IPEX-LLM is a low-bit LLM library on Intel XPU (Xeon/Core/Flex/Arc/PVC) that provides tutorials to help users understand and use the library to build LLM applications. The tutorials cover topics such as introduction to IPEX-LLM, environment setup, basic application development, Chinese language support, intermediate and advanced application development, GPU acceleration, and finetuning. Users can learn how to build chat applications, chatbots, speech recognition, and more using IPEX-LLM.

Data-Juicer is a one-stop data processing system to make data higher-quality, juicier, and more digestible for LLMs. It is a systematic & reusable library of 80+ core OPs, 20+ reusable config recipes, and 20+ feature-rich dedicated toolkits, designed to function independently of specific LLM datasets and processing pipelines. Data-Juicer allows detailed data analyses with an automated report generation feature for a deeper understanding of your dataset. Coupled with multi-dimension automatic evaluation capabilities, it supports a timely feedback loop at multiple stages in the LLM development process. Data-Juicer offers tens of pre-built data processing recipes for pre-training, fine-tuning, en, zh, and more scenarios. It provides a speedy data processing pipeline requiring less memory and CPU usage, optimized for maximum productivity. Data-Juicer is flexible & extensible, accommodating most types of data formats and allowing flexible combinations of OPs. It is designed for simplicity, with comprehensive documentation, easy start guides and demo configs, and intuitive configuration with simple adding/removing OPs from existing configs.

NeMo Curator is a GPU-accelerated open-source framework designed for efficient large language model data curation. It provides scalable dataset preparation for tasks like foundation model pretraining, domain-adaptive pretraining, supervised fine-tuning, and parameter-efficient fine-tuning. The library leverages GPUs with Dask and RAPIDS to accelerate data curation, offering customizable and modular interfaces for pipeline expansion and model convergence. Key features include data download, text extraction, quality filtering, deduplication, downstream-task decontamination, distributed data classification, and PII redaction. NeMo Curator is suitable for curating high-quality datasets for large language model training.

BeeAI Framework is a versatile tool for building production-ready multi-agent systems. It offers flexibility in orchestrating agents, seamless integration with various models and tools, and production-grade controls for scaling. The framework supports Python and TypeScript libraries, enabling users to implement simple to complex multi-agent patterns, connect with AI services, and optimize token usage and resource management.

Lobe Chat is an open-source, modern-design ChatGPT/LLMs UI/Framework. Supports speech-synthesis, multi-modal, and extensible ([function call][docs-functionc-call]) plugin system. One-click **FREE** deployment of your private OpenAI ChatGPT/Claude/Gemini/Groq/Ollama chat application.

ClearML is a suite of tools designed to streamline the machine learning workflow. It includes an experiment manager, MLOps/LLMOps, data management, and model serving capabilities. ClearML is open-source and offers a free tier hosting option. It supports various ML/DL frameworks and integrates with Jupyter Notebook and PyCharm. ClearML provides extensive logging capabilities, including source control info, execution environment, hyper-parameters, and experiment outputs. It also offers automation features, such as remote job execution and pipeline creation. ClearML is designed to be easy to integrate, requiring only two lines of code to add to existing scripts. It aims to improve collaboration, visibility, and data transparency within ML teams.

Atom is an accurate low-bit weight-activation quantization algorithm that combines mixed-precision, fine-grained group quantization, dynamic activation quantization, KV-cache quantization, and efficient CUDA kernels co-design. It introduces a low-bit quantization method, Atom, to maximize Large Language Models (LLMs) serving throughput with negligible accuracy loss. The codebase includes evaluation of perplexity and zero-shot accuracy, kernel benchmarking, and end-to-end evaluation. Atom significantly boosts serving throughput by using low-bit operators and reduces memory consumption via low-bit quantization.

Swiftide is a fast, streaming indexing and query library tailored for Retrieval Augmented Generation (RAG) in AI applications. It is built in Rust, utilizing parallel, asynchronous streams for blazingly fast performance. With Swiftide, users can easily build AI applications from idea to production in just a few lines of code. The tool addresses frustrations around performance, stability, and ease of use encountered while working with Python-based tooling. It offers features like fast streaming indexing pipeline, experimental query pipeline, integrations with various platforms, loaders, transformers, chunkers, embedders, and more. Swiftide aims to provide a platform for data indexing and querying to advance the development of automated Large Language Model (LLM) applications.

Habitat-Lab is a modular high-level library for end-to-end development in embodied AI. It is designed to train agents to perform a wide variety of embodied AI tasks in indoor environments, as well as develop agents that can interact with humans in performing these tasks.

FlashInfer is a library for Language Languages Models that provides high-performance implementation of LLM GPU kernels such as FlashAttention, PageAttention and LoRA. FlashInfer focus on LLM serving and inference, and delivers state-the-art performance across diverse scenarios.

LABO is a time series forecasting and analysis framework that integrates pre-trained and fine-tuned LLMs with multi-domain agent-based systems. It allows users to create and tune agents easily for various scenarios, such as stock market trend prediction and web public opinion analysis. LABO requires a specific runtime environment setup, including system requirements, Python environment, dependency installations, and configurations. Users can fine-tune their own models using LABO's Low-Rank Adaptation (LoRA) for computational efficiency and continuous model updates. Additionally, LABO provides a Python library for building model training pipelines and customizing agents for specific tasks.

