ChemBench is a project aimed at expanding chemistry benchmark tasks in a BIG-bench compatible way, providing a pipeline to benchmark frontier and open models. It enables benchmarking across a wide range of API-based models and employs an LLM-based extractor as a fallback mechanism. Users can evaluate models on specific chemistry topics and run comprehensive evaluations across all topics in the benchmark suite. The tool facilitates seamless benchmarking for any model supported by LiteLLM and allows running non-API hosted models.

LeRobot is a state-of-the-art AI library for real-world robotics in PyTorch. It aims to provide models, datasets, and tools to lower the barrier to entry to robotics, focusing on imitation learning and reinforcement learning. LeRobot offers pretrained models, datasets with human-collected demonstrations, and simulation environments. It plans to support real-world robotics on affordable and capable robots. The library hosts pretrained models and datasets on the Hugging Face community page.

LightLLM is a lightweight library for linear and logistic regression models. It provides a simple and efficient way to train and deploy machine learning models for regression tasks. The library is designed to be easy to use and integrate into existing projects, making it suitable for both beginners and experienced data scientists. With LightLLM, users can quickly build and evaluate regression models using a variety of algorithms and hyperparameters. The library also supports feature engineering and model interpretation, allowing users to gain insights from their data and make informed decisions based on the model predictions.

AdalFlow is a library designed to help developers build and optimize Large Language Model (LLM) task pipelines. It follows a design pattern similar to PyTorch, offering a light, modular, and robust codebase. Named in honor of Ada Lovelace, AdalFlow aims to inspire more women to enter the AI field. The library is tailored for various GenAI applications like chatbots, translation, summarization, code generation, and autonomous agents, as well as classical NLP tasks such as text classification and named entity recognition. AdalFlow emphasizes modularity, robustness, and readability to support users in customizing and iterating code for their specific use cases.

Baal is an active learning library that supports both industrial applications and research use cases. It provides a framework for Bayesian active learning methods such as Monte-Carlo Dropout, MCDropConnect, Deep ensembles, and Semi-supervised learning. Baal helps in labeling the most uncertain items in the dataset pool to improve model performance and reduce annotation effort. The library is actively maintained by a dedicated team and has been used in various research papers for production and experimentation.

MathCoder is a repository focused on enhancing mathematical reasoning by fine-tuning open-source language models to use code for modeling and deriving math equations. It introduces MathCodeInstruct dataset with solutions interleaving natural language, code, and execution results. The repository provides MathCoder models capable of generating code-based solutions for challenging math problems, achieving state-of-the-art scores on MATH and GSM8K datasets. It offers tools for model deployment, inference, and evaluation, along with a citation for referencing the work.

FedLLM-Bench is a realistic benchmark for the Federated Learning of Large Language Models community. It includes datasets for federated instruction tuning and preference alignment tasks, exhibiting diversities in language, quality, quantity, instruction, sequence length, embedding, and preference. The repository provides training scripts and code for open-ended evaluation, aiming to facilitate research and development in federated learning of large language models.

Genesis is a physics platform designed for general purpose Robotics/Embodied AI/Physical AI applications. It includes a universal physics engine, a lightweight, ultra-fast, pythonic, and user-friendly robotics simulation platform, a powerful and fast photo-realistic rendering system, and a generative data engine that transforms user-prompted natural language description into various modalities of data. It aims to lower the barrier to using physics simulations, unify state-of-the-art physics solvers, and minimize human effort in collecting and generating data for robotics and other domains.

DataDreamer is a powerful open-source Python library designed for prompting, synthetic data generation, and training workflows. It is simple, efficient, and research-grade, allowing users to create prompting workflows, generate synthetic datasets, and train models with ease. The library is built for researchers, by researchers, focusing on correctness, best practices, and reproducibility. It offers features like aggressive caching, resumability, support for bleeding-edge techniques, and easy sharing of datasets and models. DataDreamer enables users to run multi-step prompting workflows, generate synthetic datasets for various tasks, and train models by aligning, fine-tuning, instruction-tuning, and distilling them using existing or synthetic data.

Datasets is a repository that provides a collection of various datasets for machine learning and data analysis projects. It includes datasets in different formats such as CSV, JSON, and Excel, covering a wide range of topics including finance, healthcare, marketing, and more. The repository aims to help data scientists, researchers, and students access high-quality datasets for training models, conducting experiments, and exploring data analysis techniques.

CRAB is a framework for building LLM agent benchmark environments in a Python-centric way. It is cross-platform and multi-environment, allowing the creation of agent environments supporting various deployment options. The framework offers easy-to-use configuration with the ability to add new actions and define environments seamlessly. CRAB also provides a novel benchmarking suite with tasks and evaluators defined in Python, along with a unique graph evaluator method for detailed metrics.

