer interaction. The website offers real-time audio interaction, multimodal integration, advanced language understanding, vision capabilities, improved efficiency, and safety measures.

Claude is a large multi-modal model, trained by Google. It is similar to GPT-3, but it is trained on a larger dataset and with more advanced techniques. Claude is capable of generating human-like text, translating languages, answering questions, and writing different kinds of creative content.

Chat GPT Demo is a free-to-use online tool that allows users to interact with a powerful AI language model developed by OpenAI. This advanced tool is designed to generate human-like text, engage in conversations, answer questions, and assist with a wide range of writing tasks. With its user-friendly interface and advanced capabilities, Chat GPT Demo empowers users to explore the possibilities of AI and enhance their productivity. The tool is particularly valuable for individuals seeking assistance with content creation, research, and communication.

Chat GPT is an AI-powered chatbot developed by OpenAI, a leading research company in the field of artificial intelligence. It is designed to understand and generate human-like text, making it a versatile tool for a wide range of applications. With its advanced language processing capabilities, Chat GPT can engage in natural conversations, answer questions, provide information, and even create creative content. It is accessible online and can be integrated into various platforms and applications to enhance their functionality.

Anywhere GPT is a web-based platform that allows users to access a large language model, similar to ChatGPT, without the need to install any software or create an account. The platform is designed to be simple and easy to use, with a focus on providing users with quick and accurate responses to their questions and requests.

Promptstacks is a community-driven platform where people can share and discover Generative AI tips and tricks. Users can also discuss prompt engineering and general industry news. The goal of prompt engineering is typically to generate more relevant, coherent or accurate output from a large language model such as ChatGPT or Bard.

Google Gemma is a lightweight, state-of-the-art open language model (LLM) developed by Google. It is part of the same research used in the creation of Google's Gemini models. Gemma models come in two sizes, the 2B and 7B parameter versions, where each has a base (pre-trained) and instruction-tuned modifications. Gemma models are designed to be cross-device compatible and optimized for Google Cloud and NVIDIA GPUs. They are also accessible through Kaggle, Hugging Face, Google Cloud with Vertex AI or GKE. Gemma models can be used for a variety of applications, including text generation, summarization, RAG, and both commercial and research use.

Gemini is a multi-modal AI model, developed by Google. It is designed to understand and generate human language, and can be used for a variety of tasks, including question answering, translation, and dialogue generation. ChatGPT is a large language model, developed by OpenAI. It is also designed to understand and generate human language, and can be used for a variety of tasks, including question answering, translation, and dialogue generation.

Reflection 70B is a next-gen open-source LLM powered by Llama 70B, offering groundbreaking self-correction capabilities that outsmart GPT-4. It provides advanced AI-powered conversations, assists with various tasks, and excels in accuracy and reliability. Users can engage in human-like conversations, receive assistance in research, coding, creative writing, and problem-solving, all while benefiting from its innovative self-correction mechanism. Reflection 70B sets new standards in AI performance and is designed to enhance productivity and decision-making across multiple domains.

Dot is a free, locally-run language model that allows users to interact with their own documents, chat with the model, and use the model for a variety of tasks, all without sending their data away. It is powered by the Mistral 7B LLM, which means it can run locally on a user's device and does not give away any of their data. Dot can also run offline.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

LangChain.dart is a Dart port of the popular LangChain Python framework created by Harrison Chase. LangChain provides a set of ready-to-use components for working with language models and a standard interface for chaining them together to formulate more advanced use cases (e.g. chatbots, Q&A with RAG, agents, summarization, extraction, etc.). The components can be grouped into a few core modules: * **Model I/O:** LangChain offers a unified API for interacting with various LLM providers (e.g. OpenAI, Google, Mistral, Ollama, etc.), allowing developers to switch between them with ease. Additionally, it provides tools for managing model inputs (prompt templates and example selectors) and parsing the resulting model outputs (output parsers). * **Retrieval:** assists in loading user data (via document loaders), transforming it (with text splitters), extracting its meaning (using embedding models), storing (in vector stores) and retrieving it (through retrievers) so that it can be used to ground the model's responses (i.e. Retrieval-Augmented Generation or RAG). * **Agents:** "bots" that leverage LLMs to make informed decisions about which available tools (such as web search, calculators, database lookup, etc.) to use to accomplish the designated task. The different components can be composed together using the LangChain Expression Language (LCEL).

LoLLMs Server is a text generation server based on large language models. It provides a Flask-based API for generating text using various pre-trained language models. This server is designed to be easy to install and use, allowing developers to integrate powerful text generation capabilities into their applications.

