The Open MatSci ML Toolkit is a flexible framework for machine learning in materials science. It provides a unified interface to a variety of materials science datasets, as well as a set of tools for data preprocessing, model training, and evaluation. The toolkit is designed to be easy to use for both beginners and experienced researchers, and it can be used to train models for a wide range of tasks, including property prediction, materials discovery, and materials design.

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.

KerasHub is a pretrained modeling library that provides Keras 3 implementations of popular model architectures with pretrained checkpoints. It supports text, image, and audio data for generation, classification, and other tasks. Models are compatible with JAX, TensorFlow, and PyTorch, and can be fine-tuned on GPUs and TPUs. KerasHub components are provided as Layer and Model implementations, extending the core Keras API.

LOTUS (LLMs Over Tables of Unstructured and Structured Data) is a query engine that provides a declarative programming model and an optimized query engine for reasoning-based query pipelines over structured and unstructured data. It offers a simple and intuitive Pandas-like API with semantic operators for fast and easy LLM-powered data processing. The tool implements a semantic operator programming model, allowing users to write AI-based pipelines with high-level logic and leaving the rest of the work to the query engine. LOTUS supports various semantic operators like sem_map, sem_filter, sem_extract, sem_agg, sem_topk, sem_join, sem_sim_join, and sem_search, enabling users to perform tasks like mapping records, filtering data, aggregating records, and more. The tool also supports different model classes such as LM, RM, and Reranker for language modeling, retrieval, and reranking tasks respectively.

The AI Fairness 360 toolkit is an open-source library designed to detect and mitigate bias in machine learning models. It provides a comprehensive set of metrics, explanations, and algorithms for bias mitigation in various domains such as finance, healthcare, and education. The toolkit supports multiple bias mitigation algorithms and fairness metrics, and is available in both Python and R. Users can leverage the toolkit to ensure fairness in AI applications and contribute to its development for extensibility.

OntoGPT is a Python package for extracting structured information from text using large language models, instruction prompts, and ontology-based grounding. It provides a command line interface and a minimal web app for easy usage. The tool has been evaluated on test data and is used in related projects like TALISMAN for gene set analysis. OntoGPT enables users to extract information from text by specifying relevant terms and provides the extracted objects as output.

Popper is an agentic framework for automated validation of free-form hypotheses using Large Language Models (LLMs). It follows Karl Popper's principle of falsification and designs falsification experiments to validate hypotheses. Popper ensures strict Type-I error control and actively gathers evidence from diverse observations. It delivers robust error control, high power, and scalability across various domains like biology, economics, and sociology. Compared to human scientists, Popper achieves comparable performance in validating complex biological hypotheses while reducing time by 10 folds, providing a scalable, rigorous solution for hypothesis validation.

RAFT (Reusable Accelerated Functions and Tools) is a C++ header-only template library with an optional shared library that contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

LazyLLM is a low-code development tool for building complex AI applications with multiple agents. It assists developers in building AI applications at a low cost and continuously optimizing their performance. The tool provides a convenient workflow for application development and offers standard processes and tools for various stages of application development. Users can quickly prototype applications with LazyLLM, analyze bad cases with scenario task data, and iteratively optimize key components to enhance the overall application performance. LazyLLM aims to simplify the AI application development process and provide flexibility for both beginners and experts to create high-quality applications.

MIMIR is a Python package designed for measuring memorization in Large Language Models (LLMs). It provides functionalities for conducting experiments related to membership inference attacks on LLMs. The package includes implementations of various attacks such as Likelihood, Reference-based, Zlib Entropy, Neighborhood, Min-K% Prob, Min-K%++, Gradient Norm, and allows users to extend it by adding their own datasets and attacks.

This repository implements multiple key-value cache pruning methods and benchmarks using transformers, aiming to simplify the development of new methods for researchers and developers in the field of long-context language models. It provides a set of 'presses' that compress the cache during the pre-filling phase, with each press having a compression ratio attribute. The repository includes various training-free presses, special presses, and supports KV cache quantization. Users can contribute new presses and evaluate the performance of different presses on long-context datasets.

ProbSem is a repository that provides a framework to leverage large language models (LLMs) for assigning context-conditional probability distributions over queried strings. It supports OpenAI engines and HuggingFace CausalLM models, and is flexible for research applications in linguistics, cognitive science, program synthesis, and NLP. Users can define prompts, contexts, and queries to derive probability distributions over possible completions, enabling tasks like cloze completion, multiple-choice QA, semantic parsing, and code completion. The repository offers CLI and API interfaces for evaluation, with options to customize models, normalize scores, and adjust temperature for probability distributions.

Minions is a communication protocol that enables small on-device models to collaborate with frontier models in the cloud. By only reading long contexts locally, it reduces cloud costs with minimal or no quality degradation. The repository provides a demonstration of the protocol.

