Best AI tools for< Emcee >
Infographic
7 - AI tool Sites
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Exceed.ai
Exceed.ai is a Conversational Marketing and Sales Platform that leverages AI to automate conversations across the revenue lifecycle. It offers AI-powered assistants that engage with leads and customers through personalized human-like conversations over Email, Website Chat, and SMS. The platform helps in capturing, engaging, qualifying, and scheduling meetings with leads at scale, disrupting sales and marketing unit economics. Exceed.ai is trusted by marketers of leading companies and has been proven to increase productivity, generate more sales-ready leads, and achieve sales quotas. It seamlessly integrates with major CRMs, marketing automation, and sales automation platforms.
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Sense Talent Engagement Platform
Sense Talent Engagement Platform is an AI-powered recruitment platform that offers a comprehensive suite of tools to streamline the hiring process. It provides automation workflows, database cleanup, interview scheduling, text messaging, mass texting, WhatsApp and SMS integration, mobile app support, candidate matching, AI chatbot, job matching, scheduling bot, smart FAQ, pre-screening, sourcing, live chat, instant apply, talent CRM, generative AI, voice AI, referrals, analytics, and more. The platform caters to various industries such as financial services, healthcare, logistics, manufacturing, retail, staffing, technology, and more, helping organizations attract, engage, and retain top talent efficiently.
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Vista Social
Vista Social is a comprehensive social media management platform designed for brands and agencies. It offers a suite of powerful features to help users plan, collaborate, publish, engage, analyze, and listen to social media content. Vista Social is powered by ChatGPT, which enables users to generate and enhance content, automate tasks, and gain insights from social media data.
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Miros
Miros is an AI-powered ecommerce search tool that enhances the shopping experience for users by providing fast and accurate search results. It uses visual and semantic AI algorithms to understand product imagery and metadata, enabling users to find items without the need for text entry. Miros offers features such as Wordless Search, Discovery Bar, Inline Recommendations, Shop The Look, and Find by Image. The tool is designed to help retailers improve customer engagement, increase conversions, and enhance the overall shopping experience.
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Yellow.ai
Yellow.ai is a leading provider of AI-powered customer service automation solutions. Its Dynamic Automation Platform (DAP) is built on multi-LLM architecture and continuously trains on billions of conversations for scale, speed, and accuracy. Yellow.ai's platform leverages the latest advancements in NLP and generative AI to deliver empathetic and context-aware conversations that exceed customer expectations across channels. With its enterprise-grade security, advanced analytics, and zero-setup bot deployment, Yellow.ai helps businesses transform their customer and employee experiences with AI-powered automation.
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ChatNode
ChatNode is a powerful AI chatbot builder designed for businesses of all sizes. It allows users to create next-generation chatbots that are knowledgeable about their business. With features like online and offline document integration, custom prompts, and multi-language support, ChatNode empowers businesses to provide instant and accurate AI-powered responses to their customers. The platform is GDPR compliant, ensuring data privacy and security. ChatNode aims to exceed customer expectations by offering a user-friendly interface for testing and deploying chatbots at a fast speed.
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MyLoans.ai
MyLoans.ai is an AI-powered platform that offers free guidance for managing complex student loans. It provides personalized advice to borrowers, helping them save thousands of dollars by avoiding expensive advisors and navigating through confusing government websites. The platform has assisted over 10,000 borrowers, resulting in savings exceeding $120 million. Users can interact with an AI assistant to get instant answers about their student loans, access the latest news on loan plans, and receive tailored advice on refinancing options. Testimonials highlight the platform's intuitive calculator, clear advice, and user-friendly interface, making it a valuable resource for individuals seeking simplified loan repayment strategies.
20 - Open Source Tools
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crystal-text-llm
This repository contains the code for the paper Fine-Tuned Language Models Generate Stable Inorganic Materials as Text. It demonstrates how finetuned LLMs can be used to generate stable materials, match or exceed the performance of domain specific models, mutate existing materials, and sample crystal structures conditioned on text descriptions. The method is distinct from CrystaLLM, which trains language models from scratch on CIF-formatted crystals.
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llama-on-lambda
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.
