AvaTaR is a novel and automatic framework that optimizes an LLM agent to effectively use provided tools and improve performance on a given task/domain. It designs a comparator module to provide insightful prompts to the LLM agent via reasoning between positive and negative examples from training data.

AppWorld is a high-fidelity execution environment of 9 day-to-day apps, operable via 457 APIs, populated with digital activities of ~100 people living in a simulated world. It provides a benchmark of natural, diverse, and challenging autonomous agent tasks requiring rich and interactive coding. The repository includes implementations of AppWorld apps and APIs, along with tests. It also introduces safety features for code execution and provides guides for building agents and extending the benchmark.

A highly robust autonomous agent framework based on the ReAct paradigm, designed for adaptive decision making and multi-agent collaboration. It dynamically generates next actions during agent execution, enhancing robustness. Features a timely abandonment strategy and memory transfer mechanism for multi-agent collaboration. The framework allows developers to balance conservative and exploratory tendencies in agent execution strategies, improving adaptability and task execution efficiency in complex environments. Supports external tool integration, modular design, and MCP protocol compatibility for flexible action space expansion. Multi-agent collaboration mechanism enables agents to focus on specific task components, improving execution efficiency and quality.

Langgraph4j is a Java library for language processing tasks such as text classification, sentiment analysis, and named entity recognition. It provides a set of tools and algorithms for analyzing text data and extracting useful information. The library is designed to be efficient and easy to use, making it suitable for both research and production applications.

The `vectara-agentic` Python library is designed for developing powerful AI assistants using Vectara and Agentic-RAG. It supports various agent types, includes pre-built tools for domains like finance and legal, and enables easy creation of custom AI assistants and agents. The library provides tools for summarizing text, rephrasing text, legal tasks like summarizing legal text and critiquing as a judge, financial tasks like analyzing balance sheets and income statements, and database tools for inspecting and querying databases. It also supports observability via LlamaIndex and Arize Phoenix integration.

DataDreamer is an advanced toolkit designed to facilitate the development of edge AI models by enabling synthetic data generation, knowledge extraction from pre-trained models, and creation of efficient and potent models. It eliminates the need for extensive datasets by generating synthetic datasets, leverages latent knowledge from pre-trained models, and focuses on creating compact models suitable for integration into any device and performance for specialized tasks. The toolkit offers features like prompt generation, image generation, dataset annotation, and tools for training small-scale neural networks for edge deployment. It provides hardware requirements, usage instructions, available models, and limitations to consider while using the library.

Verifiers is a library of modular components for creating RL environments and training LLM agents. It includes an async GRPO implementation built around the `transformers` Trainer, is supported by `prime-rl` for large-scale FSDP training, and can easily be integrated into any RL framework which exposes an OpenAI-compatible inference client. The library provides tools for creating and evaluating RL environments, training LLM agents, and leveraging OpenAI-compatible models for various tasks. Verifiers aims to be a reliable toolkit for building on top of, minimizing fork proliferation in the RL infrastructure ecosystem.

ML-Bench is a tool designed to evaluate large language models and agents for machine learning tasks on repository-level code. It provides functionalities for data preparation, environment setup, usage, API calling, open source model fine-tuning, and inference. Users can clone the repository, load datasets, run ML-LLM-Bench, prepare data, fine-tune models, and perform inference tasks. The tool aims to facilitate the evaluation of language models and agents in the context of machine learning tasks on code repositories.

ChatWeb is a tool that can crawl web pages, extract text from PDF, DOCX, TXT files, and generate an embedded summary. It can answer questions based on text content using chatAPI and embeddingAPI based on GPT3.5. The tool calculates similarity scores between text vectors to generate summaries, performs nearest neighbor searches, and designs prompts to answer user questions. It aims to extract relevant content from text and provide accurate search results based on keywords. ChatWeb supports various modes, languages, and settings, including temperature control and PostgreSQL integration.

STaRK is a large-scale semi-structure retrieval benchmark on Textual and Relational Knowledge Bases. It provides natural-sounding and practical queries crafted to incorporate rich relational information and complex textual properties, closely mirroring real-life scenarios. The benchmark aims to assess how effectively large language models can handle the interplay between textual and relational requirements in queries, using three diverse knowledge bases constructed from public sources.

Mentals AI is a tool designed for creating and operating agents that feature loops, memory, and various tools, all through straightforward markdown syntax. This tool enables you to concentrate solely on the agent’s logic, eliminating the necessity to compose underlying code in Python or any other language. It redefines the foundational frameworks for future AI applications by allowing the creation of agents with recursive decision-making processes, integration of reasoning frameworks, and control flow expressed in natural language. Key concepts include instructions with prompts and references, working memory for context, short-term memory for storing intermediate results, and control flow from strings to algorithms. The tool provides a set of native tools for message output, user input, file handling, Python interpreter, Bash commands, and short-term memory. The roadmap includes features like a web UI, vector database tools, agent's experience, and tools for image generation and browsing. The idea behind Mentals AI originated from studies on psychoanalysis executive functions and aims to integrate 'System 1' (cognitive executor) with 'System 2' (central executive) to create more sophisticated agents.

PDEBench provides a diverse and comprehensive set of benchmarks for scientific machine learning, including challenging and realistic physical problems. The repository consists of code for generating datasets, uploading and downloading datasets, training and evaluating machine learning models as baselines. It features a wide range of PDEs, realistic and difficult problems, ready-to-use datasets with various conditions and parameters. PDEBench aims for extensibility and invites participation from the SciML community to improve and extend the benchmark.

