Best AI tools for< Distill Models >

PlainScribe is a versatile online tool that offers transcription, translation, and summarization services for various media files. Users can effortlessly transcribe their audio and video files, overcome language barriers with translations, and distill key insights through summarization. The platform supports a wide range of file sizes and provides a pay-as-you-go model for cost efficiency. With a focus on privacy and security, PlainScribe automatically deletes user data after 7 days. Additionally, users can benefit from multilingual support, summarized transcripts, and flexible export options like CSV and subtitle formats.

ZoomInfo + Chorus is an AI-powered Conversation Intelligence platform designed for sales teams to capture and analyze customer calls, meetings, and emails. It helps in understanding customer trends, improving sales processes, and driving revenue growth through data-driven insights and actionable intelligence. The platform leverages machine learning technology to provide performance-driven sales teams with visibility, process improvements, and behavior changes to enhance sales effectiveness and deal outcomes.

Blizzy AI is an innovative tool that allows users to have meaningful conversations with their data. Users can chat with any file and access the internet securely. With features like bulk upload knowledge, personalized knowledge vault creation, and ready-made prompts, Blizzy AI enhances marketing strategies, content creation, and online browsing experience. The tool prioritizes privacy and security by not using user data for training purposes and ensuring data accessibility only to the user.

Laxis is a revolutionary AI Meeting Assistant designed to capture and distill key insights from every customer interaction effortlessly. It seamlessly integrates across platforms, from online meetings to CRM updates, all with a user-friendly interface. Laxis empowers revenue teams to maximize every customer conversation, ensuring no valuable detail is missed. With Laxis, sales teams can close more deals with AI note-taking and insights from client conversations, business development teams can engage prospects more effectively and grow their business faster, marketing teams can repurpose podcasts, webinars, and meetings into engaging content with a single click, product and market researchers can conduct better research interviews that get to the "aha!" moment faster, project managers can remember key takeaways and status updates, and capture them for progress reports, and product and UX designers can capture and organize insights from their interviews and user research.

Narratize is a generative AI storytelling platform designed for innovative enterprises to accelerate R&D, product innovation, and marketing solutions. It helps enterprise teams and individuals to distill scientific, technical, and medical insights into impactful content that scales. With Narratize, users can generate various types of content quickly without the need for prompt engineering, improving communication and collaboration within the organization.

Crayon is a competitive intelligence software that helps businesses track competitors, win more deals, and stay ahead in the market. Powered by AI, Crayon enables users to analyze, enable, compete, and measure their competitive landscape efficiently. The platform offers features such as competitor monitoring, AI news summarization, importance scoring, content creation, sales enablement, performance metrics, and more. With Crayon, users can receive high-priority insights, distill articles about competitors, create battlecards, find intel to win deals, and track performance metrics. The application aims to make competitive intelligence seamless and impactful for sales teams.

Swipe Insight is a mobile application that provides users with daily updates on digital marketing and analytics trends, news, and strategies. The app features a personalized feed that adapts to the user's preferences, intelligent insights that distill complex topics into concise summaries, and a curated selection of content from over 100 trusted sources. Swipe Insight is designed to help users stay ahead in the industry with just minutes of reading per day.

AnyLearn.ai is an AI-powered platform that offers a wide range of courses and guides generated by artificial intelligence. It provides users with the opportunity to learn about various topics in a structured and comprehensive manner. The platform leverages the power of AI to create personalized learning experiences for individuals seeking to enhance their knowledge and skills.

Supermanage AI is an AI-powered tool designed to streamline and enhance 1-on-1 meetings for managers. It provides personalized insights by distilling information from public Slack channels, enabling managers to have more meaningful interactions with their team members. The tool aims to improve team dynamics, support, and engagement by offering a snapshot of contributions, challenges, and engagement levels. Supermanage AI offers features such as effortless preparation for meetings, customized snapshots, and Slack integration. It is a valuable resource for managers looking to strengthen connections and help their teams thrive.

The AI Index is a comprehensive resource for data and insights on artificial intelligence. It provides unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, journalists, executives, and the general public to develop a deeper understanding of the complex field of AI. The AI Index tracks, collates, distills, and visualizes data relating to artificial intelligence. This includes data on research and development, technical performance and ethics, the economy and education, AI policy and governance, diversity, public opinion, and more.