This project provides a proof of concept for deploying a scalable, serverless LLM Generative AI inference engine on AWS Lambda. It leverages the llama.cpp project to enable the usage of more accessible CPU and RAM configurations instead of limited and expensive GPU capabilities. By deploying a container with the llama.cpp converted models onto AWS Lambda, this project offers the advantages of scale, minimizing cost, and maximizing compute availability. The project includes AWS CDK code to create and deploy a Lambda function leveraging your model of choice, with a FastAPI frontend accessible from a Lambda URL. It is important to note that you will need ggml quantized versions of your model and model sizes under 6GB, as your inference RAM requirements cannot exceed 9GB or your Lambda function will fail.

PPL LLM Serving is a serving based on ppl.nn for various Large Language Models (LLMs). It provides inference support for LLaMA. Key features include: * **High Performance:** Optimized for fast and efficient inference on LLM models. * **Scalability:** Supports distributed deployment across multiple GPUs or machines. * **Flexibility:** Allows for customization of model configurations and inference pipelines. * **Ease of Use:** Provides a user-friendly interface for deploying and managing LLM models. This tool is suitable for various tasks, including: * **Text Generation:** Generating text, stories, or code from scratch or based on a given prompt. * **Text Summarization:** Condensing long pieces of text into concise summaries. * **Question Answering:** Answering questions based on a given context or knowledge base. * **Language Translation:** Translating text between different languages. * **Chatbot Development:** Building conversational AI systems that can engage in natural language interactions. Keywords: llm, large language model, natural language processing, text generation, question answering, language translation, chatbot development

LangChain Go is a Go language implementation of LangChain, a framework for building applications with LLMs through composability. It provides a simple and easy-to-use API for interacting with LLMs, making it easy to add language-based features to your applications.

Bark.cpp is a C/C++ implementation of the Bark model, a real-time, multilingual text-to-speech generation model. It supports AVX, AVX2, and AVX512 for x86 architectures, and is compatible with both CPU and GPU backends. Bark.cpp also supports mixed F16/F32 precision and 4-bit, 5-bit, and 8-bit integer quantization. It can be used to generate realistic-sounding audio from text prompts.

This web interface provides a user-friendly and feature-rich platform for interacting with Ollama Large Language Models (LLMs). It offers a beautiful and intuitive UI inspired by ChatGPT, making it easy for users to get started with LLMs. The interface is fully local, storing chats in local storage for convenience, and fully responsive, allowing users to chat on their phones with the same ease as on a desktop. It features easy setup, code syntax highlighting, and the ability to easily copy codeblocks. Users can also download, pull, and delete models directly from the interface, and switch between models quickly. Chat history is saved and easily accessible, and users can choose between light and dark mode. To use the web interface, users must have Ollama downloaded and running, and Node.js (18+) and npm installed. Installation instructions are provided for running the interface locally. Upcoming features include the ability to send images in prompts, regenerate responses, import and export chats, and add voice input support.

Oterm is a text-based terminal client for Ollama, a large language model. It provides an intuitive and simple terminal UI, allowing users to interact with Ollama without running servers or frontends. Oterm supports multiple persistent chat sessions, which are stored along with context embeddings and system prompt customizations in a SQLite database. Users can easily customize the model's system prompt and parameters, and select from any of the models they have pulled in Ollama or their own custom models. Oterm also supports keyboard shortcuts for creating new chat sessions, editing existing sessions, renaming sessions, exporting sessions as markdown, deleting sessions, toggling between dark and light themes, quitting the application, switching to multiline input mode, selecting images to include with messages, and navigating through the history of previous prompts. Oterm is licensed under the MIT License.

ExLlamaV2 is an inference library for running local LLMs on modern consumer GPUs. It is a faster, better, and more versatile codebase than its predecessor, ExLlamaV1, with support for a new quant format called EXL2. EXL2 is based on the same optimization method as GPTQ and supports 2, 3, 4, 5, 6, and 8-bit quantization. It allows for mixing quantization levels within a model to achieve any average bitrate between 2 and 8 bits per weight. ExLlamaV2 can be installed from source, from a release with prebuilt extension, or from PyPI. It supports integration with TabbyAPI, ExUI, text-generation-webui, and lollms-webui. Key features of ExLlamaV2 include: - Faster and better kernels - Cleaner and more versatile codebase - Support for EXL2 quantization format - Integration with various web UIs and APIs - Community support on Discord

AstrBot is a powerful and versatile tool that leverages the capabilities of large language models (LLMs) like GPT-3, GPT-3.5, and GPT-4 to enhance communication and automate tasks. It seamlessly integrates with popular messaging platforms such as QQ, QQ Channel, and Telegram, enabling users to harness the power of AI within their daily conversations and workflows.

