PyWhy-LLM is an innovative library that integrates Large Language Models (LLMs) into the causal analysis process, empowering users with knowledge previously only available through domain experts. It seamlessly augments causal inference by suggesting potential confounders, relationships between variables, backdoor/frontdoor/iv sets, estimands, critiques of DAGs, latent confounders, and negative controls. The tool aims to enhance the causal analysis process by leveraging the capabilities of LLMs.

PyWhy-LLM is an innovative library that integrates Large Language Models (LLMs) into the causal analysis process, empowering users with knowledge previously only available through domain experts. It seamlessly augments existing causal inference processes by suggesting potential confounders, relationships between variables, backdoor sets, front door sets, IV sets, estimands, critiques of DAGs, latent confounders, and negative controls. By leveraging LLMs and formalizing human-LLM collaboration, PyWhy-LLM aims to enhance causal analysis accessibility and insight.

Zeta is a tool designed to build state-of-the-art AI models faster by providing modular, high-performance, and scalable building blocks. It addresses the common issues faced while working with neural nets, such as chaotic codebases, lack of modularity, and low performance modules. Zeta emphasizes usability, modularity, and performance, and is currently used in hundreds of models across various GitHub repositories. It enables users to prototype, train, optimize, and deploy the latest SOTA neural nets into production. The tool offers various modules like FlashAttention, SwiGLUStacked, RelativePositionBias, FeedForward, BitLinear, PalmE, Unet, VisionEmbeddings, niva, FusedDenseGELUDense, FusedDropoutLayerNorm, MambaBlock, Film, hyper_optimize, DPO, and ZetaCloud for different tasks in AI model development.

Beyond LLM offers an all-in-one toolkit for experimentation, evaluation, and deployment of Retrieval-Augmented Generation (RAG) systems. It simplifies the process with automated integration, customizable evaluation metrics, and support for various Large Language Models (LLMs) tailored to specific needs. The aim is to reduce LLM hallucination risks and enhance reliability.

The Arcade Learning Environment (ALE) is a simple framework that allows researchers and hobbyists to develop AI agents for Atari 2600 games. It is built on top of the Atari 2600 emulator Stella and separates the details of emulation from agent design. The ALE currently supports three different interfaces: C++, Python, and OpenAI Gym.

Weights & Biases (W&B) is a platform that helps users build better machine learning models faster by tracking and visualizing all components of the machine learning pipeline, from datasets to production models. It offers tools for tracking, debugging, evaluating, and monitoring machine learning applications. W&B provides integrations with popular frameworks like PyTorch, TensorFlow/Keras, Hugging Face Transformers, PyTorch Lightning, XGBoost, and Sci-Kit Learn. Users can easily log metrics, visualize performance, and compare experiments using W&B. The platform also supports hosting options in the cloud or on private infrastructure, making it versatile for various deployment needs.

The 'create-million-parameter-llm-from-scratch' repository provides a detailed guide on creating a Large Language Model (LLM) with 2.3 million parameters from scratch. The blog replicates the LLaMA approach, incorporating concepts like RMSNorm for pre-normalization, SwiGLU activation function, and Rotary Embeddings. The model is trained on a basic dataset to demonstrate the ease of creating a million-parameter LLM without the need for a high-end GPU.

ChatRex is a Multimodal Large Language Model (MLLM) designed to seamlessly integrate fine-grained object perception and robust language understanding. By adopting a decoupled architecture with a retrieval-based approach for object detection and leveraging high-resolution visual inputs, ChatRex addresses key challenges in perception tasks. It is powered by the Rexverse-2M dataset with diverse image-region-text annotations. ChatRex can be applied to various scenarios requiring fine-grained perception, such as object detection, grounded conversation, grounded image captioning, and region understanding.

GEM is an open-source General Experience Maker designed for training Large Language Models (LLMs) in dynamic environments. Similar to OpenAI Gym for traditional Reinforcement Learning, GEM provides a variety of environments with standardized interfaces for seamless integration with existing LLM training frameworks. It offers tool integration, flexible wrappers, async vectorized environment execution, multi-environment training, and more to simplify LLM agent training.

Ivy is an open-source machine learning framework that enables users to convert code between different ML frameworks and write framework-agnostic code. It allows users to transpile code from one framework to another, making it easy to use building blocks from different frameworks in a single project. Ivy also serves as a flexible framework that breaks free from framework limitations, allowing users to publish code that is interoperable with various frameworks and future frameworks. Users can define trainable modules and layers using Ivy's stateful API, making it easy to build and train models across different backends.

LLM-Microscope is a toolkit designed for quantifying and visualizing language model internals. It provides functions for calculating anisotropy, intrinsic dimension, and linearity score. The toolkit also includes a Logit Lens feature for analyzing model predictions and losses. Users can easily install the toolkit using pip and explore the functionalities through provided examples.

RAGoon is a high-level library designed for batched embeddings generation, fast web-based RAG (Retrieval-Augmented Generation) processing, and quantized indexes processing. It provides NLP utilities for multi-model embedding production, high-dimensional vector visualization, and enhancing language model performance through search-based querying, web scraping, and data augmentation techniques.

