MemOS is an operating system for Large Language Models (LLMs) that enhances them with long-term memory capabilities. It allows LLMs to store, retrieve, and manage information, enabling more context-aware, consistent, and personalized interactions. MemOS provides Memory-Augmented Generation (MAG) with a unified API for memory operations, a Modular Memory Architecture (MemCube) for easy integration and management of different memory types, and multiple memory types including Textual Memory, Activation Memory, and Parametric Memory. It is extensible, allowing users to customize memory modules, data sources, and LLM integrations. MemOS demonstrates significant improvements over baseline memory solutions in multiple reasoning tasks, with a notable improvement in temporal reasoning accuracy compared to the OpenAI baseline.

Memori is a memory fabric designed for enterprise AI that seamlessly integrates into existing software and infrastructure. It is agnostic to LLM, datastore, and framework, providing support for major foundational models and databases. With features like vectorized memories, in-memory semantic search, and a knowledge graph, Memori simplifies the process of attributing LLM interactions and managing sessions. It offers Advanced Augmentation for enhancing memories at different levels and supports various platforms, frameworks, database integrations, and datastores. Memori is designed to reduce development overhead and provide efficient memory management for AI applications.

OpenViking is an open-source Context Database designed specifically for AI Agents. It aims to solve challenges in agent development by unifying memories, resources, and skills in a filesystem management paradigm. The tool offers tiered context loading, directory recursive retrieval, visualized retrieval trajectory, and automatic session management. Developers can interact with OpenViking like managing local files, enabling precise context manipulation and intuitive traceable operations. The tool supports various model services like OpenAI and Volcengine, enhancing semantic retrieval and context understanding for AI Agents.

Deeppowers is a powerful Python library for deep learning applications. It provides a wide range of tools and utilities to simplify the process of building and training deep neural networks. With Deeppowers, users can easily create complex neural network architectures, perform efficient training and optimization, and deploy models for various tasks. The library is designed to be user-friendly and flexible, making it suitable for both beginners and experienced deep learning practitioners.

SGLang is a structured generation language designed for large language models (LLMs). It makes your interaction with LLMs faster and more controllable by co-designing the frontend language and the runtime system. The core features of SGLang include: - **A Flexible Front-End Language**: This allows for easy programming of LLM applications with multiple chained generation calls, advanced prompting techniques, control flow, multiple modalities, parallelism, and external interaction. - **A High-Performance Runtime with RadixAttention**: This feature significantly accelerates the execution of complex LLM programs by automatic KV cache reuse across multiple calls. It also supports other common techniques like continuous batching and tensor parallelism.

FastFlowLM is a Python library for efficient and scalable language model inference. It provides a high-performance implementation of language model scoring using n-gram language models. The library is designed to handle large-scale text data and can be easily integrated into natural language processing pipelines for tasks such as text generation, speech recognition, and machine translation. FastFlowLM is optimized for speed and memory efficiency, making it suitable for both research and production environments.

GEN-AI is a versatile Python library for implementing various artificial intelligence algorithms and models. It provides a wide range of tools and functionalities to support machine learning, deep learning, natural language processing, computer vision, and reinforcement learning tasks. With GEN-AI, users can easily build, train, and deploy AI models for diverse applications such as image recognition, text classification, sentiment analysis, object detection, and game playing. The library is designed to be user-friendly, efficient, and scalable, making it suitable for both beginners and experienced AI practitioners.

Fast-DLLM is a diffusion-based Large Language Model (LLM) inference acceleration framework that supports efficient inference for models like Dream and LLaDA. It offers fast inference support, multiple optimization strategies, code generation, evaluation capabilities, and an interactive chat interface. Key features include Key-Value Cache for Block-Wise Decoding, Confidence-Aware Parallel Decoding, and overall performance improvements. The project structure includes directories for Dream and LLaDA model-related code, with installation and usage instructions provided for using the LLaDA and Dream models.

FastGPT is a knowledge base Q&A system based on the LLM large language model, providing out-of-the-box data processing, model calling and other capabilities. At the same time, you can use Flow to visually arrange workflows to achieve complex Q&A scenarios!

pdr_ai_v2 is a Python library for implementing machine learning algorithms and models. It provides a wide range of tools and functionalities for data preprocessing, model training, evaluation, and deployment. The library is designed to be user-friendly and efficient, making it suitable for both beginners and experienced data scientists. With pdr_ai_v2, users can easily build and deploy machine learning models for various applications, such as classification, regression, clustering, and more.

The 'llm' package for Emacs provides an interface for interacting with Large Language Models (LLMs). It abstracts functionality to a higher level, concealing API variations and ensuring compatibility with various LLMs. Users can set up providers like OpenAI, Gemini, Vertex, Claude, Ollama, GPT4All, and a fake client for testing. The package allows for chat interactions, embeddings, token counting, and function calling. It also offers advanced prompt creation and logging capabilities. Users can handle conversations, create prompts with placeholders, and contribute by creating providers.