Groq2API is a REST API wrapper around the Groq2 model, a large language model trained by Google. The API allows you to send text prompts to the model and receive generated text responses. The API is easy to use and can be integrated into a variety of applications.

Wllama is a WebAssembly binding for llama.cpp, a high-performance and lightweight language model library. It enables you to run inference directly on the browser without the need for a backend or GPU. Wllama provides both high-level and low-level APIs, allowing you to perform various tasks such as completions, embeddings, tokenization, and more. It also supports model splitting, enabling you to load large models in parallel for faster download. With its Typescript support and pre-built npm package, Wllama is easy to integrate into your React Typescript projects.

This repository contains Python scripts that enable two-way voice conversations with Anthropic Claude, utilizing ElevenLabs for text-to-speech, Faster-Whisper for speech-to-text, and Pygame for audio playback. The tool operates by transcribing human audio using Faster-Whisper, sending the transcription to Anthropic Claude for response generation, and converting the LLM's response into audio using ElevenLabs. The audio is then played back through Pygame, allowing for a seamless and interactive conversation between the user and the AI. The repository includes variations of the main script to support different operating systems and configurations, such as using CPU transcription on Linux or employing the AssemblyAI API instead of Faster-Whisper.

This project aims to create a RESTful API server compatible with the OpenAI API using open-source backends like llama/llama2. With this project, various GPT tools/frameworks can be compatible with your own model. Key features include: - **Compatibility with OpenAI API**: The API server follows the OpenAI API structure, allowing seamless integration with existing tools and frameworks. - **Support for Multiple Backends**: The server supports both llama.cpp and pyllama backends, providing flexibility in model selection. - **Customization Options**: Users can configure model parameters such as temperature, top_p, and top_k to fine-tune the model's behavior. - **Batch Processing**: The API supports batch processing for embeddings, enabling efficient handling of multiple inputs. - **Token Authentication**: The server utilizes token authentication to secure access to the API. This tool is particularly useful for developers and researchers who want to integrate large language models into their applications or explore custom models without relying on proprietary APIs.

LLM-Blender is a framework for ensembling large language models (LLMs) to achieve superior performance. It consists of two modules: PairRanker and GenFuser. PairRanker uses pairwise comparisons to distinguish between candidate outputs, while GenFuser merges the top-ranked candidates to create an improved output. LLM-Blender has been shown to significantly surpass the best LLMs and baseline ensembling methods across various metrics on the MixInstruct benchmark dataset.

The Alignment Handbook provides robust training recipes for continuing pretraining and aligning language models with human and AI preferences. It includes techniques such as continued pretraining, supervised fine-tuning, reward modeling, rejection sampling, and direct preference optimization (DPO). The handbook aims to fill the gap in public resources on training these models, collecting data, and measuring metrics for optimal downstream performance.

This project is a personal implementation and reproduction of a small-parameter Chinese LLM. It mainly refers to these two open source projects: https://github.com/charent/Phi2-mini-Chinese and https://github.com/DLLXW/baby-llama2-chinese. It includes the complete process of pre-training, SFT instruction fine-tuning, DPO, and PPO (to be done). I hope to share it with everyone and hope that everyone can work together to improve it!

LLM.swift is a simple and readable library that allows you to interact with large language models locally with ease for macOS, iOS, watchOS, tvOS, and visionOS. It's a lightweight abstraction layer over `llama.cpp` package, so that it stays as performant as possible while is always up to date. Theoretically, any model that works on `llama.cpp` should work with this library as well. It's only a single file library, so you can copy, study and modify the code however you want.

Kannada GPT

I'm a wizard who believes in magic.

HackingPT is a specialized language model focused on cybersecurity and penetration testing, committed to providing precise and in-depth insights in these fields.

Next level high accuracy GPT5 model

ChatGPT официальная русская версия

专门优化和翻译不同LLM的提示词的专家，基于用户选择和LLM特性。

大型语言模型的学习和训练指导专家

A GPT from 1723, unaware of post-1723 events

提供专业中文论文润色、内容研究与大语言模型学术分析服务。

An LLM specialist aiding in learning and understanding LLMs.

Daily updates on LLM news and trends

Specializes in summarizing LLM news and papers from the past week.

Regele GPT ce încearcă să 'paseze' răspunsuri precise și să 'marcheze' puncte cu inteligența sa artificială.

解释大模型原理，指导技术新手实践

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

──ようこそ、担当職員。

历时几十小时反复打磨的英文论文无痛手把手拆解分析，结果如果不满意，可以提出要求更详尽举例解释😄 💡更多好用 gpts，可入微信群获取 微信号： lucia00112233，小红书可搜：学术巧学 Lucia

GPT teached using a mental models

去网上找东西，帮我总结好，弄出思维导图。