The Video Calling App is an AI-powered platform designed to revolutionize meeting experiences by providing laser-focused, context-aware, and outcome-driven meetings. It aims to streamline post-meeting routines, enhance collaboration, and improve overall meeting efficiency. With powerful integrations and AI features, the app captures, organizes, and distills meeting content to provide users with a clearer perspective and free headspace. It offers seamless integration with popular tools like Slack, Linear, and Google Calendar, enabling users to automate tasks, manage schedules, and enhance productivity. The app's user-friendly interface, interactive features, and advanced search capabilities make it a valuable tool for global teams and remote workers seeking to optimize their meeting experiences.

Model2Vec is a technique to turn any sentence transformer into a really small static model, reducing model size by 15x and making the models up to 500x faster, with a small drop in performance. It outperforms other static embedding models like GLoVe and BPEmb, is lightweight with only `numpy` as a major dependency, offers fast inference, dataset-free distillation, and is integrated into Sentence Transformers, txtai, and Chonkie. Model2Vec creates powerful models by passing a vocabulary through a sentence transformer model, reducing dimensionality using PCA, and weighting embeddings using zipf weighting. Users can distill their own models or use pre-trained models from the HuggingFace hub. Evaluation can be done using the provided evaluation package. Model2Vec is licensed under MIT.

The Google Gen AI SDK is a Python library that provides access to Google AI and Vertex AI services. It allows users to create clients for different services, work with parameter types, models, generate content, call functions, handle JSON response schemas, stream text and image content, perform async operations, count and compute tokens, embed content, generate and upscale images, edit images, work with files, create and get cached content, tune models, distill models, perform batch predictions, and more. The SDK supports various features like automatic function support, manual function declaration, JSON response schema support, streaming for text and image content, async methods, tuning job APIs, distillation, batch prediction, and more.

DataDreamer is a powerful open-source Python library designed for prompting, synthetic data generation, and training workflows. It is simple, efficient, and research-grade, allowing users to create prompting workflows, generate synthetic datasets, and train models with ease. The library is built for researchers, by researchers, focusing on correctness, best practices, and reproducibility. It offers features like aggressive caching, resumability, support for bleeding-edge techniques, and easy sharing of datasets and models. DataDreamer enables users to run multi-step prompting workflows, generate synthetic datasets for various tasks, and train models by aligning, fine-tuning, instruction-tuning, and distilling them using existing or synthetic data.

RAG-Retrieval is an end-to-end code repository that provides training, inference, and distillation capabilities for the RAG retrieval model. It supports fine-tuning of various open-source RAG retrieval models, including embedding models, late interactive models, and reranker models. The repository offers a lightweight Python library for calling different RAG ranking models and allows distillation of LLM-based reranker models into bert-based reranker models. It includes features such as support for end-to-end fine-tuning, distillation of large models, advanced algorithms like MRL, multi-GPU training strategy, and a simple code structure for easy modifications.

Awesome-LLM-Large-Language-Models-Notes is a repository that provides a comprehensive collection of information on various Large Language Models (LLMs) classified by year, size, and name. It includes details on known LLM models, their papers, implementations, and specific characteristics. The repository also covers LLM models classified by architecture, must-read papers, blog articles, tutorials, and implementations from scratch. It serves as a valuable resource for individuals interested in understanding and working with LLMs in the field of Natural Language Processing (NLP).

This repository is a collection of resources focused on interpretability in large language models (LLMs). It aims to help beginners get started in the area and keep researchers updated on the latest progress. It includes libraries, blogs, tutorials, forums, tools, programs, papers, and more related to interpretability in LLMs.

This repository, Awesome Efficient AIGC, collects efficient approaches for AI-generated content (AIGC) to cope with its huge demand for computing resources. It includes efficient Large Language Models (LLMs), Diffusion Models (DMs), and more. The repository is continuously improving and welcomes contributions of works like papers and repositories that are missed by the collection.

vnc-lm is a Discord bot designed for messaging with language models. Users can configure model parameters, branch conversations, and edit prompts to enhance responses. The bot supports various providers like OpenAI, Huggingface, and Cloudflare Workers AI. It integrates with ollama and LiteLLM, allowing users to access a wide range of language model APIs through a single interface. Users can manage models, switch between models, split long messages, and create conversation branches. LiteLLM integration enables support for OpenAI-compatible APIs and local LLM services. The bot requires Docker for installation and can be configured through environment variables. Troubleshooting tips are provided for common issues like context window problems, Discord API errors, and LiteLLM issues.