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Pai-Megatron-Patch
Pai-Megatron-Patch is a deep learning training toolkit built for developers to train and predict LLMs & VLMs by using Megatron framework easily. With the continuous development of LLMs, the model structure and scale are rapidly evolving. Although these models can be conveniently manufactured using Transformers or DeepSpeed training framework, the training efficiency is comparably low. This phenomenon becomes even severer when the model scale exceeds 10 billion. The primary objective of Pai-Megatron-Patch is to effectively utilize the computational power of GPUs for LLM. This tool allows convenient training of commonly used LLM with all the accelerating techniques provided by Megatron-LM.
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ChatLaw
ChatLaw is an open-source legal large language model tailored for Chinese legal scenarios. It aims to combine LLM and knowledge bases to provide solutions for legal scenarios. The models include ChatLaw-13B and ChatLaw-33B, trained on various legal texts to construct dialogue data. The project focuses on improving logical reasoning abilities and plans to train models with parameters exceeding 30B for better performance. The dataset consists of forum posts, news, legal texts, judicial interpretations, legal consultations, exam questions, and court judgments, cleaned and enhanced to create dialogue data. The tool is designed to assist in legal tasks requiring complex logical reasoning, with a focus on accuracy and reliability.
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parlant
Parlant is a structured approach to building and guiding customer-facing AI agents. It allows developers to create and manage robust AI agents, providing specific feedback on agent behavior and helping understand user intentions better. With features like guidelines, glossary, coherence checks, dynamic context, and guided tool use, Parlant offers control over agent responses and behavior. Developer-friendly aspects include instant changes, Git integration, clean architecture, and type safety. It enables confident deployment with scalability, effective debugging, and validation before deployment. Parlant works with major LLM providers and offers client SDKs for Python and TypeScript. The tool facilitates natural customer interactions through asynchronous communication and provides a chat UI for testing new behaviors before deployment.
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abliterator
abliterator.py is a simple Python library/structure designed to ablate features in large language models (LLMs) supported by TransformerLens. It provides capabilities to enter temporary contexts, cache activations with N samples, calculate refusal directions, and includes tokenizer utilities. The library aims to streamline the process of experimenting with ablation direction turns by encapsulating useful logic and minimizing code complexity. While currently basic and lacking comprehensive documentation, the library serves well for personal workflows and aims to expand beyond feature ablation to augmentation and additional features over time with community support.
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aiomultiprocess
aiomultiprocess is a Python library that combines AsyncIO and multiprocessing to achieve high levels of concurrency in Python applications. It allows running a full AsyncIO event loop on each child process, enabling multiple coroutines to execute simultaneously. The library provides a simple interface for executing asynchronous tasks on a pool of worker processes, making it easy to gather large amounts of network requests quickly. aiomultiprocess is designed to take Python codebases to the next level of performance by leveraging the combined power of AsyncIO and multiprocessing.
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long-context-attention
Long-Context-Attention (YunChang) is a unified sequence parallel approach that combines the strengths of DeepSpeed-Ulysses-Attention and Ring-Attention to provide a versatile and high-performance solution for long context LLM model training and inference. It addresses the limitations of both methods by offering no limitation on the number of heads, compatibility with advanced parallel strategies, and enhanced performance benchmarks. The tool is verified in Megatron-LM and offers best practices for 4D parallelism, making it suitable for various attention mechanisms and parallel computing advancements.
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cursive-py
Cursive is a universal and intuitive framework for interacting with LLMs. It is extensible, allowing users to hook into any part of a completion life cycle. Users can easily describe functions that LLMs can use with any supported model. Cursive aims to bridge capabilities between different models, providing a single interface for users to choose any model. It comes with built-in token usage and costs calculations, automatic retry, and model expanding features. Users can define and describe functions, generate Pydantic BaseModels, hook into completion life cycle, create embeddings, and configure retry and model expanding behavior. Cursive supports various models from OpenAI, Anthropic, OpenRouter, Cohere, and Replicate, with options to pass API keys for authentication.
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telegram-summary-bot
Telegram group summary bot is a tool designed to help users manage large group chats on Telegram by summarizing and searching messages. It allows users to easily read and search through messages in groups with high message volume. The bot stores chat history in a database and provides features such as summarizing messages, searching for specific words, answering questions based on group chat, and checking bot status. Users can deploy their own instance of the bot to avoid limitations on message history and interactions with other bots. The tool is free to use and integrates with services like Cloudflare Workers and AI Gateway for enhanced functionality.
