Volga is a general purpose real-time data processing engine in Python for modern AI/ML systems. It aims to be a Python-native alternative to Flink/Spark Streaming with extended functionality for real-time AI/ML workloads. It provides a hybrid push+pull architecture, Entity API for defining data entities and feature pipelines, DataStream API for general data processing, and customizable data connectors. Volga can run on a laptop or a distributed cluster, making it suitable for building custom real-time AI/ML feature platforms or general data pipelines without relying on third-party platforms.

Pixeltable is a Python library designed for ML Engineers and Data Scientists to focus on exploration, modeling, and app development without the need to handle data plumbing. It provides a declarative interface for working with text, images, embeddings, and video, enabling users to store, transform, index, and iterate on data within a single table interface. Pixeltable is persistent, acting as a database unlike in-memory Python libraries such as Pandas. It offers features like data storage and versioning, combined data and model lineage, indexing, orchestration of multimodal workloads, incremental updates, and automatic production-ready code generation. The tool emphasizes transparency, reproducibility, cost-saving through incremental data changes, and seamless integration with existing Python code and libraries.

CodeTF is a Python transformer-based library for code large language models (Code LLMs) and code intelligence. It provides an interface for training and inferencing on tasks like code summarization, translation, and generation. The library offers utilities for code manipulation across various languages, including easy extraction of code attributes. Using tree-sitter as its core AST parser, CodeTF enables parsing of function names, comments, and variable names. It supports fast model serving, fine-tuning of LLMs, various code intelligence tasks, preprocessed datasets, model evaluation, pretrained and fine-tuned models, and utilities to manipulate source code. CodeTF aims to facilitate the integration of state-of-the-art Code LLMs into real-world applications, ensuring a user-friendly environment for code intelligence tasks.

ReqLLM is a Req-based library for LLM interactions, offering a unified interface to AI providers through a plugin-based architecture. It brings composability and middleware advantages to LLM interactions, with features like auto-synced providers/models, typed data structures, ergonomic helpers, streaming capabilities, usage & cost extraction, and a plugin-based provider system. Users can easily generate text, structured data, embeddings, and track usage costs. The tool supports various AI providers like Anthropic, OpenAI, Groq, Google, and xAI, and allows for easy addition of new providers. ReqLLM also provides API key management, detailed documentation, and a roadmap for future enhancements.

TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. It provides pytorch and **python-first** , low and high level abstractions for RL that are intended to be **efficient** , **modular** , **documented** and properly **tested**. The code is aimed at supporting research in RL. Most of it is written in python in a highly modular way, such that researchers can easily swap components, transform them or write new ones with little effort.

LightRAG is a repository hosting the code for LightRAG, a system that supports seamless integration of custom knowledge graphs, Oracle Database 23ai, Neo4J for storage, and multiple file types. It includes features like entity deletion, batch insert, incremental insert, and graph visualization. LightRAG provides an API server implementation for RESTful API access to RAG operations, allowing users to interact with it through HTTP requests. The repository also includes evaluation scripts, code for reproducing results, and a comprehensive code structure.

sdg_hub is a modular Python framework designed for building synthetic data generation pipelines using composable blocks and flows. Users can mix and match LLM-powered and traditional processing blocks to create sophisticated data generation workflows. The toolkit offers features such as modular composability, async performance, built-in validation, auto-discovery, rich monitoring, dataset schema discovery, and easy extensibility. sdg_hub provides detailed documentation and supports high-throughput processing with error handling. It simplifies the process of transforming datasets by allowing users to chain blocks together in YAML-configured flows, enabling the creation of complex data generation pipelines.

MCPHub.nvim is a powerful Neovim plugin that integrates MCP (Model Context Protocol) servers into your workflow. It offers a centralized config file for managing servers and tools, with an intuitive UI for testing resources. Ideal for LLM integration, it provides programmatic API access and interactive testing through the `:MCPHub` command.

Open-Source Evaluation for LLM Applications. `continuous-eval` is an open-source package created for granular and holistic evaluation of GenAI application pipelines. It offers modularized evaluation, a comprehensive metric library covering various LLM use cases, the ability to leverage user feedback in evaluation, and synthetic dataset generation for testing pipelines. Users can define their own metrics by extending the Metric class. The tool allows running evaluation on a pipeline defined with modules and corresponding metrics. Additionally, it provides synthetic data generation capabilities to create user interaction data for evaluation or training purposes.

ShannonBase is a HTAP database provided by Shannon Data AI, designed for big data and AI. It extends MySQL with native embedding support, machine learning capabilities, a JavaScript engine, and a columnar storage engine. ShannonBase supports multimodal data types and natively integrates LightGBM for training and prediction. It leverages embedding algorithms and vector data type for ML/RAG tasks, providing Zero Data Movement, Native Performance Optimization, and Seamless SQL Integration. The tool includes a lightweight JavaScript engine for writing stored procedures in SQL or JavaScript.

Next-Token Prediction is a language model tool that allows users to create high-quality predictions for the next word, phrase, or pixel based on a body of text. It can be used as an alternative to well-known decoder-only models like GPT and Mistral. The tool provides options for simple usage with built-in data bootstrap or advanced customization by providing training data or creating it from .txt files. It aims to simplify methodologies, provide autocomplete, autocorrect, spell checking, search/lookup functionalities, and create pixel and audio transformers for various prediction formats.

Monty is a minimal, secure Python interpreter written in Rust for use by AI. It allows safe execution of Python code written by an LLM embedded in your agent, with fast startup times and performance similar to CPython. Monty supports running a subset of Python code, blocking access to the host environment, calling host functions, typechecking, snapshotting interpreter state, controlling resource usage, collecting stdout and stderr, and running async or sync code. It is designed for running code written by agents, providing a sandboxed environment without the complexity of a full container-based solution.

