Best AI tools for< Support Large Models >

MacWhisper is a native macOS application that utilizes OpenAI's Whisper technology for transcribing audio files into text. It offers a user-friendly interface for recording, transcribing, and editing audio, making it suitable for various use cases such as transcribing meetings, lectures, interviews, and podcasts. The application is designed to protect user privacy by performing all transcriptions locally on the device, ensuring that no data leaves the user's machine.

Stockpulse is an AI-powered platform that analyzes financial news and communities using Artificial Intelligence. It provides decision support for operations by collecting, filtering, and converting unstructured data into processable information. With extensive coverage of financial media sources globally, Stockpulse offers unique historical data, sentiment analysis, and AI-driven insights for various sectors in the financial markets.

Anthropic is a research and deployment company founded in 2021 by former OpenAI researchers Dario Amodei, Daniela Amodei, and Geoffrey Irving. The company is developing large language models, including Claude, a multimodal AI model that can perform a variety of language-related tasks, such as answering questions, generating text, and translating languages.

The Intel Gaudi AI accelerator developer website provides resources, guidance, tools, and support for building, migrating, and optimizing AI models. It offers software, model references, libraries, containers, and tools for training and deploying Generative AI and Large Language Models. The site focuses on the Intel Gaudi accelerators, including tutorials, documentation, and support for developers to enhance AI model performance.

Upstage is an Artificial General Intelligence (AGI) application designed to enhance work productivity by automating simple tasks and providing decision support through generative Business Intelligence (BI) knowledge and numerical understanding. The application offers various features such as Document AI, Solar LLM, and Developers Demo Playground, enabling users to automate tasks, extract key information from documents, and create conversational agents. Upstage aims to streamline workflow automation and improve efficiency in various domains such as healthcare, finance, and law.

Weam is an AI adoption platform designed for digital agencies to supercharge their operations with collaborative AI. It offers a comprehensive suite of tools for simplifying AI implementation, including project management, resource allocation, training modules, and ongoing support to ensure successful AI integration. Weam enables teams to interact and collaborate over their preferred LLMs, facilitating scalability, time-saving, and widespread AI adoption across the organization.

NewsDigest.Ai is an AI-powered platform that provides 24/7 financial news updates. It utilizes advanced AI agents to gather news related to technology, economics, and geopolitics in multiple languages. Users can access real-time news content and stay informed about the latest developments in the financial world.

Thales Labs is a premier AI research lab and incubator empowering entrepreneurs and domain experts to revolutionize industries with large language models and web3. They focus on fostering innovation in sectors like Insurance, Finance, Healthcare, Pharma, Law, and Journalism. The user-friendly app allows experts to build AI applications using their natural language skills, with support from skilled engineers for complex challenges. Join Thales Labs to transform industries, unlock new opportunities, and create value with AI-driven innovation.

LlamaIndex is a leading data framework designed for building LLM (Large Language Model) applications. It allows enterprises to turn their data into production-ready applications by providing functionalities such as loading data from various sources, indexing data, orchestrating workflows, and evaluating application performance. The platform offers extensive documentation, community-contributed resources, and integration options to support developers in creating innovative LLM applications.

DentroChat is an AI chat application that reimagines the way users interact with AI models. It allows users to select from various large language models (LLMs) in different modes, enabling them to choose the best AI for their specific tasks. With seamless mode switching and optimized performance, DentroChat offers flexibility and precision in AI interactions.

Toloka AI is a data labeling platform that empowers AI development by combining human insight with machine learning models. It offers adaptive AutoML, human-in-the-loop workflows, large language models, and automated data labeling. The platform supports various AI solutions with human input, such as e-commerce services, content moderation, computer vision, and NLP. Toloka AI aims to accelerate machine learning processes by providing high-quality human-labeled data and leveraging the power of the crowd.

Predibase is a platform for fine-tuning and serving Large Language Models (LLMs). It provides a cost-effective and efficient way to train and deploy LLMs for a variety of tasks, including classification, information extraction, customer sentiment analysis, customer support, code generation, and named entity recognition. Predibase is built on proven open-source technology, including LoRAX, Ludwig, and Horovod.

Aisera is a generative AI platform that provides various AI-powered solutions for businesses, including AI Copilot, AI Search, AI Assist, and AI Voice Bot. These solutions are designed to automate tasks, improve efficiency, and enhance customer experience. Aisera's AI Copilot acts as a proactive concierge, providing personalized assistance and automating workflows. AI Search offers enterprise-wide search capabilities powered by large language models (LLMs), ensuring personalized and privacy-aware results. AI Assist empowers agents with real-time answers, summaries, and next-best actions, boosting their productivity. AI Voice Bot enables natural language interactions, providing instant support and automating routine tasks.