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griptape
Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.
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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} }
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opening-up-chatgpt.github.io
This repository provides a curated list of open-source projects that implement instruction-tuned large language models (LLMs) with reinforcement learning from human feedback (RLHF). The projects are evaluated in terms of their openness across a predefined set of criteria in the areas of Availability, Documentation, and Access. The goal of this repository is to promote transparency and accountability in the development and deployment of LLMs.
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InfLLM
InfLLM is a training-free memory-based method that unveils the intrinsic ability of LLMs to process streaming long sequences. It stores distant contexts into additional memory units and employs an efficient mechanism to lookup token-relevant units for attention computation. Thereby, InfLLM allows LLMs to efficiently process long sequences while maintaining the ability to capture long-distance dependencies. Without any training, InfLLM enables LLMs pre-trained on sequences of a few thousand tokens to achieve superior performance than competitive baselines continually training these LLMs on long sequences. Even when the sequence length is scaled to 1, 024K, InfLLM still effectively captures long-distance dependencies.
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discord-llm-chatbot
llmcord.py enables collaborative LLM prompting in your Discord server. It works with practically any LLM, remote or locally hosted. ### Features ### Reply-based chat system Just @ the bot to start a conversation and reply to continue. Build conversations with reply chains! You can do things like: - Build conversations together with your friends - "Rewind" a conversation simply by replying to an older message - @ the bot while replying to any message in your server to ask a question about it Additionally: - Back-to-back messages from the same user are automatically chained together. Just reply to the latest one and the bot will see all of them. - You can seamlessly move any conversation into a thread. Just create a thread from any message and @ the bot inside to continue. ### Choose any LLM Supports remote models from OpenAI API, Mistral API, Anthropic API and many more thanks to LiteLLM. Or run a local model with ollama, oobabooga, Jan, LM Studio or any other OpenAI compatible API server. ### And more: - Supports image attachments when using a vision model - Customizable system prompt - DM for private access (no @ required) - User identity aware (OpenAI API only) - Streamed responses (turns green when complete, automatically splits into separate messages when too long, throttled to prevent Discord ratelimiting) - Displays helpful user warnings when appropriate (like "Only using last 20 messages", "Max 5 images per message", etc.) - Caches message data in a size-managed (no memory leaks) and per-message mutex-protected (no race conditions) global dictionary to maximize efficiency and minimize Discord API calls - Fully asynchronous - 1 Python file, ~200 lines of code
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chatgpt-subtitle-translator
This tool utilizes the OpenAI ChatGPT API to translate text, with a focus on line-based translation, particularly for SRT subtitles. It optimizes token usage by removing SRT overhead and grouping text into batches, allowing for arbitrary length translations without excessive token consumption while maintaining a one-to-one match between line input and output.
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PowerInfer
PowerInfer is a high-speed Large Language Model (LLM) inference engine designed for local deployment on consumer-grade hardware, leveraging activation locality to optimize efficiency. It features a locality-centric design, hybrid CPU/GPU utilization, easy integration with popular ReLU-sparse models, and support for various platforms. PowerInfer achieves high speed with lower resource demands and is flexible for easy deployment and compatibility with existing models like Falcon-40B, Llama2 family, ProSparse Llama2 family, and Bamboo-7B.
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mentat
Mentat is an AI tool designed to assist with coding tasks directly from the command line. It combines human creativity with computer-like processing to help users understand new codebases, add new features, and refactor existing code. Unlike other tools, Mentat coordinates edits across multiple locations and files, with the context of the project already in mind. The tool aims to enhance the coding experience by providing seamless assistance and improving edit quality.
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aika
AIKA (Artificial Intelligence for Knowledge Acquisition) is a new type of artificial neural network designed to mimic the behavior of a biological brain more closely and bridge the gap to classical AI. The network conceptually separates activations from neurons, creating two separate graphs to represent acquired knowledge and inferred information. It uses different types of neurons and synapses to propagate activation values, binding signals, causal relations, and training gradients. The network structure allows for flexible topology and supports the gradual population of neurons and synapses during training.
2 - OpenAI Gpts
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AI.EX Wedding Speech Consultant
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