The GenAI repository contains pipelines that implement image and text generation tasks. The implementation uses OpenVINO capabilities to optimize the pipelines. Each sample covers a family of models and suggests certain modifications to adapt the code to specific needs. It includes the following pipelines: 1. Benchmarking script for large language models 2. Text generation C++ samples that support most popular models like LLaMA 2 3. Stable Diffuison (with LoRA) C++ image generation pipeline 4. Latent Consistency Model (with LoRA) C++ image generation pipeline

Funcchain is a Python library that allows you to easily write cognitive systems by leveraging Pydantic models as output schemas and LangChain in the backend. It provides a seamless integration of LLMs into your apps, utilizing OpenAI Functions or LlamaCpp grammars (json-schema-mode) for efficient structured output. Funcchain compiles the Funcchain syntax into LangChain runnables, enabling you to invoke, stream, or batch process your pipelines effortlessly.

IntelliNode is a javascript module that integrates cutting-edge AI models like ChatGPT, LLaMA, WaveNet, Gemini, and Stable diffusion into projects. It offers functions for generating text, speech, and images, as well as semantic search, multi-model evaluation, and chatbot capabilities. The module provides a wrapper layer for low-level model access, a controller layer for unified input handling, and a function layer for abstract functionality tailored to various use cases.

Instructor is a tool that provides structured outputs from Large Language Models (LLMs) in a reliable manner. It simplifies the process of extracting structured data by utilizing Pydantic for validation, type safety, and IDE support. With Instructor, users can define models and easily obtain structured data without the need for complex JSON parsing, error handling, or retries. The tool supports automatic retries, streaming support, and extraction of nested objects, making it production-ready for various AI applications. Trusted by a large community of developers and companies, Instructor is used by teams at OpenAI, Google, Microsoft, AWS, and YC startups.

Volga is a general purpose real-time data processing engine in Python for modern AI/ML systems. It aims to be a Python-native alternative to Flink/Spark Streaming with extended functionality for real-time AI/ML workloads. It provides a hybrid push+pull architecture, Entity API for defining data entities and feature pipelines, DataStream API for general data processing, and customizable data connectors. Volga can run on a laptop or a distributed cluster, making it suitable for building custom real-time AI/ML feature platforms or general data pipelines without relying on third-party platforms.

Awesome Mamba is a curated collection of groundbreaking research papers and articles on Mamba Architecture, a pioneering framework in deep learning known for its selective state spaces and efficiency in processing complex data structures. The repository offers a comprehensive exploration of Mamba architecture through categorized research papers covering various domains like visual recognition, speech processing, remote sensing, video processing, activity recognition, image enhancement, medical imaging, reinforcement learning, natural language processing, 3D recognition, multi-modal understanding, time series analysis, graph neural networks, point cloud analysis, and tabular data handling.

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.

LLM App Stack, also known as Emerging Architectures for LLM Applications, is a comprehensive list of available tools, projects, and vendors at each layer of the LLM app stack. It covers various categories such as Data Pipelines, Embedding Models, Vector Databases, Playgrounds, Orchestrators, APIs/Plugins, LLM Caches, Logging/Monitoring/Eval, Validators, LLM APIs (proprietary and open source), App Hosting Platforms, Cloud Providers, and Opinionated Clouds. The repository aims to provide a detailed overview of tools and projects for building, deploying, and maintaining enterprise data solutions, AI models, and applications.

Awesome deep logic is a curated list of papers and resources focusing on integrating symbolic logic into deep neural networks. It includes surveys, tutorials, and research papers that explore the intersection of logic and deep learning. The repository aims to provide valuable insights and knowledge on how logic can be used to enhance reasoning, knowledge regularization, weak supervision, and explainability in neural networks.

Welcome to the ultimate hub for on-device Large Language Models (LLMs)! This repository is your go-to resource for all things related to LLMs designed for on-device deployment. Whether you're a seasoned researcher, an innovative developer, or an enthusiastic learner, this comprehensive collection of cutting-edge knowledge is your gateway to understanding, leveraging, and contributing to the exciting world of on-device LLMs.

DuoAttention is a framework designed to optimize long-context large language models (LLMs) by reducing memory and latency during inference without compromising their long-context abilities. It introduces a concept of Retrieval Heads and Streaming Heads to efficiently manage attention across tokens. By applying a full Key and Value (KV) cache to retrieval heads and a lightweight, constant-length KV cache to streaming heads, DuoAttention achieves significant reductions in memory usage and decoding time for LLMs. The framework uses an optimization-based algorithm with synthetic data to accurately identify retrieval heads, enabling efficient inference with minimal accuracy loss compared to full attention. DuoAttention also supports quantization techniques for further memory optimization, allowing for decoding of up to 3.3 million tokens on a single GPU.

LLM notes repository contains detailed analysis on transformer models, language model compression, inference and deployment, high-performance computing, and system optimization methods. It includes discussions on various algorithms, frameworks, and performance analysis related to large language models and high-performance computing. The repository serves as a comprehensive resource for understanding and optimizing language models and computing systems.

A curated list of high-quality papers on resource-efficient Large Language Models (LLMs) with a focus on various aspects such as architecture design, pre-training, fine-tuning, inference, system design, and evaluation metrics. The repository covers topics like efficient transformer architectures, non-transformer architectures, memory efficiency, data efficiency, model compression, dynamic acceleration, deployment optimization, support infrastructure, and other related systems. It also provides detailed information on computation metrics, memory metrics, energy metrics, financial cost metrics, network communication metrics, and other metrics relevant to resource-efficient LLMs. The repository includes benchmarks for evaluating the efficiency of NLP models and references for further reading.