Only Train Once (OTO) is an automatic, architecture-agnostic DNN training and compression framework that allows users to train a general DNN from scratch or a pretrained checkpoint to achieve high performance and slimmer architecture simultaneously in a one-shot manner without fine-tuning. The framework includes features for automatic structured pruning and erasing operators, as well as hybrid structured sparse optimizers for efficient model compression. OTO provides tools for pruning zero-invariant group partitioning, constructing pruned models, and visualizing pruning and erasing dependency graphs. It supports the HESSO optimizer and offers a sanity check for compliance testing on various DNNs. The repository also includes publications, installation instructions, quick start guides, and a roadmap for future enhancements and collaborations.

TruLens provides a set of tools for developing and monitoring neural nets, including large language models. This includes both tools for evaluation of LLMs and LLM-based applications with _TruLens-Eval_ and deep learning explainability with _TruLens-Explain_. _TruLens-Eval_ and _TruLens-Explain_ are housed in separate packages and can be used independently.

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.

Docs2KG is a tool designed for constructing a unified knowledge graph from heterogeneous documents. It addresses the challenges of digitizing diverse unstructured documents and constructing a high-quality knowledge graph with less effort. The tool combines bottom-up and top-down approaches, utilizing a human-LLM collaborative interface to enhance the generated knowledge graph. It organizes the knowledge graph into MetaKG, LayoutKG, and SemanticKG, providing a comprehensive view of document content. Docs2KG aims to streamline the process of knowledge graph construction and offers metrics for evaluating the quality of automatic construction.

Nitrain is a framework for medical imaging AI that provides tools for sampling and augmenting medical images, training models on medical imaging datasets, and visualizing model results in a medical imaging context. It supports using pytorch, keras, and tensorflow.

ChemBench is a project aimed at expanding chemistry benchmark tasks in a BIG-bench compatible way, providing a pipeline to benchmark frontier and open models. It enables benchmarking across a wide range of API-based models and employs an LLM-based extractor as a fallback mechanism. Users can evaluate models on specific chemistry topics and run comprehensive evaluations across all topics in the benchmark suite. The tool facilitates seamless benchmarking for any model supported by LiteLLM and allows running non-API hosted models.

The Ollama Conversation integration adds a conversation agent powered by Ollama in Home Assistant. This agent can be used in automations to query information provided by Home Assistant about your house, including areas, devices, and their states. Users can install the integration via HACS and configure settings such as API timeout, model selection, context size, maximum tokens, and other parameters to fine-tune the responses generated by the AI language model. Contributions to the project are welcome, and discussions can be held on the Home Assistant Community platform.

rclip is a command-line photo search tool powered by the OpenAI's CLIP neural network. It allows users to search for images using text queries, similar image search, and combining multiple queries. The tool extracts features from photos to enable searching and indexing, with options for previewing results in supported terminals or custom viewers. Users can install rclip on Linux, macOS, and Windows using different installation methods. The repository follows the Conventional Commits standard and welcomes contributions from the community.

Honcho is a platform for creating personalized AI agents and LLM powered applications for end users. The repository is a monorepo containing the server/API for managing database interactions and storing application state, along with a Python SDK. It utilizes FastAPI for user context management and Poetry for dependency management. The API can be run using Docker or manually by setting environment variables. The client SDK can be installed using pip or Poetry. The project is open source and welcomes contributions, following a fork and PR workflow. Honcho is licensed under the AGPL-3.0 License.

OpenSumi is a framework designed to help users quickly build AI Native IDE products. It provides a set of tools and templates for creating Cloud IDEs, Desktop IDEs based on Electron, CodeBlitz web IDE Framework, Lite Web IDE on the Browser, and Mini-App liked IDE. The framework also offers documentation for users to refer to and a detailed guide on contributing to the project. OpenSumi encourages contributions from the community and provides a platform for users to report bugs, contribute code, or improve documentation. The project is licensed under the MIT license and contains third-party code under other open source licenses.

The Ultralytics YOLO iOS App GitHub repository offers an advanced object detection tool leveraging YOLOv8 models for iOS devices. Users can transform their devices into intelligent detection tools to explore the world in a new and exciting way. The app provides real-time detection capabilities with multiple AI models to choose from, ranging from 'nano' to 'x-large'. Contributors are welcome to participate in this open-source project, and licensing options include AGPL-3.0 for open-source use and an Enterprise License for commercial integration. Users can easily set up the app by following the provided steps, including cloning the repository, adding YOLOv8 models, and running the app on their iOS devices.

PyAirbyte brings the power of Airbyte to every Python developer by providing a set of utilities to use Airbyte connectors in Python. It enables users to easily manage secrets, work with various connectors like GitHub, Shopify, and Postgres, and contribute to the project. PyAirbyte is not a replacement for Airbyte but complements it, supporting data orchestration frameworks like Airflow and Snowpark. Users can develop ETL pipelines and import connectors from local directories. The tool simplifies data integration tasks for Python developers.

MD-Agent is a LLM-agent based toolset for Molecular Dynamics. It uses Langchain and a collection of tools to set up and execute molecular dynamics simulations, particularly in OpenMM. The tool assists in environment setup, installation, and usage by providing detailed steps. It also requires API keys for certain functionalities, such as OpenAI and paper-qa for literature searches. Contributions to the project are welcome, with a detailed Contributor's Guide available for interested individuals.

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.