BentoVLLM is an example project demonstrating how to serve and deploy open-source Large Language Models using vLLM, a high-throughput and memory-efficient inference engine. It provides a basis for advanced code customization, such as custom models, inference logic, or vLLM options. The project allows for simple LLM hosting with OpenAI compatible endpoints without the need to write any code. Users can interact with the server using Swagger UI or other methods, and the service can be deployed to BentoCloud for better management and scalability. Additionally, the repository includes integration examples for different LLM models and tools.

The Semantic Router is an intelligent routing tool that utilizes a Mixture-of-Models (MoM) approach to direct OpenAI API requests to the most suitable models based on semantic understanding. It enhances inference accuracy by selecting models tailored to different types of tasks. The tool also automatically selects relevant tools based on the prompt to improve tool selection accuracy. Additionally, it includes features for enterprise security such as PII detection and prompt guard to protect user privacy and prevent misbehavior. The tool implements similarity caching to reduce latency. The comprehensive documentation covers setup instructions, architecture guides, and API references.

LangGraph.js is a library for building stateful, multi-actor applications with LLMs, offering benefits such as cycles, controllability, and persistence. It allows defining flows involving cycles, providing fine-grained control over application flow and state. Inspired by Pregel and Apache Beam, it includes features like loops, persistence, human-in-the-loop workflows, and streaming support. LangGraph integrates seamlessly with LangChain.js and LangSmith but can be used independently.

Neurons.me is an open-source tool designed for creating and managing neural network models. It provides a user-friendly interface for building, training, and deploying deep learning models. With Neurons.me, users can easily experiment with different architectures, hyperparameters, and datasets to optimize their neural networks for various tasks. The tool simplifies the process of developing AI applications by abstracting away the complexities of model implementation and training.

Cellm is an Excel extension that allows users to leverage Large Language Models (LLMs) like ChatGPT within cell formulas. It enables users to extract AI responses to text ranges, making it useful for automating repetitive tasks that involve data processing and analysis. Cellm supports various models from Anthropic, Mistral, OpenAI, and Google, as well as locally hosted models via Llamafiles, Ollama, or vLLM. The tool is designed to simplify the integration of AI capabilities into Excel for tasks such as text classification, data cleaning, content summarization, entity extraction, and more.

MemOS is an operating system for Large Language Models (LLMs) that enhances them with long-term memory capabilities. It allows LLMs to store, retrieve, and manage information, enabling more context-aware, consistent, and personalized interactions. MemOS provides Memory-Augmented Generation (MAG) with a unified API for memory operations, a Modular Memory Architecture (MemCube) for easy integration and management of different memory types, and multiple memory types including Textual Memory, Activation Memory, and Parametric Memory. It is extensible, allowing users to customize memory modules, data sources, and LLM integrations. MemOS demonstrates significant improvements over baseline memory solutions in multiple reasoning tasks, with a notable improvement in temporal reasoning accuracy compared to the OpenAI baseline.

Step-DPO is a method for enhancing long-chain reasoning ability of LLMs with a data construction pipeline creating a high-quality dataset. It significantly improves performance on math and GSM8K tasks with minimal data and training steps. The tool fine-tunes pre-trained models like Qwen2-7B-Instruct with Step-DPO, achieving superior results compared to other models. It provides scripts for training, evaluation, and deployment, along with examples and acknowledgements.

optillm is an OpenAI API compatible optimizing inference proxy implementing state-of-the-art techniques to enhance accuracy and performance of LLMs, focusing on reasoning over coding, logical, and mathematical queries. By leveraging additional compute at inference time, it surpasses frontier models across diverse tasks.

MoBA (Mixture of Block Attention) is an innovative approach for long-context language models, enabling efficient processing of long sequences by dividing the full context into blocks and introducing a parameter-less gating mechanism. It allows seamless transitions between full and sparse attention modes, enhancing efficiency without compromising performance. MoBA has been deployed to support long-context requests and demonstrates significant advancements in efficient attention computation for large language models.

This repository is dedicated to enhancing Language Model (LLM) reasoning with reinforcement learning (RL). It includes a collection of the latest papers, slides, and materials related to RL-based LLM reasoning, aiming to facilitate quick learning and understanding in this field. Starring this repository allows users to stay updated and engaged with the forefront of RL-based LLM reasoning.

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.

The Awesome-Neuro-Symbolic-Learning-with-LLM repository is a curated collection of papers and resources focusing on improving reasoning and planning capabilities of Large Language Models (LLMs) and Multi-Modal Large Language Models (MLLMs) through neuro-symbolic learning. It covers a wide range of topics such as neuro-symbolic visual reasoning, program synthesis, logical reasoning, mathematical reasoning, code generation, visual reasoning, geometric reasoning, classical planning, game AI planning, robotic planning, AI agent planning, and more. The repository provides a comprehensive overview of tutorials, workshops, talks, surveys, papers, datasets, and benchmarks related to neuro-symbolic learning with LLMs and MLLMs.

The Awesome-LLM-Causal-Reasoning repository provides a comprehensive review of research focused on enhancing Large Language Models (LLMs) for causal reasoning (CR). It categorizes existing methods based on the role of LLMs as reasoning engines or helpers, evaluates LLMs' performance on various causal reasoning tasks, and discusses methodologies and insights for future research. The repository includes papers, datasets, and benchmarks related to causal reasoning in LLMs.

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.