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 application 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 application aims to improve team dynamics, support, and engagement by offering a snapshot of contributions, challenges, and engagement levels. Supermanage AI offers features such as automated snapshots, Slack integration, and data privacy measures to ensure a seamless and secure user experience.

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.

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.

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.

Xorbits Inference (Xinference) is a powerful and versatile library designed to serve language, speech recognition, and multimodal models. With Xorbits Inference, you can effortlessly deploy and serve your or state-of-the-art built-in models using just a single command. Whether you are a researcher, developer, or data scientist, Xorbits Inference empowers you to unleash the full potential of cutting-edge AI models.

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.

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.

This repository is a collection of research notes and resources focusing on training large language models (LLMs) and Reinforcement Learning from Human Feedback (RLHF). It includes methodologies, techniques, and state-of-the-art approaches for optimizing preferences and model alignment in LLM training. The purpose is to serve as a reference for researchers and engineers interested in reinforcement learning, large language models, model alignment, and alternative RL-based methods.

This project is a Chinese tutorial for domestic beginners based on the AutoDL platform, providing full-process guidance for various open-source large models, including environment configuration, local deployment, and efficient fine-tuning. It simplifies the deployment, use, and application process of open-source large models, enabling more ordinary students and researchers to better use open-source large models and helping open and free large models integrate into the lives of ordinary learners faster.

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.

LLM-TPU project aims to deploy various open-source generative AI models on the BM1684X chip, with a focus on LLM. Models are converted to bmodel using TPU-MLIR compiler and deployed to PCIe or SoC environments using C++ code. The project has deployed various open-source models such as Baichuan2-7B, ChatGLM3-6B, CodeFuse-7B, DeepSeek-6.7B, Falcon-40B, Phi-3-mini-4k, Qwen-7B, Qwen-14B, Qwen-72B, Qwen1.5-0.5B, Qwen1.5-1.8B, Llama2-7B, Llama2-13B, LWM-Text-Chat, Mistral-7B-Instruct, Stable Diffusion, Stable Diffusion XL, WizardCoder-15B, Yi-6B-chat, Yi-34B-chat. Detailed model deployment information can be found in the 'models' subdirectory of the project. For demonstrations, users can follow the 'Quick Start' section. For inquiries about the chip, users can contact SOPHGO via the official website.

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.

The 'scaling-book' repository contains a book that aims to demystify the art of scaling Large Language Models (LLMs) on Tensor Processing Units (TPUs). It explains how TPUs work, how LLMs run at scale, and how to choose parallelism schemes to avoid communication bottlenecks during training and inference. The book provides insights and guidance on scaling models effectively for improved performance.

Kiln is an intuitive tool for fine-tuning LLM models, generating synthetic data, and collaborating on datasets. It offers desktop apps for Windows, MacOS, and Linux, zero-code fine-tuning for various models, interactive data generation, and Git-based version control. Users can easily collaborate with QA, PM, and subject matter experts, generate auto-prompts, and work with a wide range of models and providers. The tool is open-source, privacy-first, and supports structured data tasks in JSON format. Kiln is free to use and helps build high-quality AI products with datasets, facilitates collaboration between technical and non-technical teams, allows comparison of models and techniques without code, ensures structured data integrity, and prioritizes user privacy.

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.

Flutter Gemma is a family of lightweight, state-of-the art open models that bring the power of Google's Gemma language models directly to Flutter applications. It allows for local execution on user devices, supports both iOS and Android platforms, and offers LoRA support for tailored AI behavior. The tool provides a simple interface for integrating Gemma models into Flutter projects, enabling advanced AI capabilities without relying on external servers. Users can easily download pre-trained Gemma models, fine-tune them for specific use cases, and customize behavior using LoRA weights. The tool supports model and LoRA weight management, model initialization, response generation, and chat scenarios, with considerations for model size, LoRA weights, and production app deployment.

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.

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