Ivy is an open-source machine learning framework that enables you to: * 🔄 **Convert code into any framework** : Use and build on top of any model, library, or device by converting any code from one framework to another using `ivy.transpile`. * ⚒️ **Write framework-agnostic code** : Write your code once in `ivy` and then choose the most appropriate ML framework as the backend to leverage all the benefits and tools. Join our growing community 🌍 to connect with people using Ivy. **Let's** unify.ai **together 🦾**

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.

Jlama is a modern Java inference engine designed for large language models. It supports various model types such as Gemma, Llama, Mistral, GPT-2, BERT, and more. The tool implements features like Flash Attention, Mixture of Experts, and supports different model quantization formats. Built with Java 21 and utilizing the new Vector API for faster inference, Jlama allows users to add LLM inference directly to their Java applications. The tool includes a CLI for running models, a simple UI for chatting with LLMs, and examples for different model types.

mcpdotnet is a .NET implementation of the Model Context Protocol (MCP), facilitating connections and interactions between .NET applications and MCP clients and servers. It aims to provide a clean, specification-compliant implementation with support for various MCP capabilities and transport types. The library includes features such as async/await pattern, logging support, and compatibility with .NET 8.0 and later. Users can create clients to use tools from configured servers and also create servers to register tools and interact with clients. The project roadmap includes expanding documentation, increasing test coverage, adding samples, performance optimization, SSE server support, and authentication.

PyWhy-LLM is an innovative library that integrates Large Language Models (LLMs) into the causal analysis process, empowering users with knowledge previously only available through domain experts. It seamlessly augments existing causal inference processes by suggesting potential confounders, relationships between variables, backdoor sets, front door sets, IV sets, estimands, critiques of DAGs, latent confounders, and negative controls. By leveraging LLMs and formalizing human-LLM collaboration, PyWhy-LLM aims to enhance causal analysis accessibility and insight.

OneKE is a flexible dockerized system for schema-guided knowledge extraction, capable of extracting information from the web and raw PDF books across multiple domains like science and news. It employs a collaborative multi-agent approach and includes a user-customizable knowledge base to enable tailored extraction. OneKE offers various IE tasks support, data sources support, LLMs support, extraction method support, and knowledge base configuration. Users can start with examples using YAML, Python, or Web UI, and perform tasks like Named Entity Recognition, Relation Extraction, Event Extraction, Triple Extraction, and Open Domain IE. The tool supports different source formats like Plain Text, HTML, PDF, Word, TXT, and JSON files. Users can choose from various extraction models like OpenAI, DeepSeek, LLaMA, Qwen, ChatGLM, MiniCPM, and OneKE for information extraction tasks. Extraction methods include Schema Agent, Extraction Agent, and Reflection Agent. The tool also provides support for schema repository and case repository management, along with solutions for network issues. Contributors to the project include Ningyu Zhang, Haofen Wang, Yujie Luo, Xiangyuan Ru, Kangwei Liu, Lin Yuan, Mengshu Sun, Lei Liang, Zhiqiang Zhang, Jun Zhou, Lanning Wei, Da Zheng, and Huajun Chen.

PyWhy-LLM is an innovative library that integrates Large Language Models (LLMs) into the causal analysis process, empowering users with knowledge previously only available through domain experts. It seamlessly augments causal inference by suggesting potential confounders, relationships between variables, backdoor/frontdoor/iv sets, estimands, critiques of DAGs, latent confounders, and negative controls. The tool aims to enhance the causal analysis process by leveraging the capabilities of LLMs.

PyWhy-LLM is an innovative library that integrates Large Language Models (LLMs) into the causal analysis process, empowering users with knowledge previously only available through domain experts. It seamlessly augments existing causal inference processes by suggesting potential confounders, relationships between variables, backdoor sets, front door sets, IV sets, estimands, critiques of DAGs, latent confounders, and negative controls. By leveraging LLMs and formalizing human-LLM collaboration, PyWhy-LLM aims to enhance causal analysis accessibility and insight.

PyWhy-LLM is an innovative library that integrates Large Language Models (LLMs) into the causal analysis process, empowering users with knowledge previously only available through domain experts. It seamlessly augments causal inference by suggesting potential confounders, relationships between variables, backdoor/frontdoor/iv sets, estimands, critiques of DAGs, latent confounders, and negative controls. The tool aims to enhance the causal analysis process by leveraging the capabilities of LLMs.

LoLLMs WebUI (Lord of Large Language Multimodal Systems: One tool to rule them all) is a user-friendly interface to access and utilize various LLM (Large Language Models) and other AI models for a wide range of tasks. With over 500 AI expert conditionings across diverse domains and more than 2500 fine tuned models over multiple domains, LoLLMs WebUI provides an immediate resource for any problem, from car repair to coding assistance, legal matters, medical diagnosis, entertainment, and more. The easy-to-use UI with light and dark mode options, integration with GitHub repository, support for different personalities, and features like thumb up/down rating, copy, edit, and remove messages, local database storage, search, export, and delete multiple discussions, make LoLLMs WebUI a powerful and versatile tool.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.

MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.

Mage is an open-source data pipeline tool for transforming and integrating data. It offers an easy developer experience, engineering best practices built-in, and data as a first-class citizen. Mage makes it easy to build, preview, and launch data pipelines, and provides observability and scaling capabilities. It supports data integrations, streaming pipelines, and dbt integration.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.