BoCoEL is a tool that leverages Bayesian Optimization to efficiently evaluate large language models by selecting a subset of the corpus for evaluation. It encodes individual entries into embeddings, uses Bayesian optimization to select queries, retrieves from the corpus, and provides easily managed evaluations. The tool aims to reduce computation costs during evaluation with a dynamic budget, supporting models like GPT2, Pythia, and LLAMA through integration with Hugging Face transformers and datasets. BoCoEL offers a modular design and efficient representation of the corpus to enhance evaluation quality.

The 'unilm' repository is a collection of tools, models, and architectures for Foundation Models and General AI, focusing on tasks such as NLP, MT, Speech, Document AI, and Multimodal AI. It includes various pre-trained models, such as UniLM, InfoXLM, DeltaLM, MiniLM, AdaLM, BEiT, LayoutLM, WavLM, VALL-E, and more, designed for tasks like language understanding, generation, translation, vision, speech, and multimodal processing. The repository also features toolkits like s2s-ft for sequence-to-sequence fine-tuning and Aggressive Decoding for efficient sequence-to-sequence decoding. Additionally, it offers applications like TrOCR for OCR, LayoutReader for reading order detection, and XLM-T for multilingual NMT.

The NVIDIA TensorRT Model Optimizer is a library designed to quantize and compress deep learning models for optimized inference on GPUs. It offers state-of-the-art model optimization techniques including quantization and sparsity to reduce inference costs for generative AI models. Users can easily stack different optimization techniques to produce quantized checkpoints from torch or ONNX models. The quantized checkpoints are ready for deployment in inference frameworks like TensorRT-LLM or TensorRT, with planned integrations for NVIDIA NeMo and Megatron-LM. The tool also supports 8-bit quantization with Stable Diffusion for enterprise users on NVIDIA NIM. Model Optimizer is available for free on NVIDIA PyPI, and this repository serves as a platform for sharing examples, GPU-optimized recipes, and collecting community feedback.

Current trends and state of the art for using open & local LLM models as copilots to complete code, generate projects, act as shell assistants, automatically fix bugs, and more. This document is a curated list of local Copilots, shell assistants, and related projects, intended to be a resource for those interested in a survey of the existing tools and to help developers discover the state of the art for projects like these.

A collection of papers related to knowledge distillation of large language models (LLMs). The repository focuses on techniques to transfer advanced capabilities from proprietary LLMs to smaller models, compress open-source LLMs, and refine their performance. It covers various aspects of knowledge distillation, including algorithms, skill distillation, verticalization distillation in fields like law, medical & healthcare, finance, science, and miscellaneous domains. The repository provides a comprehensive overview of the research in the area of knowledge distillation of LLMs.

SLMs-Survey is a comprehensive repository that includes papers and surveys on small language models. It covers topics such as technology, on-device applications, efficiency, enhancements for LLMs, and trustworthiness. The repository provides a detailed overview of existing SLMs, their architecture, enhancements, and specific applications in various domains. It also includes information on SLM deployment optimization techniques and the synergy between SLMs and LLMs.

ChatLLM.cpp is a pure C++ implementation tool for real-time chatting with RAG on your computer. It supports inference of various models ranging from less than 1B to more than 300B. The tool provides accelerated memory-efficient CPU inference with quantization, optimized KV cache, and parallel computing. It allows streaming generation with a typewriter effect and continuous chatting with virtually unlimited content length. ChatLLM.cpp also offers features like Retrieval Augmented Generation (RAG), LoRA, Python/JavaScript/C bindings, web demo, and more possibilities. Users can clone the repository, quantize models, build the project using make or CMake, and run quantized models for interactive chatting.

This repository is an updating survey for Continual Learning of Large Language Models (CL-LLMs), providing a comprehensive overview of various aspects related to the continual learning of large language models. It covers topics such as continual pre-training, domain-adaptive pre-training, continual fine-tuning, model refinement, model alignment, multimodal LLMs, and miscellaneous aspects. The survey includes a collection of relevant papers, each focusing on different areas within the field of continual learning of large language models.

The 'summarize' tool is designed to transcribe and summarize videos from various sources using AI models. It helps users efficiently summarize lengthy videos, take notes, and extract key insights by providing timestamps, original transcripts, and support for auto-generated captions. Users can utilize different AI models via Groq, OpenAI, or custom local models to generate grammatically correct video transcripts and extract wisdom from video content. The tool simplifies the process of summarizing video content, making it easier to remember and reference important information.

A curated list of 50+ resources to help you become a Generative AI Data Scientist. This repository includes resources on building GenAI applications with Large Language Models (LLMs), and deploying LLMs and GenAI with Cloud-based solutions.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

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