LM Studio Client SDK lmstudio-ts is LM Studio's official JavaScript/TypeScript client SDK. It allows you to use LLMs to respond in chats or predict text completions, define functions as tools, and turn LLMs into autonomous agents that run completely locally, load, configure, and unload models from memory, supports both browser and any Node-compatible environments, generate embeddings for text, and more! Why use `lmstudio-js` over `openai` sdk? Open AI's SDK is designed to use with Open AI's proprietary models. As such, it is missing many features that are essential for using LLMs in a local environment, such as managing loading and unloading models from memory, configuring load parameters (context length, gpu offload settings, etc.), speculative decoding, getting information (such as context length, model size, etc.) about a model, and more. In addition, while `openai` sdk is automatically generated, `lmstudio-js` is designed from ground-up to be clean and easy to use for TypeScript/JavaScript developers.

EasyDist is an automated parallelization system and infrastructure designed for multiple ecosystems. It offers usability by making parallelizing training or inference code effortless with just a single line of change. It ensures ecological compatibility by serving as a centralized source of truth for SPMD rules at the operator-level for various machine learning frameworks. EasyDist decouples auto-parallel algorithms from specific frameworks and IRs, allowing for the development and benchmarking of different auto-parallel algorithms in a flexible manner. The architecture includes MetaOp, MetaIR, and the ShardCombine Algorithm for SPMD sharding rules without manual annotations.

AgentDojo is a dynamic environment designed to evaluate prompt injection attacks and defenses for large language models (LLM) agents. It provides a benchmark script to run different suites and tasks with specified LLM models, defenses, and attacks. The tool is under active development, and users can inspect the results through dedicated documentation pages and the Invariant Benchmark Registry.

The Open MatSci ML Toolkit is a flexible framework for machine learning in materials science. It provides a unified interface to a variety of materials science datasets, as well as a set of tools for data preprocessing, model training, and evaluation. The toolkit is designed to be easy to use for both beginners and experienced researchers, and it can be used to train models for a wide range of tasks, including property prediction, materials discovery, and materials design.

AIDE is a machine learning code generation agent that can generate solutions for machine learning tasks from natural language descriptions. It has the following features: 1. **Instruct with Natural Language**: Describe your problem or additional requirements and expert insights, all in natural language. 2. **Deliver Solution in Source Code**: AIDE will generate Python scripts for the **tested** machine learning pipeline. Enjoy full transparency, reproducibility, and the freedom to further improve the source code! 3. **Iterative Optimization**: AIDE iteratively runs, debugs, evaluates, and improves the ML code, all by itself. 4. **Visualization**: We also provide tools to visualize the solution tree produced by AIDE for a better understanding of its experimentation process. This gives you insights not only about what works but also what doesn't. AIDE has been benchmarked on over 60 Kaggle data science competitions and has demonstrated impressive performance, surpassing 50% of Kaggle participants on average. It is particularly well-suited for tasks that require complex data preprocessing, feature engineering, and model selection.

The Open MatSci ML Toolkit is a flexible framework for machine learning in materials science. It provides a unified interface to a variety of materials science datasets, as well as a set of tools for data preprocessing, model training, and evaluation. The toolkit is designed to be easy to use for both beginners and experienced researchers, and it can be used to train models for a wide range of tasks, including property prediction, materials discovery, and materials design.

NoLabs is an open-source biolab that provides easy access to state-of-the-art models for bio research. It supports various tasks, including drug discovery, protein analysis, and small molecule design. NoLabs aims to accelerate bio research by making inference models accessible to everyone.

AlphaFold3 is an implementation of the Alpha Fold 3 model in PyTorch for accurate structure prediction of biomolecular interactions. It includes modules for genetic diffusion and full model examples for forward pass computations. The tool allows users to generate random pair and single representations, operate on atomic coordinates, and perform structure predictions based on input tensors. The implementation also provides functionalities for training and evaluating the model.

This repository contains the code for the paper Fine-Tuned Language Models Generate Stable Inorganic Materials as Text. It demonstrates how finetuned LLMs can be used to generate stable materials, match or exceed the performance of domain specific models, mutate existing materials, and sample crystal structures conditioned on text descriptions. The method is distinct from CrystaLLM, which trains language models from scratch on CIF-formatted crystals.

Scientific Large Language Models (Sci-LLMs) is a repository that collects papers on scientific large language models, focusing on biology and chemistry domains. It includes textual, molecular, protein, and genomic languages, as well as multimodal language. The repository covers various large language models for tasks such as molecule property prediction, interaction prediction, protein sequence representation, protein sequence generation/design, DNA-protein interaction prediction, and RNA prediction. It also provides datasets and benchmarks for evaluating these models. The repository aims to facilitate research and development in the field of scientific language modeling.

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.

AIMNet2 Calculator is a package that integrates the AIMNet2 neural network potential into simulation workflows, providing fast and reliable energy, force, and property calculations for molecules with diverse elements. It excels at modeling various systems, offers flexible interfaces for popular simulation packages, and supports long-range interactions using DSF or Ewald summation Coulomb models. The tool is designed for accurate and versatile molecular simulations, suitable for large molecules and periodic calculations.

ADMET-AI is a platform for ADMET prediction using Chemprop-RDKit models trained on ADMET datasets from the Therapeutics Data Commons. It offers command line, Python API, and web server interfaces for making ADMET predictions on new molecules. The platform can be easily installed using pip and supports GPU acceleration. It also provides options for processing TDC data, plotting results, and hosting a web server. ADMET-AI is a machine learning platform for evaluating large-scale chemical libraries.