The GFM-RAG is a graph foundation model-powered pipeline that combines graph neural networks to reason over knowledge graphs and retrieve relevant documents for question answering. It features a knowledge graph index, efficiency in multi-hop reasoning, generalizability to unseen datasets, transferability for fine-tuning, compatibility with agent-based frameworks, and interpretability of reasoning paths. The tool can be used for conducting retrieval and question answering tasks using pre-trained models or fine-tuning on custom datasets.

Hurley AI is a next-gen framework for developing intelligent agents through Retrieval-Augmented Generation. It enables easy creation of custom AI assistants and agents, supports various agent types, and includes pre-built tools for domains like finance and legal. Hurley AI integrates with LLM inference services and provides observability with Arize Phoenix. Users can create Hurley RAG tools with a single line of code and customize agents with specific instructions. The tool also offers various helper functions to connect with Hurley RAG and search tools, along with pre-built tools for tasks like summarizing text, rephrasing text, understanding memecoins, and querying databases.

BlinkID iOS is a mobile SDK that enables developers to easily integrate ID scanning and data extraction capabilities into their iOS applications. The SDK supports scanning and processing various types of identity documents, such as passports, driver's licenses, and ID cards. It provides accurate and fast data extraction, including personal information and document details. With BlinkID iOS, developers can enhance their apps with secure and reliable ID verification functionality, improving user experience and streamlining identity verification processes.

Ai2 Scholar QA is a system for answering scientific queries and literature review by gathering evidence from multiple documents across a corpus and synthesizing an organized report with evidence for each claim. It consists of a retrieval component and a three-step generator pipeline. The retrieval component fetches relevant evidence passages using the Semantic Scholar public API and reranks them. The generator pipeline includes quote extraction, planning and clustering, and summary generation. The system is powered by the ScholarQA class, which includes components like PaperFinder and MultiStepQAPipeline. It requires environment variables for Semantic Scholar API and LLMs, and can be run as local docker containers or embedded into another application as a Python package.

AvaTaR is a novel and automatic framework that optimizes an LLM agent to effectively use provided tools and improve performance on a given task/domain. It designs a comparator module to provide insightful prompts to the LLM agent via reasoning between positive and negative examples from training data.

Lhotse is a Python library designed to make speech and audio data preparation flexible and accessible. It aims to attract a wider community to speech processing tasks by providing a Python-centric design and an expressive command-line interface. Lhotse offers standard data preparation recipes, PyTorch Dataset classes for speech tasks, and efficient data preparation for model training with audio cuts. It supports data augmentation, feature extraction, and feature-space cut mixing. The tool extends Kaldi's data preparation recipes with seamless PyTorch integration, human-readable text manifests, and convenient Python classes.

This repository contains the code to prepare the data for the Amber 7B language model. The final training data comes from three sources: RedPajama V1, RefinedWeb, and StarCoderData. The data preparation involves downloading untokenized data, tokenizing the data using the Huggingface tokenizer, concatenating tokens into 2048 token sequences, merging datasets, and splitting the merged dataset into 360 chunks. Each tokenized data chunk is a jsonl file containing samples with 2049 tokens. The repository provides scripts for downloading datasets, tokenizing and concatenating sequences, validating data, and merging subsets into chunks.

This repository contains custom recipes for H2O Driverless AI, which is an Automatic Machine Learning platform for the Enterprise. Custom recipes are Python code snippets that can be uploaded into Driverless AI at runtime to automate feature engineering, model building, visualization, and interpretability. Users can gain control over the optimization choices made by Driverless AI by providing their own custom recipes. The repository includes recipes for various tasks such as data manipulation, data preprocessing, feature selection, data augmentation, model building, scoring, and more. Best practices for creating and using recipes are also provided, including security considerations, performance tips, and safety measures.

This repository contains the code for the paper 'LESS: Selecting Influential Data for Targeted Instruction Tuning'. The work proposes a data selection method to choose influential data for inducing a target capability. It includes steps for warmup training, building the gradient datastore, selecting data for a task, and training with the selected data. The repository provides tools for data preparation, data selection pipeline, and evaluation of the model trained on the selected data.

This repository contains slides, notebooks, and data for the Machine Learning University (MLU) Responsible AI class. The mission is to make Machine Learning accessible to everyone, covering widely used ML techniques and applying them to real-world problems. The class includes lectures, final projects, and interactive visuals to help users learn about Responsible AI and core ML concepts.

TempCompass is a benchmark designed to evaluate the temporal perception ability of Video LLMs. It encompasses a diverse set of temporal aspects and task formats to comprehensively assess the capability of Video LLMs in understanding videos. The benchmark includes conflicting videos to prevent models from relying on single-frame bias and language priors. Users can clone the repository, install required packages, prepare data, run inference using examples like Video-LLaVA and Gemini, and evaluate the performance of their models across different tasks such as Multi-Choice QA, Yes/No QA, Caption Matching, and Caption Generation.

ML-Bench is a tool designed to evaluate large language models and agents for machine learning tasks on repository-level code. It provides functionalities for data preparation, environment setup, usage, API calling, open source model fine-tuning, and inference. Users can clone the repository, load datasets, run ML-LLM-Bench, prepare data, fine-tune models, and perform inference tasks. The tool aims to facilitate the evaluation of language models and agents in the context of machine learning tasks on code repositories.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.