Cuckoo is an AI interpreter designed for global teams, offering seamless multilingual conversation support for sales, marketing, and customer support interactions. It enables users to effortlessly communicate in multiple languages during meetings and events, adapting to conversations of any size and topic. Cuckoo is powered by large language models, speaks 20+ languages, and can be integrated with popular communication platforms like Zoom, Google Meet, Slack, and Microsoft Teams. The application is user-friendly, requires no prior arrangements or rehearsals, and provides real-time interpretation on the fly.

AnythingLLM is an all-in-one AI application designed for everyone. It offers a comprehensive suite of tools for working with LLMs (Large Language Models), documents, and agents in a fully private manner. Users can download AnythingLLM for Desktop on Windows, MacOS, and Linux, enabling flexible one-click installation. The application supports custom model integration, including closed-source models like GPT-4 and custom fine-tuned models like Llama2. With the ability to handle various document formats beyond PDFs, AnythingLLM provides tailored solutions with locally running defaults for privacy. Additionally, users can access AnythingLLM Cloud for extended functionalities.

Sarvam AI is an AI application focused on leading transformative research in AI to develop, deploy, and distribute Generative AI applications in India. The platform aims to build efficient large language models for India's diverse linguistic culture and enable new GenAI applications through bespoke enterprise models. Sarvam AI is also developing an enterprise-grade platform for developing and evaluating GenAI apps, while contributing to open-source models and datasets to accelerate AI innovation.

TextSynth is an AI tool that provides access to large language models such as Mistral, Llama, Stable Diffusion, Whisper for text-to-image, text-to-speech, and speech-to-text capabilities via a REST API and playground. It employs custom inference code for faster inference on standard GPUs and CPUs. Founded in 2020, TextSynth was among the first to offer access to the GPT-2 language model. The service is free with rate limitations, and users can opt for custom support at a small cost per request. All servers are located in France.

Vectorize is a fast, accurate, and production-ready AI tool that helps users turn unstructured data into optimized vector search indexes. It leverages Large Language Models (LLMs) to create copilots and enhance customer experiences by extracting natural language from various sources. With built-in support for top AI platforms and a variety of embedding models and chunking strategies, Vectorize enables users to deploy real-time vector pipelines for accurate search results. The tool also offers out-of-the-box connectors to popular knowledge repositories and collaboration platforms, making it easy to transform knowledge into AI-generated content.

Puppeteer is an AI application that offers Gen AI Nurses to empower patient support in healthcare. It addresses staffing shortages and enhances access to quality care through personalized and human-like patient experiences. The platform revolutionizes patient intake with features like mental health companions, virtual assistants, streamlined data collection, and clinic customization. Additionally, Puppeteer provides a comprehensive solution for building conversational bots, real-time API and database integrations, and personalized user experiences. It also offers a chatbot service for direct patient interaction and support in psychological help-seeking. The platform is designed to enhance healthcare delivery through AI integration and Large Language Models (LLMs) for modern medical solutions.

Novo AI is an AI application that empowers financial institutions by leveraging Generative AI and Large Language Models to streamline operations, maximize insights, and automate processes like claims processing and customer support traditionally handled by humans. The application helps insurance companies understand claim documents, automate claims processing, optimize pricing strategies, and improve customer satisfaction. For banks, Novo AI automates document processing across multiple languages and simplifies adverse media screenings through efficient research on live internet data.

JittorLLMs is a large model inference library that allows running large models on machines with low hardware requirements. It significantly reduces hardware configuration demands, enabling deployment on ordinary machines with 2GB of memory. It supports various large models and provides a unified environment configuration for users. Users can easily migrate models without modifying any code by installing Jittor version of torch (JTorch). The framework offers fast model loading speed, optimized computation performance, and portability across different computing devices and environments.

ms-swift is an official framework provided by the ModelScope community for fine-tuning and deploying large language models and multi-modal large models. It supports training, inference, evaluation, quantization, and deployment of over 400 large models and 100+ multi-modal large models. The framework includes various training technologies and accelerates inference, evaluation, and deployment modules. It offers a Gradio-based Web-UI interface and best practices for easy application of large models. ms-swift supports a wide range of model types, dataset types, hardware support, lightweight training methods, distributed training techniques, quantization training, RLHF training, multi-modal training, interface training, plugin and extension support, inference acceleration engines, model evaluation, and model quantization.