LightLLM is a Python-based LLM (Large Language Model) inference and serving framework known for its lightweight design, scalability, and high-speed performance. It offers features like tri-process asynchronous collaboration, Nopad for efficient attention operations, dynamic batch scheduling, FlashAttention integration, tensor parallelism, Token Attention for zero memory waste, and Int8KV Cache. The tool supports various models like BLOOM, LLaMA, StarCoder, Qwen-7b, ChatGLM2-6b, Baichuan-7b, Baichuan2-7b, Baichuan2-13b, InternLM-7b, Yi-34b, Qwen-VL, Llava-7b, Mixtral, Stablelm, and MiniCPM. Users can deploy and query models using the provided server launch commands and interact with multimodal models like QWen-VL and Llava using specific queries and images.

SLAM-LLM is a deep learning toolkit designed for researchers and developers to train custom multimodal large language models (MLLM) focusing on speech, language, audio, and music processing. It provides detailed recipes for training and high-performance checkpoints for inference. The toolkit supports tasks such as automatic speech recognition (ASR), text-to-speech (TTS), visual speech recognition (VSR), automated audio captioning (AAC), spatial audio understanding, and music caption (MC). SLAM-LLM features easy extension to new models and tasks, mixed precision training for faster training with less GPU memory, multi-GPU training with data and model parallelism, and flexible configuration based on Hydra and dataclass.

AIQC is an open source Python package that provides a declarative API for end-to-end MLOps in order to make deep learning more accessible to researchers. It utilizes a SQLite object-relational model for machine learning objects and stacks standardized workflows for various analyses, data types, and libraries. The benefits include a 90% reduction in data wrangling, reproducibility, and no need to install and maintain application and database servers for experiment tracking. AIQC is pip-installable and provides a Dash-Plotly UI for real-time experiment tracking.

ReproModel is an open-source toolbox designed to boost AI research efficiency by enabling researchers to reproduce, compare, train, and test AI models faster. It provides standardized models, dataloaders, and processing procedures, allowing researchers to focus on new datasets and model development. With a no-code solution, users can access benchmark and SOTA models and datasets, utilize training visualizations, extract code for publication, and leverage an LLM-powered automated methodology description writer. The toolbox helps researchers modularize development, compare pipeline performance reproducibly, and reduce time for model development, computation, and writing. Future versions aim to facilitate building upon state-of-the-art research by loading previously published study IDs with verified code, experiments, and results stored in the system.

PyTorch Forecasting is a PyTorch-based package designed for state-of-the-art timeseries forecasting using deep learning architectures. It offers a high-level API and leverages PyTorch Lightning for efficient training on GPU or CPU with automatic logging. The package aims to simplify timeseries forecasting tasks by providing a flexible API for professionals and user-friendly defaults for beginners. It includes features such as a timeseries dataset class for handling data transformations, missing values, and subsampling, various neural network architectures optimized for real-world deployment, multi-horizon timeseries metrics, and hyperparameter tuning with optuna. Built on pytorch-lightning, it supports training on CPUs, single GPUs, and multiple GPUs out-of-the-box.

IPEX-LLM is a low-bit LLM library on Intel XPU (Xeon/Core/Flex/Arc/PVC) that provides tutorials to help users understand and use the library to build LLM applications. The tutorials cover topics such as introduction to IPEX-LLM, environment setup, basic application development, Chinese language support, intermediate and advanced application development, GPU acceleration, and finetuning. Users can learn how to build chat applications, chatbots, speech recognition, and more using IPEX-LLM.

OpenAI is a Swift community-maintained implementation over OpenAI public API. It is a non-profit artificial intelligence research organization founded in San Francisco, California in 2015. OpenAI's mission is to ensure safe and responsible use of AI for civic good, economic growth, and other public benefits. The repository provides functionalities for text completions, chats, image generation, audio processing, edits, embeddings, models, moderations, utilities, and Combine extensions.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.

ChatDev IDE is a tool for building your AI agent, Whether it's NPCs in games or powerful agent tools, you can design what you want for this platform. It accelerates prompt engineering through **JavaScript Support** that allows implementing complex prompting techniques.

Chat Worm is a ChatGPT client that provides access to the API for generating text using OpenAI's GPT models. It works as a single-page application directly communicating with the API, allowing users to interact with the latest GPT-4 model if they have access. The project includes web, Android, and Windows apps for easy access. Users can set up local development, contribute improvements via pull requests, report bugs or request features on GitHub, deploy to production servers, and release on different app stores. The project is licensed under the MIT License.

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

JamAI Base is an open-source platform integrating SQLite and LanceDB databases with managed memory and RAG capabilities. It offers built-in LLM, vector embeddings, and reranker orchestration accessible through a spreadsheet-like UI and REST API. Users can transform static tables into dynamic entities, facilitate real-time interactions, manage structured data, and simplify chatbot development. The tool focuses on ease of use, scalability, flexibility, declarative paradigm, and innovative RAG techniques, making complex data operations accessible to users with varying technical expertise.

draive is an open-source Python library designed to simplify and accelerate the development of LLM-based applications. It offers abstract building blocks for connecting functionalities with large language models, flexible integration with various AI solutions, and a user-friendly framework for building scalable data processing pipelines. The library follows a function-oriented design, allowing users to represent complex programs as simple functions. It also provides tools for measuring and debugging functionalities, ensuring type safety and efficient asynchronous operations for modern Python apps.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.