Efficient Transformers Library provides reimplemented blocks of Large Language Models (LLMs) to make models functional and highly performant on Qualcomm Cloud AI 100. It includes graph transformations, handling for under-flows and overflows, patcher modules, exporter module, sample applications, and unit test templates. The library supports seamless inference on pre-trained LLMs with documentation for model optimization and deployment. Contributions and suggestions are welcome, with a focus on testing changes for model support and common utilities.

TPI-LLM (Tensor Parallelism Inference for Large Language Models) is a system designed to bring LLM functions to low-resource edge devices, addressing privacy concerns by enabling LLM inference on edge devices with limited resources. It leverages multiple edge devices for inference through tensor parallelism and a sliding window memory scheduler to minimize memory usage. TPI-LLM demonstrates significant improvements in TTFT and token latency compared to other models, and plans to support infinitely large models with low token latency in the future.

Evalverse is an open-source project designed to support Large Language Model (LLM) evaluation needs. It provides a standardized and user-friendly solution for processing and managing LLM evaluations, catering to AI research engineers and scientists. Evalverse supports various evaluation methods, insightful reports, and no-code evaluation processes. Users can access unified evaluation with submodules, request evaluations without code via Slack bot, and obtain comprehensive reports with scores, rankings, and visuals. The tool allows for easy comparison of scores across different models and swift addition of new evaluation tools.

NarratoAI is an automated video narration tool that provides an all-in-one solution for script writing, automated video editing, voice-over, and subtitle generation. It is powered by LLM to enhance efficient content creation. The tool aims to simplify the process of creating film commentary and editing videos by automating various tasks such as script writing and voice-over generation. NarratoAI offers a user-friendly interface for users to easily generate video scripts, edit videos, and customize video parameters. With future plans to optimize story generation processes and support additional large models, NarratoAI is a versatile tool for content creators looking to streamline their video production workflow.

AI Novel Writing Assistant is an intelligent productivity tool for novel creation based on AI + prompt words. It has been used by hundreds of studios and individual authors to quickly and batch generate novels. With AI technology to enhance writing efficiency and a comprehensive prompt word management feature, it achieves 20 times efficiency improvement in intelligent book disassembly, intelligent book title and synopsis generation, text polishing, and shift+L quick term insertion, making writing easier and more professional. It has been upgraded to v5.2. The tool supports mind map construction of outlines and chapters, AI self-optimization of novels, writing knowledge base management, shift+L quick term insertion in the text input field, support for any mainstream large models integration, custom skin color, prompt word import and export, support for large text memory, right-click polishing, expansion, and de-AI flavoring of outlines, chapters, and text, multiple sets of novel prompt word library management, and book disassembly function.

PostgresML is a powerful Postgres extension that seamlessly combines data storage and machine learning inference within your database. It enables running machine learning and AI operations directly within PostgreSQL, leveraging GPU acceleration for faster computations, integrating state-of-the-art large language models, providing built-in functions for text processing, enabling efficient similarity search, offering diverse ML algorithms, ensuring high performance, scalability, and security, supporting a wide range of NLP tasks, and seamlessly integrating with existing PostgreSQL tools and client libraries.

PatchWork is an open-source framework designed for automating development tasks using large language models. It enables users to automate workflows such as PR reviews, bug fixing, security patching, and more through a self-hosted CLI agent and preferred LLMs. The framework consists of reusable atomic actions called Steps, customizable LLM prompts known as Prompt Templates, and LLM-assisted automations called Patchflows. Users can run Patchflows locally in their CLI/IDE or as part of CI/CD pipelines. PatchWork offers predefined patchflows like AutoFix, PRReview, GenerateREADME, DependencyUpgrade, and ResolveIssue, with the flexibility to create custom patchflows. Prompt templates are used to pass queries to LLMs and can be customized. Contributions to new patchflows, steps, and the core framework are encouraged, with chat assistants available to aid in the process. The roadmap includes expanding the patchflow library, introducing a debugger and validation module, supporting large-scale code embeddings, parallelization, fine-tuned models, and an open-source GUI. PatchWork is licensed under AGPL-3.0 terms, while custom patchflows and steps can be shared using the Apache-2.0 licensed patchwork template repository.

This repository provides a comprehensive list of academic papers, articles, tutorials, slides, and projects related to Large Language Model (LLM) systems. It covers various aspects of LLM research, including pre-training, serving, system efficiency optimization, multi-model systems, image generation systems, LLM applications in systems, ML systems, survey papers, LLM benchmarks and leaderboards, and other relevant resources. The repository is regularly updated to include the latest developments in this rapidly evolving field, making it a valuable resource for researchers, practitioners, and anyone interested in staying abreast of the advancements in LLM technology.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

This repository contains a hand-curated list of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, Chatbot, and transfer learning in NLP.

SWIFT (Scalable lightWeight Infrastructure for Fine-Tuning) supports training, inference, evaluation and deployment of nearly **200 LLMs and MLLMs** (multimodal large models). Developers can directly apply our framework to their own research and production environments to realize the complete workflow from model training and evaluation to application. In addition to supporting the lightweight training solutions provided by [PEFT](https://github.com/huggingface/peft), we also provide a complete **Adapters library** to support the latest training techniques such as NEFTune, LoRA+, LLaMA-PRO, etc. This adapter library can be used directly in your own custom workflow without our training scripts. To facilitate use by users unfamiliar with deep learning, we provide a Gradio web-ui for controlling training and inference, as well as accompanying deep learning courses and best practices for beginners. Additionally, we are expanding capabilities for other modalities. Currently, we support full-parameter training and LoRA training for AnimateDiff.

EmoLLM is a series of large-scale psychological health counseling models that can support **understanding-supporting-helping users** in the psychological health counseling chain, which is fine-tuned from `LLM` instructions. Welcome everyone to star~⭐⭐. The currently open source `LLM` fine-tuning configurations are as follows:

XTuner is an efficient, flexible, and full-featured toolkit for fine-tuning large models. It supports various LLMs (InternLM, Mixtral-8x7B, Llama 2, ChatGLM, Qwen, Baichuan, ...), VLMs (LLaVA), and various training algorithms (QLoRA, LoRA, full-parameter fine-tune). XTuner also provides tools for chatting with pretrained / fine-tuned LLMs and deploying fine-tuned LLMs with any other framework, such as LMDeploy.

IPEX-LLM is a PyTorch library for running Large Language Models (LLMs) on Intel CPUs and GPUs with very low latency. It provides seamless integration with various LLM frameworks and tools, including llama.cpp, ollama, Text-Generation-WebUI, HuggingFace transformers, and more. IPEX-LLM has been optimized and verified on over 50 LLM models, including LLaMA, Mistral, Mixtral, Gemma, LLaVA, Whisper, ChatGLM, Baichuan, Qwen, and RWKV. It supports a range of low-bit inference formats, including INT4, FP8, FP4, INT8, INT2, FP16, and BF16, as well as finetuning capabilities for LoRA, QLoRA, DPO, QA-LoRA, and ReLoRA. IPEX-LLM is actively maintained and updated with new features and optimizations, making it a valuable tool for researchers, developers, and anyone interested in exploring and utilizing LLMs.

llmc is an off-the-shell tool designed for compressing LLM, leveraging state-of-the-art compression algorithms to enhance efficiency and reduce model size without compromising performance. It provides users with the ability to quantize LLMs, choose from various compression algorithms, export transformed models for further optimization, and directly infer compressed models with a shallow memory footprint. The tool supports a range of model types and quantization algorithms, with ongoing development to include pruning techniques. Users can design their configurations for quantization and evaluation, with documentation and examples planned for future updates. llmc is a valuable resource for researchers working on post-training quantization of large language models.

QServe is a serving system designed for efficient and accurate Large Language Models (LLM) on GPUs with W4A8KV4 quantization. It achieves higher throughput compared to leading industry solutions, allowing users to achieve A100-level throughput on cheaper L40S GPUs. The system introduces the QoQ quantization algorithm with 4-bit weight, 8-bit activation, and 4-bit KV cache, addressing runtime overhead challenges. QServe improves serving throughput for various LLM models by implementing compute-aware weight reordering, register-level parallelism, and fused attention memory-bound techniques.

This repository focuses on test-time computing, exploring various strategies such as test-time adaptation, modifying the input, editing the representation, calibrating the output, test-time reasoning, and search strategies. It covers topics like self-supervised test-time training, in-context learning, activation steering, nearest neighbor models, reward modeling, and multimodal reasoning. The repository provides resources including papers and code for researchers and practitioners interested in enhancing the reasoning capabilities of large language models.

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